DETAILED ACTION
This action is responsive to papers filed on 3/4/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, the claim recites, in part, causing a multi-stage model to determine attribution of a content campaign to conversion by: at a first stage of the multi-stage model, causing a media mix modeling model to generate a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable; at a second stage of the multi-stage model, causing a multi-touch attribution model to generate a second attribution of the intermediate events to conversions using the intermediate events as an independent variable; and determining the attribution of the content campaign to the conversions as a function of the first attribution and the second attribution and communicating the attribution of the content campaign to the conversions for display.
Regarding claim 7, the claim recites, in part, causing a multi-stage model to determine attribution of a content campaign to conversion by: causing, at a first stage of the multi-stage model, a first model to generate a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable; causing, at a second stage of the multi-stage model, a second model to generate a second attribution of the intermediate events to conversions using the intermediate events as an independent variable; and determining the attribution of the content campaign to the conversions as a function of the first attribution and the second attribution; and generating a user interface presenting the attribution of the content campaign to the conversions.
Regarding claim 15, the claim recites, in part, causing a multi-stage model to determine attribution of a content campaign to conversion by: generating, at a first stage of the multi-stage model, a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable by: accessing content campaign data for a plurality of content campaigns that includes the content campaign, accessing intermediate event data for the intermediate events, accessing environmental factors, and providing the content campaign data, the intermediate event data, and the environmental factors as input to a first model, causing the first model to generate an output that comprises an attribution of each of the plurality of content campaigns to the intermediate events; generating, at a second stage of the multi-stage model, a second attribution of the intermediate events to conversions using the intermediate events as an independent variable by: accessing individual-level touchpoint data for a plurality of touchpoints for a plurality of individuals, the individual-level touchpoint data comprising individual-level intermediate event data for the intermediate events, accessing individual-level conversion data for the conversions for the plurality of individuals, accessing environmental variables, and providing the individual-level touchpoint data, the individual-level conversion data, and the environmental variables as input to the second model, causing the second model to generate an output that comprises an attribution of each type of touchpoint to the conversions; determining the attribution of the content campaign to the conversions as a function of the first attribution and the second attribution; and communicating the attribution of the content campaign to the conversions.
The limitations, as drafted and detailed above, recites determination of attribution of content campaigns to conversions, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and specifically commercial interactions including advertising, marketing or sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of computer storage media (claims 1, 15), computing devices (claim 1), multi-stage machine learning model (claims 1, 7, 15); first machine learning model (claims 7, 15), second machine learning model (claims 7, 15), computer-implemented (claim 7), intermediate event component (claims 7, 15), conversion component (claims 7, 15), attribution component (claims 7, 15), interface component (claims 7, 15), computer system (claim 15), and one or more processors (claim 15). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of generating, accessing, providing, determining, and communicating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using computer storage media (claims 1, 15), computing devices (claim 1), multi-stage machine learning model (claims 1, 7, 15); first machine learning model (claims 7, 15), second machine learning model (claims 7, 15), computer-implemented (claim 7), intermediate event component (claims 7, 15), conversion component (claims 7, 15), attribution component (claims 7, 15), interface component (claims 7, 15), computer system (claim 15), and one or more processors (claim 15) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraph 0091); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 2-6, 8-14, and 16-20 appear to merely limit elements from the other independent claims as well as providing a time period as input to a model and providing a time lag for a touch point to a model, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The computer storage media (claims 1, 15), computing devices (claim 1), multi-stage machine learning model (claims 1, 7, 15); first machine learning model (claims 7, 15), second machine learning model (claims 7, 15), computer-implemented (claim 7), intermediate event component (claims 7, 15), conversion component (claims 7, 15), attribution component (claims 7, 15), interface component (claims 7, 15), computer system (claim 15), and one or more processors (claim 15) are each functional generic computer components that perform the generic functions of generating, accessing, providing, determining, and communicating, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the computer storage media (claims 1, 15), computing devices (claim 1), multi-stage machine learning model (claims 1, 7, 15); first machine learning model (claims 7, 15), second machine learning model (claims 7, 15), computer-implemented (claim 7), intermediate event component (claims 7, 15), conversion component (claims 7, 15), attribution component (claims 7, 15), interface component (claims 7, 15), computer system (claim 15), and one or more processors (claim 15) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chittilappilly (U.S. Pub No. 2013/0332264) in view of Hassantabar (U.S. Pub No. 2025/0131243), and further in view of Yan (U.S. Pub No. 2018/0260715).
