[00:00:01] Speaker 04: We will hear argument next in number 222100, Angel Technologies against MetaPlanet. [00:00:14] Speaker 04: Mr. Campbell. [00:00:23] Speaker 05: Anytime. [00:00:23] Speaker 05: Thank you, Your Honor. [00:00:24] Speaker 05: May it please the court? [00:00:26] Speaker 05: John Campbell for Angel Technologies. [00:00:29] Speaker 05: This court should reverse the district court's holding that Angel Tech's claims are ineligible for patent protection because the claims are directed to an improvement in the function of computer networks. [00:00:39] Speaker 05: Moreover, the claims conclude an inventive concept that was not well understood, routine, or conventional. [00:00:46] Speaker 05: Specifically, the prior art did not contemplate, much less propose a solution for, how to efficiently locate network users in digital images, search for those users in digital images, [00:00:59] Speaker 05: and distribute digital images for those particular users through the network. [00:01:06] Speaker 04: So what do any of the claims say about how to find somebody in a picture? [00:01:12] Speaker 05: Well, the claims set up the structure to change the network so that you can do this, right? [00:01:19] Speaker 05: The claims are set up so that you assign. [00:01:21] Speaker 04: None of the claims say, here's a way of doing facial recognition that nobody had suggested before. [00:01:29] Speaker 04: There's nothing about that really difficult problem. [00:01:35] Speaker 04: It just says, identify somebody. [00:01:38] Speaker 04: And maybe even put a grid over it so it's in the northeast quadrant or something of the picture rather than the southwest. [00:01:49] Speaker 05: It's a little more specific than that, in particular for a 275 claim. [00:01:53] Speaker 05: One does talk about using coordinates. [00:01:55] Speaker 05: And what the specification teaches is a pinpoint and a radius to identify coordinates for some users' face. [00:02:02] Speaker 05: And then it does talk about using those coordinates with artificial intelligence to match the characteristics of the image data bound by those coordinates to then search for other users that without anything [00:02:16] Speaker 04: resembling specificity about what comes under this phrase, artificial intelligence? [00:02:22] Speaker 05: Well, the artificial intelligence is it does take to match the characteristics of a subset of image data bound by the set of coordinates where the set of coordinates correspond to the location of the named user. [00:02:34] Speaker 05: So it does give some specificity that you're going to use the coordinates [00:02:39] Speaker 05: use the image characteristic data bound by those coordinates to then search for other image data. [00:02:45] Speaker 05: So it is setting up a structure to create a system whereby users can be found in other digital images, which was a problem that came about in 2000 from the proliferation of digital images. [00:03:00] Speaker 05: This didn't exist before. [00:03:03] Speaker 05: And so when we think about [00:03:04] Speaker 05: Step one, where the court is looking at the advance over the prior art, or even step two, whether we look at whether it's well understood, routine, and conventional. [00:03:13] Speaker 05: What the specification explains at column one and column two is people had long had photo albums, right? [00:03:20] Speaker 05: Hard copy photo albums, you know, your wedding, your birthdays, whatever. [00:03:25] Speaker 05: And then digital photographs came along, and people created digital photo albums. [00:03:31] Speaker 05: But those digital photo albums left no way to find users in different pictures. [00:03:37] Speaker 05: And certainly in a multi-user network, it's all these claims are directed to to identify the individuals within those photographs, and then find the individuals within those photographs in those pictures and other pictures. [00:03:51] Speaker 05: And so what Mr. Fagone recognized when he created his website, sacco.com, is that there is an opportunity here to create something new, to create something that gives people functionality that they never had before. [00:04:06] Speaker 04: What is in any claim about, just that very first step, how to identify an individual in a picture? [00:04:13] Speaker 04: So what the claims talk about is I thought at least the basic claim [00:04:21] Speaker 04: doesn't require anything more than, oh, I recognize, you know, there's my sister. [00:04:27] Speaker 04: And now, at