five forty four is what you're doing with yeah but let's say for example so the application math in of itself is not necessarily unpatentable right but in this case we're using something that generates learned functions but i think part of the confusion comes here is in the definition of a learned function and what it means in the context of the specification so if we were to go to for example and this was actually one of the things that our opposition brief that we presented to the district court the definition of a learned function and that would be let me pull that so so learn function and this is  want to give you the right citation i think it's on page seven of the opposition brief but as in so what's the number in the record and that would be joined appendix one fifty seven lines of twelve to fifteen you think that that's a definitional yes so it is in the sense that so learned function it's computer but we have a code and then if you look at the if you look at in the context of the claim the context of claims ok so if you look at figure five of the patent which is on now if you look at each of these it alongside each one will say features a through f or feature and through s ok and then down below you're going to see where it explains that the different functions include  different types of decision trees are things like this but there are configured to receive a subset of data so that the learned functions are identified using meditate as he'll have like this computer readable code that takes the net the meditated dictates which set which type of data it's going to get or maybe a column of data could be some sort of a a group of data different or a class of data that's going to be dictated by nedda data that is directed necessarily to claim one is more representative but i mean the problem with just saying that it's representative is they made arguments this representative and then they started to claim that it missed was missing all the stuff that was actually found in the other claims that were sort of computer related i mean if you look at claim fourteen to me it appears to represent the running your data through an allen rhythm to create a new one well except for that and that's and that's a good point so except for the data is not and you're referencing data using data in order to know which predictive information you want to process ok now the other thing that he did give me one concrete example  data referencing other data the latter to be yeah so just give one concrete example abstractly ok so let's say there's a bunch of information you have information regarding zip codes information regarding marital status and information regarding something like death rates ok and so different  types of of predictive modeling or artificial intelligence is going to be more adept for different types of it's going to be more debt for different types of information for getting you results ok so so in this way that what we're claiming here is you issued a randomly generated these learned functions so the the the the day the data that you're using the generation pseudorandom in that you're really just producing a bunch of these various types and then you're you're running it against test data but in the prior art you had there are these known artificial intelligence models that they use for computer you know with using computers to generate predictive you know outcomes of those carried assumptions there was sort of these kind there's an assumption that this could do this kind of result and so that's where experts will get in and get involved with having prior knowledge and so you want to try some of these when we try some of these by removing that extra or you remove the assumptions and you get a better result from what you're producing if you're producing all these these different generation of them isn't necessarily by filtering data through and you don't have some just says let's get these kind of things let's get these algorithms because they have assumptions are based on because the data but once you generate a bunch of them you can you can analyze it based on the test data and then you you don't necessarily get the fastest result out of these order or the best result by each individual one what happens is you start from bind them you put them into an  and together you may end up getting a bunch of slow one functions but we combine them together you get a better result a better predictor result well how do you deal with the c it it holds that a process that serves with data to the algorithm and ends with a new form of data is directed to an abstract idea well because the claims are getting us to a new form of data what we're doing is we're creating an arrangement of learned functions now that we learn functions will come up with with data but that's something else right the claim is actually towards the arrangement of these and how we  re was a relationship defined by data right within a data table there that made it that knowledgeable in this case there's a relationship where you show them well there i know there's a little bit of difference in a couple of claims in the last element like claim seventeen slightly different but we can go to claim fourteen or or claim one think those words are fairly represent one two three lines in a paragraph actually i probably should open the actual of natural perhaps we can be reading pat winds will make it easier so if a few terms that are particularly important and that were discussed so far and i want to start with a learned function because i think i heard a mr scription of that that is defined in the patent this is that appendix twenty seven it's can be anything it can be for example just rogers you suggested a regression that's clear from figure five it indicates a regression as one of the learned functions but it's well broader than that it is anything that accepts data a input and provides any kind of result in the here economy goes on to explain that the result can be a classification a competence metric and a bird function a regression function an answer a prediction a recognized pattern a rule a recommendation or the like it's extremely broad but i also want to clarify that the the metadata is not part of the learned function if we look at the claims and i think we may now have a five the top of column twenty three talks about the predictive ensemble and the predictive ensemble is made up of two things one is the subset of learned functions that you have selected in some unspecified way based on some  solves so so what you have here is exactly what the district court found and correctly to determine to be the patent ineligible abstract concept of testing and refining mathematical algorithms that's a quote from appendix eleven in the district court's opinion it's nothing more than manipulating and organizing data and math to be clear we are not claiming that these claims are directed to a mathematical formula to any particular mathematical formula or any set there oh the claims are directed to simply this this idea that certain functions will be better at predicting something about certain types of data a certain subsets of data certain features within the data and so you see to randomly generate functions which again can be anything and then you evaluate them in some way to determine something about those functions and particularly which data they will care with and then you tells you how you're going to use them there is no structure this is not and fish in and fish the claims as construed under section