Regarding claim 1, Chittilappilly teaches causing a multi-touch attribution model to generate a second attribution of the intermediate events to conversions (Paragraphs 0029, 0041, user’s encounters with a touchpoint represents an intermediate event, 0034, 0039-0040, machine learning model used to assign attribution between touchpoints and conversions, representative of a multi-touch attribution model) using the intermediate events as an independent variable (an independent variable is what is manipulated in an experiment to effect a change, since the user’s encounters with a touchpoint, the intermediate event, is what is manipulated by users, the intermediate event is taken to act as an independent variable); and determining an attribution of the content campaign to the conversions as a function of an attribution of a campaign to an intermediate event and the second attribution (Paragraphs 0026, advertising campaign comprises multiple touchpoints thus showing attribution between a campaign and intermediate events, 0027, 0029, user data is gathered for each touchpoint of a campaign thus showing attribution between a campaign and intermediate events, 0030, entries may be grouped according to campaign thus showing attribution between a campaign and intermediate events, 0034, attribution as a percentage of an overall campaign represents an attribution of content campaign to conversions) and communicating, by a user interface component, the attribution of the content campaign to the conversions over a network to a client computing device (Paragraphs 0053-0054, all processed data may be displayed as a visual interface which would need to be generated and communicated to the client device).
Chittilappilly does not appear to specify causing a media mix modeling model to generate a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable. However, Hassantabar teaches causing a media mix modeling model to generate a first attribution of a content campaign to intermediate events (Paragraphs 0022, media mix modeling, 0052-0055, performance is indicative of attribution) using the intermediate events as a dependent variable (a dependent variable represents a measured outcome in an experiment once an independent variable is changed, since the interaction data, the intermediate event, is what changes based on simulation data that is manipulated, the intermediate event is taken to act as a dependent variable). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use a media mix modeling model to determine an attribution of a campaign to an intermediate event since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Neither Chittilappilly nor Hassantabar appear to specify causing a multi-stage model to determine attribution of a content campaign to conversion. However, Yan teaches causing a multi-stage model to determine attribution of a content campaign to conversion (Paragraph 0033). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the models of Chittilappilly and Hassantabar as different stages of a multi-stage model since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 10, Chittilappilly does not appear to specify generating the first attribution comprises: accessing content campaign data for a plurality of content campaigns that includes the content campaign, accessing intermediate event data for the intermediate events, accessing environmental factors, and providing the content campaign data, the intermediate event data, and the environmental factors as input to a first machine learning model, causing the first machine learning model to generate an output that comprises an attribution of each of the plurality of content campaigns to the intermediate events. However, Hassantabar teaches generating, by an intermediate event component, a first attribution of a content campaign to intermediate events by: accessing content campaign data for a plurality of content campaigns that includes the content campaign (Paragraphs 0022, 0029, content saturation is one example that represents content campaign data, invention is not limited to one content campaign), accessing intermediate event data for the intermediate events (Paragraph 0032, interaction data represents intermediate event data), accessing environmental factors (Paragraph 0043, weather and season represent environmental factors), and providing the content campaign data, the intermediate event data, and the environmental factors as input to a first machine learning model, causing the first machine learning model to generate an output that comprises an attribution of each of the plurality of content campaigns to the intermediate events (Paragraphs 0052-0055, machine learning, performance is indicative of attribution). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use a media mix modeling model to determine an attribution of a campaign to an intermediate event since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 3, 11, 18, Chittilappilly does not appear to specify accessing a time period associated with each content campaign from the plurality of content campaigns; and providing the time period for each content campaign as input to the first machine learning model. However, Hassantabar teaches accessing a time period associated with each content campaign from the plurality of content campaigns; and providing the time period for each content campaign as input to the first machine learning mode (Paragraph 0052). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to specify a time period since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 4, 12, Chittilappilly teaches generating the second attribution comprises: accessing individual-level touchpoint data for a plurality of touchpoints for a plurality of individuals, the individual-level touchpoint data comprising individual-level intermediate event data for the intermediate events (Paragraphs 0030, organized by user represents individual-level, 0037, user-level data, 0029, 0041, user’s encounters with a touchpoint represents an intermediate event), accessing individual-level conversion data for the conversions for the plurality of individuals (Paragraphs 0030, organized by user represents individual-level, 0037, user-level data, 0029, 0041, all of the conversions are identified), accessing environmental variables (Paragraph 0033, day of the week and weather are examples of environmental variables), and providing the individual-level touchpoint data, the individual-level conversion data, and the environmental variables as input to the second machine learning model, causing the second machine learning model to generate an output that comprises an attribution of each type of touchpoint to the conversions (Paragraphs 0034, 0039-0040, machine learning model used to assign attribution between touchpoints and conversions).
Regarding claims 5, 13, 19, Chittilappilly teaches accessing a time lag associated with each touchpoint from the plurality of touchpoints; and providing the time lag for each touchpoint as input to the multi-touch attribution model (Paragraph 0045).
Regarding claims 6, 20, Chittilappilly teaches generating a user interface presenting the attribution of the content campaign to the conversions; and communicating the user interface over a network to a client computing device (Paragraphs 0053-0054, all processed data may be displayed as a visual interface which would need to be generated and communicated to the client device).
Regarding claim 7, Chittilappilly teaches causing, by a conversion component, a second machine learning model to generate a second attribution of the intermediate events to conversions (Paragraphs 0029, 0041, user’s encounters with a touchpoint represents an intermediate event, 0034, 0039-0040, machine learning model used to assign attribution between touchpoints and conversions) using the intermediate events as an independent variable (an independent variable is what is manipulated in an experiment to effect a change, since the user’s encounters with a touchpoint, the intermediate event, is what is manipulated by users, the intermediate event is taken to act as an independent variable); determining, by an attribution component, the attribution of the content campaign to the conversions as a function of an attribution of a campaign to an intermediate event and the second attribution (Paragraphs 0026, advertising campaign comprises multiple touchpoints thus showing attribution between a campaign and intermediate events, 0027, 0029, user data is gathered for each touchpoint of a campaign thus showing attribution between a campaign and intermediate events, 0030, entries may be grouped according to campaign thus showing attribution between a campaign and intermediate events, 0034, attribution as a percentage of an overall campaign represents an attribution of content campaign to conversions); and generating, by a user interface component, a user interface presenting the attribution of the content campaign to the conversions (Paragraphs 0053-0054, all processed data may be displayed as a visual interface).
Chittilappilly does not appear to specify causing, by an intermediate event component, a first machine learning model to generate a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable. However, Hassantabar teaches causing, by an intermediate event component, a first machine learning model to generate a first attribution of a content campaign to intermediate events (Paragraphs 0022, media mix modeling, 0052-0055, machine learning, performance is indicative of attribution) using the intermediate events as a dependent variable (a dependent variable represents a measured outcome in an experiment once an independent variable is changed, since the interaction data, the intermediate event, is what changes based on simulation data that is manipulated, the intermediate event is taken to act as a dependent variable). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use a media mix modeling model to determine an attribution of a campaign to an intermediate event since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Neither Chittilappilly nor Hassantabar appear to specify causing a multi-stage model to determine attribution of a content campaign to conversion. However, Yan teaches causing a multi-stage model to determine attribution of a content campaign to conversion (Paragraph 0033). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the models of Chittilappilly and Hassantabar as different stages of a multi-stage model since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 8, 16, Chittilappilly does not appear to specify the first machine learning model is a media mix modeling model. However, Hassantabar teaches specify the first machine learning model is a media mix modeling model (Paragraphs 0022, media mix modeling, 0052-0055, machine learning, performance is indicative of attribution). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use a media mix modeling model to determine an attribution of a campaign to an intermediate event since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 9, 17, Chittilappilly teaches the second machine learning model is a multi-touch attribution model (Paragraphs 0034, 0039-0040, machine learning model used to assign attribution between touchpoints and conversions, representative of a multi-touch attribution model).