that point, I do something to note somewhere that's my sister. [00:04:32] Speaker 05: Yes, claim six of the 432 does talk about identifying a user, having a unique user identification stored within a database, then having a unique image identifier stored in a database, and associating those two users so that your sister is identified within that picture. [00:04:51] Speaker 00: Does that all require human intervention, I'm going to say, as opposed to some type of artificial intelligence that's allowing that identification to occur? [00:04:59] Speaker 05: Initially, it does. [00:05:01] Speaker 05: Initially, there's going to be someone that's going to identify that individual and identify that individual in that image. [00:05:10] Speaker 05: And then the 275 pattern talks about using the coordinates of where that user is, where that person is, this person in a multi-user network, to say that's their image bound by this radius. [00:05:24] Speaker 05: And then the artificial intelligence can kick in to find other people in other pictures [00:05:29] Speaker 05: that share the same image characteristics. [00:05:32] Speaker 05: But it's initially, yes, a human that is going to identify the person in the photograph. [00:05:37] Speaker 05: So claim six, your honor, does simply talk about assigning a unique identifier to these people in the network, identifying an image identifier, and associating those two [00:05:50] Speaker 05: identifiers so that there is information about who is in the different digital photographs. [00:05:56] Speaker 03: Isn't that just like creating an index, like at the back of a school yearbook, associating different people with different pictures in a book or in a catalog? [00:06:10] Speaker 05: No, Your Honor, I don't think so, because that doesn't tell you, particularly if you look at 275 Claim 1, that might tell you that that's more akin to a caption, which the patent talks about in the specification. [00:06:22] Speaker 05: And it explains the limitations on captions where that doesn't give you enough information. [00:06:29] Speaker 05: Particularly if you have a picture with multiple people, you may not know [00:06:33] Speaker 05: who is who in the picture, particularly that doesn't give you an identification. [00:06:42] Speaker 05: Yes, Your Honor. [00:06:43] Speaker 05: But again, the specification talks about the issues with those captions. [00:06:48] Speaker 05: There's no system in place with those captions. [00:06:52] Speaker 05: to use those captions to identify users within the network in many photographs, right? [00:06:58] Speaker 05: That just becomes a caption for the photograph that then you need to repeat and retype and re-figure out for the next photograph, the next year's yearbook, the next picture of people where you're going to go left or right. [00:07:11] Speaker 03: So the system automates that process of looking through other pictures to find the same person in any of those other pictures? [00:07:21] Speaker 05: Well, certainly as the 275 points out. [00:07:23] Speaker 03: Using artificial intelligence. [00:07:25] Speaker 05: Well, the 275 talks about using artificial intelligence for image matching so that you can find other users in other photographs. [00:07:32] Speaker 05: But then it also, it's more than just automating, right? [00:07:35] Speaker 05: It's standardizing this. [00:07:37] Speaker 05: It's creating a system so that it's standardized such that I have a contact list, right? [00:07:41] Speaker 05: And I can identify this as this person. [00:07:44] Speaker 05: And then that is standardized throughout these photographs so that [00:07:48] Speaker 05: When you really are searching for a particular user in these photographs, you're finding that user. [00:07:54] Speaker 05: Whereas if you look at captions and somebody has typed in the captions, even assuming you can figure out who is who, [00:08:02] Speaker 05: You're not going to always pick up that person because the caption is going to be different each time because somebody had to retype that in. [00:08:08] Speaker 05: Here, you've got a standardized system that improves a computer network so that you can use a contact list as a 275 claim one talks about and identify users using that contact list. [00:08:20] Speaker 05: And then it's going to be the same every time. [00:08:22] Speaker 05: So when you have this network of users, they can be located in each image. [00:08:28] Speaker 05: And then they can later be found in other images