one twelve for quired a particular structure to the database a required as opposed to the prior art which which had multiple tables in the database and then definitions of the relationships between them the claims and fish were directed to a self referential database and so here you only used one table ensemble and then the claim goes on the predictive ensemble comprising a subset of multiple learned functions from the plurality of learned functions the multiple learned functions selected and combined based on the evaluation mehta data for the of learned functions and the predictive ensemble comprising a rule set synthesize from the evaluation metadata to direct data through the multiple learned functions so again this is this shows how the predictive ensemble really is the abstract idea itself it is this notion that you will have so many functions they may be combined in different ways or extended in different ways but some set of functions and then information that comes from this metadata which can be anything it just means absolutely and there's certainly claim fourteen but but in our opinion most of the claims can be performed entirely in the human mind or with pen and paper or at the very least the equivalent of human mental activity which this said is not that knowledgeable so yes there's no there's no structure to the predictive ensemble and i want to talk about a term that is used repeatedly in your predictive brief and that is metadata structured environment which does not appear in the patent but is it is there characterization of what the predictive ensemble is and if you unpack that term i think it it helps explain why this sounds like it might be like in fish but really it is it is not at all a meditative structured environment is as you suggest judgment not any specific structure all it's telling you is that you have information that is organized by using met a data it doesn't tell you how it's organized it doesn't tell you what kind of metadata it doesn't tell you how you how you actually make that happen and again metadata is a broad term that simply means information about information i want to also talk about the predictive voting system i think there was also a the prior art richter reference which describe using a predictive voting system i first want to clarify the predictive voting system is not what is described in the four four six pattern as the par if you look at him except believe it's twenty seven appendix twenty seven and this is a column seven and it's lines two through sixteen this is the description of the prior art that used a data scientist and it describes the data scientists the human who would and i met data scientists typically must determine the optimal class of learning machines that would be the most to political for a given data set and rigorously test the selected hypothesis by first fine tuning the learning machine parameters and second by evaluating results fed by trained data that's not describing a predictive voting system that is describing a human implemented version of exactly what these patent claims cover and underscores the fact that these claims are simply automating human activity which again this court has repeatedly held is not does not make something p eligible the predictive voting is it is a different concept it instead of running different subsets of data through different functions you run all the data through all of the functions and then you each may have claimed predictive voting on a little way and we don't have those claims before us but but the mere fact that this might be a novel way of doing predictive analytics which we don't concede but even if it is novelty alone does not get you section one  i also want to just make sure to clarify there is a reference to artificial intelligence this is not a patent about artificial intelligence this is not a patent about machine learning this is a patent that is at most about predictive analytics that term it in fact appears only in the preamble so the claims but we can agree that it's about predictive analytics and the patent here again tells me what predictive analytics is this is at appendix twenty six columns six one twenty five predictive analytics is the study of past performance or patterns found in historical and transactional data to identify behavior and trends in future events it's simply taking past behavior and using that to predict the future the patent goes on to say this may be accomplished using a variety of citrus tickle techniques including modeling machine learning data mining or the like and then the following paragraphs discuss among other things that a category of learned functions may be classifications which are a form of artificial intelligence and so this patent may rely on machine learning or artificial intelligence to perform predictive analytics that is not required by of the claims there's no allegation that it's required by any of the claims but this is not a patent directed to artificial intelligence bunch of divides yeah we'll all start with line sixty five sixty six ok up above sixty six just references a whole bunch of different types of modeling predictive analytics modeling and then right there it says each of these forms of modeling make assumptions about the data set model the given data however some models are more accurate than others and none of the models are ideal and that's going on to call in seven and then coming down and call seven at about a line eight says the diet data scientists typically must determine the optimal class of learning machines that would be most a click well for a given data set so again we're talking about there's these assumptions that come with a data scientist and it kind of gets in the way of you get a more effective predictive on sambal this predictive analytics tool because you're removing the bias you're removing and there's no when you're doing pseudorandom generation there's no sort of random generation and there's no omitting the knowledge of an expert when you're doing it you know so you're not really doing the same thing in the same way that that an expert  presumptions about a group of models because you're you're sort of randomly just generating so it might be removing the data scientists you're removing the data scientist from that removes these biase issue and i think that's what we're getting at is that my question was can a data scientists do that could they well we could well using yes certainly anybody could do it using the invention sure because no no could data scientist certainly randomly generate beat of this is randomly generating word functions but that doesn't make a routine in conventional particularly here where we're doing it in order to create and samples and that is one of the things that that i want to ask you because  that it's about thirty seconds if i mean they pointed out that in fish had a particular structural self referential database but one of the things that were pointed out in and fish is and this is addressed on the joint appendix one sixty one where we cite this proposition brief specifically the in fish court found that the specification teaches the suffer table functions differently than conventional database structures and that while the structural requirements of current database we did it basis require programmer to pre define a structure and subsequent data entry must conform to that structure the database or the president mentioned does not require a programmer to preconfigured a structure which a user must adapt data entry