Regarding claim 14, Chittilappilly teaches communicating the user interface over a network to a client computing device (Paragraphs 0053-0054, all processed data may be displayed as a visual interface which would need to be generated and communicated to the client device).
Regarding claim 15, Chittilappilly teaches generating, by a conversion component, a second attribution of the intermediate events to conversions using the intermediate events as an independent variable (an independent variable is what is manipulated in an experiment to effect a change, since the user’s encounters with a touchpoint, the intermediate event, is what is manipulated by users, the intermediate event is taken to act as an independent variable) by: accessing individual-level touchpoint data for a plurality of touchpoints for a plurality of individuals, the individual-level touchpoint data comprising individual-level intermediate event data for the intermediate events (Paragraphs 0030, organized by user represents individual-level, 0037, user-level data, 0029, 0041, user’s encounters with a touchpoint represents an intermediate event), accessing individual-level conversion data for the conversions for the plurality of individuals (Paragraphs 0030, organized by user represents individual-level, 0037, user-level data, 0029, 0041, all of the conversions are identified), accessing environmental variables (Paragraph 0033, day of the week and weather are examples of environmental variables), and providing the individual-level touchpoint data, the individual-level conversion data, and the environmental variables as input to the second machine learning model, causing the second machine learning model to generate an output that comprises an attribution of each type of touchpoint to the conversions (Paragraphs 0034, 0039-0040, machine learning model used to assign attribution between touchpoints and conversions); determining, by an attribution component, an attribution of the content campaign to the conversions as a function of an attribution of a campaign to an intermediate event and the second attribution (Paragraphs 0026, advertising campaign comprises multiple touchpoints thus showing attribution between a campaign and intermediate events, 0027, 0029, user data is gathered for each touchpoint of a campaign thus showing attribution between a campaign and intermediate events, 0030, entries may be grouped according to campaign thus showing attribution between a campaign and intermediate events, 0034, attribution as a percentage of an overall campaign represents an attribution of content campaign to conversions); and communicating, by a user interface component, the attribution of the content campaign to the conversions over a network to a client computing device (Paragraphs 0053-0054, all processed data may be displayed as a visual interface which would need to be generated and communicated to the client device).