that contain the picture of that person. [00:08:34] Speaker 05: So what this is directed to, again, going to step one in thinking about what digital photos were in 2000, [00:08:44] Speaker 05: Considering what we have in this limited record on 12b6, we have the patent talking about digital online photo albums and captions and how this is different. [00:08:54] Speaker 05: This is something more. [00:08:56] Speaker 05: This is something that gives the users in functionality, computer functionality that they didn't have before. [00:09:03] Speaker 05: And that functionality allows them to [00:09:05] Speaker 05: identify users and images, associate that identification with that images, and then using that system, you can find other users and be consistent across it. [00:09:15] Speaker 05: It gives functionality that didn't exist prior to this time. [00:09:20] Speaker 04: Can you remind me what, if anything, the spec says about this phrase, artificial intelligence? [00:09:28] Speaker 05: Your honor, it is limited. [00:09:31] Speaker 05: I don't have the exact language from it. [00:09:33] Speaker 04: My recollection is it uses that two-word expression, but it doesn't say. [00:09:38] Speaker 04: And what we mean is a particular form of, I don't know, Bayesian inference or something, whatever. [00:09:45] Speaker 04: There's a lot of stuff that goes under the heading of artificial intelligence. [00:09:48] Speaker 05: There's a lot of stuff that goes under the affair. [00:09:50] Speaker 04: So why is that not just the same as saying use a computer to find a picture [00:09:58] Speaker 04: In or to find a person in picture number two if if that person is there that we've already Identified from picture number one right yours is with no detail about how the computer does that right well there again I think it's important not to get too hung up on [00:10:19] Speaker 05: because it's a buzzword of the day, and maybe the word of the year I don't remember for last year, artificial intelligence. [00:10:25] Speaker 05: And what does that mean? [00:10:26] Speaker 05: What the spec talks about, and we're in the 2000 time frame, and it's in the claim, the 275 claim, is that I'm going to use the coordinates. [00:10:36] Speaker 05: I'm going to have a point and a radius. [00:10:38] Speaker 05: And I'm going to take the image characteristics of that person. [00:10:42] Speaker 05: And I'm going to compare those to the image characteristics that show up in other photographs to determine if that person exists in other photographs. [00:10:51] Speaker 05: And so the words artificial. [00:10:53] Speaker 04: I mean, that is something, of course, that humans have been doing forever. [00:10:57] Speaker 04: Not forever, but forever. [00:10:58] Speaker 04: You know, 150 years, 170 years. [00:11:03] Speaker 05: Well, again, we've got a problem that arose in computer networks through the proliferation of digital images that didn't exist before. [00:11:10] Speaker 05: We've got humans that used to put captions, right? [00:11:13] Speaker 04: These claims are not, are they limited to, I'm not sure this would be relevant, but just as a factual matter, are they limited to [00:11:25] Speaker 04: large, large, large databases, I thought, if you had a database with three items, that'll do. [00:11:30] Speaker 05: Well, the database probably, it is a multi-user network, right? [00:11:35] Speaker 05: The claims are limited to a multi-user network. [00:11:37] Speaker 05: So it's not for one person's photos on their computer for them to identify. [00:11:44] Speaker 05: It's a multi-user network, and that's where [00:11:46] Speaker 05: That's where the benefits come in. [00:11:48] Speaker 05: And so this did become, as the complaint discusses, a problem that arose out of the digital photography that started with 2000. [00:11:56] Speaker 05: And we used to be much more limited and much more. [00:11:59] Speaker 05: I still remember the days of being careful with, I only have 20 pictures left on this roll of film. [00:12:04] Speaker 05: In digital photography, you don't have that. [00:12:07] Speaker 05: And so it became, we've got these multi-user networks. [00:12:12] Speaker 05: Who is in this picture, and how do we identify them, and how do we consistently do that? [00:12:16] Speaker 05: And this set up a system that allowed for that to happen. [00:12:20] Speaker 00: Can you address representativeness? [00:12:24] Speaker 00: Are you contending that the district court should have addressed all 76 claims across the four patents, or what is your contention for such claims? [00:12:33] Speaker 05: Yes, Your Honor. [00:12:34] Speaker 05: At the very least, the district court should have looked at the features [00:12:38] Speaker 05: that Angel Tech pointed out in its brief were different in the different claims. [00:12:42] Speaker 05: And Angel Tech did go through in its brief and talk about each patent individually and some of the features that were inventive in those claims of each of those patents. [00:12:53] Speaker 05: And the district court didn't do that. [00:12:54] Speaker 05: The district court just accepted Claim 6 of the 432 as representative. [00:12:58] Speaker 05: Instead, it went through [00:13:00] Speaker 05: all of the claims, but didn't provide any basis for anybody to review what that finding was. [00:13:06] Speaker 05: And so when AngelTech went through, for example, the 275 patent and pointed out the use of coordinates and the use of the contact list and the ability to match other photographs, those are distinctions that were talked about for each of those patents that the district court didn't address. [00:13:21] Speaker 05: And AngelTech did that for the 480 patent and the 291 patent. [00:13:25] Speaker 05: And so at the very least, [00:13:26] Speaker 05: the district court should have looked at those limitations and said, why or why not, those had an impact on the 101 policies. [00:13:37] Speaker 04: Why don't you save your remaining time for rebuttal? [00:13:41] Speaker 05: Yes, Your Honor. [00:13:41] Speaker 04: Thank you. [00:13:51] Speaker 04: Good to see you again. [00:13:52] Speaker 01: Good to see you, Your Honor. [00:13:54] Speaker 01: Good morning, and may it please the court. [00:13:56] Speaker 01: Gabe Bell for the appellees. [00:13:58] Speaker 01: So I think what we have here, based on my friend's comments, is really a human-based problem and a human-based solution, ultimately. [00:14:09] Speaker 01: On the solution side, there's no dispute that it's the human that does the work of identifying. [00:14:15] Speaker 01: And on the problem side, when you look at the specification in columns one and two, and I think if you look at the complaint as well, [00:14:22] Speaker 01: You get the sense that this is about resolving tedious manual labor that you would otherwise have to do of identifying people in a bunch of photos. [00:14:31] Speaker 01: The inventor talked about this crowdsourcing epiphany that he had, where he had gone through and tagged all his digital photos to make it easier to find and search among. [00:14:39] Speaker 01: And he realized, aha, if I put it out on a network, [00:14:42] Speaker 01: I can leverage the labor of everyone can weigh in and tag people with the same ultimate end result. [00:14:49] Speaker 01: Once you collect that data about who's in what photo, that's what the specification says makes the searching easier. [00:14:56] Speaker 01: Not any improvement in a searching algorithm. [00:14:58] Speaker 01: In fact, it admits that searching in the internet context, even involving photos, was well understood. [00:15:05] Speaker 01: It was just applying it in a particular context to his photo albums. [00:15:09] Speaker 01: And that's not the type of thing that would get you over the eligibility. [00:15:14] Speaker 04: Searching using the internet, what was well understood? [00:15:19] Speaker 01: So at the bottom of column two. [00:15:22] Speaker 04: Which patent do you like to look at? [00:15:24] Speaker 01: I'm sorry, the 432 patent. [00:15:25] Speaker 01: This is at appendix page 24. [00:15:28] Speaker 01: And what it says is this. [00:15:34] Speaker 01: While reliable database searchability for digital image [00:15:39] Speaker 01: digital images over the internet is available, it has not been implemented with photo albums. [00:15:45] Speaker 04: So I think it's acknowledging there that the... Oh, for digital images, that's not necessarily searching for my face. [00:15:54] Speaker 01: Exactly. [00:15:55] Speaker 04: It's not facial recognition. [00:15:56] Speaker 01: Exactly. [00:15:56] Speaker 04: So I... Tags on a whole bunch of photographs over... Precisely, Your Honor. [00:16:00] Speaker 04: ...at various locations. [00:16:03] Speaker 01: Right. [00:16:03] Speaker 01: And I think what this underscores is that it's not [00:16:05] Speaker 01: Improvement in the technology computer itself. [00:16:08] Speaker 01: I mean you look elsewhere in the specification. [00:16:10] Speaker 