Chittilappilly does not appear to specify generating, by an intermediate event component, a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable by: accessing content campaign data for a plurality of content campaigns that includes the content campaign, accessing intermediate event data for the intermediate events, accessing environmental factors, and providing the content campaign data, the intermediate event data, and the environmental factors as input to a first machine learning model, causing the first machine learning model to generate an output that comprises an attribution of each of the plurality of content campaigns to the intermediate events. However, Hassantabar teaches generating, by an intermediate event component, a first attribution of a content campaign to intermediate events using the intermediate events as a dependent variable (a dependent variable represents a measured outcome in an experiment once an independent variable is changed, since the interaction data, the intermediate event, is what changes based on simulation data that is manipulated, the intermediate event is taken to act as a dependent variable) by: accessing content campaign data for a plurality of content campaigns that includes the content campaign (Paragraphs 0022, 0029, content saturation is one example that represents content campaign data, invention is not limited to one content campaign), accessing intermediate event data for the intermediate events (Paragraph 0032, interaction data represents intermediate event data), accessing environmental factors (Paragraph 0043, weather and season represent environmental factors), and providing the content campaign data, the intermediate event data, and the environmental factors as input to a first machine learning model, causing the first machine learning model to generate an output that comprises an attribution of each of the plurality of content campaigns to the intermediate events (Paragraphs 0052-0055, machine learning, performance is indicative of attribution). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use a media mix modeling model to determine an attribution of a campaign to an intermediate event since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Neither Chittilappilly nor Hassantabar appear to specify causing a multi-stage model to determine attribution of a content campaign to conversion. However, Yan teaches causing a multi-stage model to determine attribution of a content campaign to conversion (Paragraph 0033). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the models of Chittilappilly and Hassantabar as different stages of a multi-stage model since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
Applicant argues “Similar to the eligible claims in examples 37 and 39, "the claim does not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people" for at least the reasons above”. However, with regard to claim 2 of example 37, that claim pertained to reorganization of icons in a GUI and was determined to not contain a judicial exception because none of the steps could be practically performed in the human mind required a processor to perform. Likewise, with regard to example 39, that claim had to do with training a neural network for facial detection, and none of the claimed steps recited mathematics, none could be performed in the human mind, and none recited organizing of human activity. The current claims are non-analogous to those, as the claims merely recite generating of attributions related to advertising and communicating those advertising attributions for display. An analogous case would be Electric Power Group, which recited the abstract idea of “collecting data, analyzing it, and displaying certain results of the collection and analysis”. Much like Electric Power Group, the current claims recite a similar abstract idea, albeit related to advertising, and therefore falls beneath the Certain Methods of Organizing Human Activity grouping.
Applicant cites the Desjardins decision and argues “By utilizing intermediate events as a pivotal link between the two machine learning models to provide a multi-stage model, the technology described herein is able to factor in individual-level data into the calculation of the attribution of content campaigns to conversion, which was not previously possible”. However, the Desjardins case was found to overcome 101 due to the specification outlining a specific improvement to the additional element of the machine learning itself. While Paragraph 0035 of the specification states that there are improvements to the functioning of the computer itself, no evidence exists that the computer itself is actually improved. Rather, the multi-stage model is merely being applied to the abstract idea, and the only improvements appear to be to effectively and efficiently implement the determination of attribution of content campaigns to conversions, which is the abstract idea. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Applicant argues “Similar to claims 2 and 3 of example 35 from the USPTO's Subject Matter Eligibility Examples, the combination of elements in claim 1 operate "in a non-conventional and non-generic way" and are therefore eligible”. However, Example 35 related to a non-conventional improvement in fraud prevention, and how the devices were very specific in their functionality that the improvement was driven by each device performing the exact function claimed. Specifically, “like in BASCOM, the claimed combination of additional elements presents a specific, discrete implementation of the abstract idea. Further, the combination of obtaining information from the mobile communication device (instead of the ATM keypad) and using the image (instead of a PIN) to verify the customer’s identity by matching identification information does not merely select information by content or source, in contrast to Electric Power, but instead describes a process that differs from the routine and conventional sequence of events normally conducted by ATM verification, such as entering a PIN, similar to the unconventional sequence of events in DDR” (Subject Matter Eligibility Examples: Business Methods, Example 35). The current claims differ from this as the claims merely recite collecting information, analyzing it, and displaying certain results of the collection and analysis applied using a general purpose computer implementing a multi-stage machine learning model. Further, there is no improvement over a technical art in the claim language, but rather, if an improvement exists, it is merely an improvement to the abstract idea, which is still just an abstract idea. Finally, “mere automation of an economic business practice does not provide significantly more” (Subject Matter Eligibility Examples: Business Methods, Example 35). Therefore, Applicant’s arguments are not persuasive.
Applicant’s arguments regarding the prior art are believed to be rendered moot in view of the new grounds of rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BEKERMAN whose telephone number is (571)272-3256. The examiner can normally be reached 9PM-3PM EST M, T, TH, F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WASEEM ASHRAF can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL BEKERMAN/ Primary Examiner, Art Unit 3621
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