01: It says you can use any network It's not limited to the web the internet HTML or anything of the sort you can use any data structures so although the claims do recite nominally some Particular data structures. [00:16:22] Speaker 01: They're not particular in the way that matters because at column 12 the patent is unusually clear It says you can do anything that stores data [00:16:30] Speaker 01: The same is true as to computational devices, any of those, et cetera, et cetera. [00:16:35] Speaker 01: And returning then to the court's point about artificial intelligence, there are two places that the specification mentions it. [00:16:41] Speaker 01: And I'd be happy to point the court to those. [00:16:43] Speaker 01: It's in column three at page appendix 25. [00:16:47] Speaker 01: And it's near the bottom of that column, lines 59 to 63. [00:16:55] Speaker 01: There's a single sentence there. [00:16:58] Speaker 01: about artificial intelligence. [00:17:00] Speaker 01: It simply says, use artificial intelligence to find other people who are similar, precisely what a human does. [00:17:06] Speaker 01: The other location is at column 13. [00:17:09] Speaker 01: And it is similar. [00:17:11] Speaker 01: It's at the top of column 13. [00:17:14] Speaker 01: After talking about how you might go out and find other people, it says, well, and for instance, optionally, artificial intelligence algorithms, e.g., image recognition systems, this is at lines 5 to 10, [00:17:26] Speaker 01: may be applied against images to further find certain characteristics and so on and so forth. [00:17:32] Speaker 01: And that's reflected in the claims, too. [00:17:33] Speaker 01: There's only two claims at issue. [00:17:35] Speaker 01: As the court, I'm sure, is aware, claims one and two of the 275 patent. [00:17:38] Speaker 01: They didn't even mention it. [00:17:40] Speaker 01: And they do so without any sort of detail. [00:17:41] Speaker 01: And I think it kind of echoes in my mind this court's decision in the Capital One case early on after Alice, where it said, simply having a software brain do some sort of user-tailorization of information [00:17:55] Speaker 01: is not the kind of thing that gets you over 101. [00:17:58] Speaker 01: You have to tell us how to do it, as the court noted. [00:18:01] Speaker 04: Right. [00:18:01] Speaker 04: Simply saying that you should use a software brain isn't enough. [00:18:06] Speaker 04: But using a software brain can be unbelievably inventive if you say how. [00:18:12] Speaker 01: Exactly. [00:18:13] Speaker 01: Absolutely. [00:18:14] Speaker 01: It absolutely could be. [00:18:16] Speaker 01: It's not here. [00:18:17] Speaker 01: And I don't think my friends on the other side are really relying on that too much because there is so little detail on that. [00:18:25] Speaker 01: It's artificial intelligence of the sort that the court has found in other cases is not the type of thing that makes it eligible. [00:18:31] Speaker 01: Another case that kind of resonates with me is the court's recent Trinity case. [00:18:35] Speaker 01: where it's about creating social networks. [00:18:37] Speaker 01: You even have user profiles there, my friend referred to, kind of this notion that what's inventive is having all the information stored about a user so you don't have to reenter it. [00:18:46] Speaker 01: Well, that's of course true in social networking, like in Trinity. [00:18:49] Speaker 01: In NetSocial is another one. [00:18:51] Speaker 01: There's a legion of cases where you have a user profile that stores unique information about a person. [00:18:58] Speaker 01: But ultimately, that's just data storage and matching it to other data that may be relevant, which is true in Trinity. [00:19:03] Speaker 01: It's true here as well. [00:19:05] Speaker 01: TLI is another case that I think is instructive there. [00:19:08] Speaker 01: We were talking about categorizing and storing photos, digital photos. [00:19:13] Speaker 01: The problem there was you had a lot of photos. [00:19:15] Speaker 01: And it makes it easier to search them if you can categorize them and store them in a certain way. [00:19:20] Speaker 01: And the court recognized that that's ultimately just an abstraction. [00:19:23] Speaker 01: You need more than that. [00:19:25] Speaker 01: One other one I would just point the court to is the BSG case. [00:19:28] Speaker 01: The BSG case was about a self-evolving data index for a database. [00:19:34] Speaker 01: And it guided the user to input certain characteristics, parameters, and types [00:19:40] Speaker 01: of the data precisely so that it could be more easily searched and found later. [00:19:44] Speaker 01: It even said this might allow you to search millions of records more quickly. [00:19:50] Speaker 01: But ultimately, you're using just conventional database technology for an abstract idea. [00:19:55] Speaker 01: And I think the parallel here is pretty clear when, as the court noted, it's not adding anything to that, whether in terms of artificial intelligence or other features that would take it [00:20:08] Speaker 01: out of the ineligibility realm. [00:20:12] Speaker 00: Sure. [00:20:16] Speaker 01: Thank you, Your Honor. [00:20:18] Speaker 01: So I would say, first of all, the district court did look beyond claim six. [00:20:23] Speaker 01: We know that for two reasons. [00:20:24] Speaker 01: One, it said it. [00:20:26] Speaker 01: It said, I'm going to use claim six as representative, but I've also done my own independent review, which I think was reasonable because we presented arguments on all the claims. [00:20:37] Speaker 01: So we said, for the convenience of the court, you can do a representative claim consistent with this court's directions in many cases, or we're happy to go through them claim by claim, which we did. [00:20:46] Speaker 01: So the district court said that. [00:20:48] Speaker 01: And then I respectfully disagree with my friend who said the district court didn't even look at other features. [00:20:53] Speaker 01: I think if you look at the court's decision at page A8, for example, and the court maybe could have been a little more clear on this, but it said, I'm going to look [00:21:04] Speaker 01: Beyond claim six, this is step one in the representative analysis. [00:21:08] Speaker 01: I'm going to look at the other three claims that Angel put forth as somehow showing some difference from claim six. [00:21:14] Speaker 01: And it referred to their opposition. [00:21:17] Speaker 01: And those three claims were related to AI, number one. [00:21:23] Speaker 01: Number two, user interface to tag users. [00:21:27] Speaker 01: So that's that conventional figure four. [00:21:29] Speaker 01: You can see a person say, that's Aunt Jane. [00:21:31] Speaker 01: And then three, add a contact. [00:21:34] Speaker 01: So make friend. [00:21:35] Speaker 01: So you've tagged somebody, and now you want to add them to your contact list. [00:21:38] Speaker 01: No different than in Trinity. [00:21:40] Speaker 01: And that's reflected in the district court's decision when it goes on to analyze step one. [00:21:44] Speaker 01: It says, I'm going to address those in step one. [00:21:46] Speaker 01: And it does. [00:21:47] Speaker 01: It really takes on, again, could have used some more words, but we think in substance, it's all there. [00:21:53] Speaker 01: It says that the AI doesn't help you. [00:21:56] Speaker 01: It talks about the 275 patent. [00:21:58] Speaker 01: And it also talks about the user interface being just simply clicking on somebody's face, which the patent says that's all that's required. [00:22:06] Speaker 01: And so that really undermines any notion that there is an improved interface here, like the ones that the court has found eligible. [00:22:14] Speaker 01: In other cases, that's underscored by the specification, which says any interface or any display can be used [00:22:22] Speaker 01: And so I think the court properly, whether you look at it as a representative claim analysis or as sufficiently considering other things, I think the court was on solid ground. [00:22:32] Speaker 01: And I haven't heard anything here today, and we didn't hear anything at the hearing below, although the district court gave fulsome opportunity at the hearing below, said, [00:22:41] Speaker 01: pose this very question, representative claim, why should I not consider it? [00:22:46] Speaker 01: And there's been nothing different there than we heard here, than we heard in the briefs. [00:22:50] Speaker 01: And I think each of those just reflects, at most, inconsequential additions to it or unexplained elements like the artificial intelligence. [00:23:01] Speaker 02: How would you articulate the abstract idea for claim six of the 432? [00:23:07] Speaker 01: Sure. [00:23:08] Speaker 01: So the abstract idea is identifying people in photos. [00:23:13] Speaker 01: And I'll just give you what the district court said, and I think I agree with it. [00:23:17] Speaker 01: It's reflecting what we put forth below. [00:23:19] Speaker 01: It's identifying people in photos with a unique tag containing some other piece of information, such as a name or unique identifying number, and then storing that information. [00:23:28] Speaker 04: It's kind of a mouthful. [00:23:29] Speaker 01: It is. [00:23:30] Speaker 01: Let me boil it down, and here's how we presented it. [00:23:32] Speaker 01: Identifying people in photos, e.g. [00:23:35] Speaker 01: tagging, and storing that information. [00:23:37] Speaker 01: That's it. [00:23:38] Speaker 01: So identifying people in photos, that's a step in every single claim at issue here. [00:23:44] Speaker 01: It's across all the claims. [00:23:45] Speaker 01: And then store it. [00:23:46] Speaker 01: And by the way, that's something humans do. [00:23:48] Speaker 01: Humans have always done. [00:23:49] Speaker 01: Humans do in these claims. [00:23:52] Speaker 01: And then store that information. [00:23:53] Speaker 01: And I would note, one of the patents doesn't even require storing. [00:23:56] Speaker 01: The 275 doesn't even go that far. [00:23:59] Speaker 01: It's kind of implicit, like why would you do it [00:24:02] Speaker 01: unless you're going to store it. [00:24:03] Speaker 01: But it's even broader, I guess, in that sense. [00:24:05] Speaker 01: But identify and store. [00:24:07] Speaker 01: And what does that do? [00:24:08] Speaker 01: That enables all of the benefits that they identify, all the benefits in searching more easily, in sharing photos with other people. [00:24:17] Speaker 01: It all stems from this notion that have a bunch of humans, identify people in photos, store that information, and then lots of good things happen. [00:24:28] Speaker 01: Unless the court has further questions, we'd respectfully ask to affirm. [00:24:31] Speaker 04: We have your position. [00:24:33] Speaker 01: Thank you. [00:24:33] Speaker 05: Thank you, Your Honor. [00:24:45] Speaker 05: So just to rebut a few points that counsel brought up. [00:24:49] Speaker 05: First of all, if you look at column two, he talked about the searching discussion in the prior art. [00:24:55] Speaker 05: If you look at what is discussed there, it's talking about searching stock photos. [00:25:00] Speaker 05: It explicitly says this doesn't work for online photo albums. [00:25:04] Speaker 05: It doesn't work for an individual's online photo album. [00:25:06] Speaker 05: It works for stock photos. [00:25:07] Speaker 05: If I want to find a picture of a sunset, well, people have had pictures of sunsets, and I can search for a sunset. [00:25:14] Speaker 05: If I want to find a picture of a beach, I can do that. [00:25:16] Speaker 05: But it's not searching the photos. [00:25:19] Speaker 05: Those stock companies, is what it's explaining, is just giving you a group of photographs that [00:25:24] Speaker 05: are that, and then saying, well, here, if you want sunset photos, we've categorized this for you, and we've given this to you. [00:25:30] Speaker 05: But that's different than what this invention is requiring. [00:25:34] Speaker 05: And that's showing the advantage over the prior art of this invention. [00:25:39] Speaker 05: I would similar say that what counsel's description of what the patent is directed to does the same thing, right? [00:25:46] Speaker 05: To just ID a photo and then store the ID, well, that is photo albums that were [00:25:53] Speaker 05: my wedding album and then those became online photo albums of my wedding album and captions that were put to it. [00:26:00] Speaker 05: But the patent explains why captions don't provide the advantages of this system. [00:26:05] Speaker 05: And so when you [00:26:07] Speaker 05: Say what the patent is directed to, and cover what was done before, and omit all of the new things about what is inventive here, about what Mr. Forgone came up with and provided with sacco.com. [00:26:19] Speaker 05: And what was lauded by the Wall Street Journal, Mobile PC Magazine, is unique and wonderful features. [00:26:25] Speaker 05: That can't be what the patent is directed to. [00:26:27] Speaker 05: Or if it is, well, is it well-understood, routine, and conventional? [00:26:31] Speaker 05: And there's an inventive concept here that takes it out of that realm. [00:26:35] Speaker 05: The court has any further questions. [00:26:38] Speaker 05: Thank you. [00:26:38] Speaker 05: Thanks to both counsel. [00:26:40] Speaker 05: The case is submitted.