DETAILED ACTION
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 .
Response to Amendment
The amendment filed on February 27, 2026 cancelled no claims. Claims 1, 5, 7-10, 14-17, 21, and 24 were amended and no new claims were added. Thus, the currently pending claims addressed below are claims 1-17, 21 and 24-25.
Claim Interpretation
The following claim terms have been defined in the applicant’s specification:
Segment of an audience (i.e., audience segment): subsets of an audience having homogeneous characteristics (Definition found in paragraph 26 of the applicant’s specification)
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17, 21 and 24-25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 10, and 24 recite:
“training, by a processing device, a machine-learning model based on training data, the training data: describing interaction of a plurality of segments with a plurality of content; and
identifying a plurality of components included, respectively, in the plurality of content, in which at least one said component is shared between two said content.
Thus, it is clear that the training data comprises data describing interaction of a plurality of segments with a plurality of content. This appears to be the only data that is used as training data when training the machine learning model. The claim indicates that a plurality of components included, respectively, in the plurality of content, in which at least one said component is shared between two said content are identified, but this is an identifying step and not a training step.
Next, the claims require receiving user interaction data for an audience that describes the effect of user interaction with the plurality of content on achieving an action and identifying which of the plurality of components are included in respective ones of the plurality of content. At this point we know that:
the machine learning model was trained using data describing interaction of a plurality of segments with a plurality of content;
the plurality of content, upon which the machine learning model was trained, is provided to an audience;
user interaction data describing the effect of user interaction with the plurality of content on achieving an action is received; and
there appears to be some type of distinction intended between:
identifying a plurality of components included, respectively, in the plurality of content, in which at least one said component is shared between two said content; and
identifying, by the processing device, which of the plurality of components are included in respective ones of the plurality of content.
However, one of ordinary skill in the art would not be able to determine the intended distinction between these two steps. Given that the first identifying step is done respectively, it would appear that the identifying is performed for every one of the plurality of content. Thus, a plurality of components is being identified for respective ones of the plurality of content. This identifying step does require that at least one of said plurality of components identified for a first one of the plurality of content also be in a second one of the plurality of content. The second identifying step identifies which of the plurality of components are included in respective ones of the plurality of content. This identifying step, while worded differently appears to merely identify for the respective ones of the plurality of content said plurality of components. the plurality of components in each of the plurality of content. Given that the plurality of content is the same in the training step and the receiving step, the identified plurality of components would be the same and at least one said component is shared between two said content would also be an inherently required limitation of the second identifying step. Therefore, one of ordinary skill in the art would conclude that the two identifying steps are identical. As such, one of ordinary skill in the art would not be able to determine the intended distinction between the two steps and the claims are indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Next, the claim requires the machine learning model to generate two different outputs: first outcome data for a first individual member of the audience describing the effect likely resulted from membership in a respective said segment from the plurality of segments and second outcome data for a second individual member of the audience describing the effect likely resulted from one or more of the plurality of components included, respectively, in the plurality of content. However, one of ordinary skill in the art would not be able to determine how a machine learning model trained only on data describing interaction of a plurality of segments with a plurality of content could be expected to generate such output. The machine learning model does not have any knowledge of the plurality of components in each of the plurality of content nor any knowledge of the first or the second individual member of the audience which could allow them to be associated with a respective said segment. The only thing that one of ordinary skill in the art could infer from the two generating steps is:
to perform the first generating step, the user interaction data for the first individual member of the audience appears to be input into the machine learning model; and
to perform the second generating step, the user interaction data for the second individual member of the audience appears to be input into the machine learning model.
If the “interaction of the plurality of segments with the plurality of content” was the same as the “user interaction data describing an effect of user interaction with the plurality of content on achieving an action”, were required to contain the same type of information, one of ordinary skill in the art could probably understand how the machine learning model could categorize “user interactions” of the “first individual member of the audience” as being a member of a respective said segment from the plurality of segments based on a similarity between the interactions of said respective said segment and said user interaction data of said first individual member of the audience. However, this does not appear to be the case. The training data describing interaction of the plurality of segments with a plurality of content does not appears to describe an effect of the interaction on achieving an action. Instead, it merely describes the interactions that each respective segments had with the plurality of content. Whereas, the user interaction data describes the effect of the user interaction on achieving an action. As such, given two different types of data describing different types of interactions, one of ordinary skill in the art would not be able to determine how the machine learning model could generate the first outcome data claimed. Likewise, given that the machine learning model is never made aware of, much less trained on, the identified plurality of components included in the plurality of components, it would not appear to be possible for the machine leaning model to generated the claimed second output data. Therefore, the claims are indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Finally, as currently written, the claim requires that the various members of the audience be able to perform a user interaction with the plurality of content. The user interaction data obtained from such user interactions must describe an effect of the user interaction of achieving an action. Thus, one of ordinary skill in the art would expect that each member of the audience is able to individually interact with each of the plurality of content. However, when outputting the first outcome data and the second output data for display and generating a first additional content and second additional content for display, both the outputting and generating are for display on a single user interface. One of ordinary skill in the art would conclude that the applicant’s invention must be construed extremely narrowly. The audience must all be viewing the plurality of content on a single display with a single user interface. They must all be close enough to individually interact with the single display with the single user interface so that the claimed “user interaction data” can be obtained. They must all be viewing the effects of the other audience member user interactions and they must all see the output first outcome data, the output second outcome data, the first additional content and the second first additional content when they are displayed in the single user interface. While such a narrow interpretation of the claim does not raise to the level of an indefinite issue, the implementation of such a narrow interpretation does. If everyone is using the same user interface to perform interactions, how can the invention determine which “user interactions” are from the “first individual member” and/or which “user interactions” are from the “second individual member”. Such a determination would be necessary in order for member specific data be used as input to the machine learning model in order for the machine learning model to generate the two different claimed outputs. Therefore, the claims are indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Dependent claims 2-9, 11-17, 21 and 25 fail to cure all of the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
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 1-17, 21 and 24-25 are directed to a method and a system which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1-17 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim(s) 1, 10 and 24 recite(s) the following abstract idea:
training an algorithmic model based on training data: describing interaction of a plurality of segments with a plurality of content;
identifying a plurality of components included, respectively, in the plurality of content, in which at least one said component is shared between two said content;
receiving user interaction data for an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action;
identifying which of the plurality of components are included in respective ones of the plurality of content;
generating, using the trained algorithmic model, first outcome data for a first individual member of the audience describing the effect likely resulted from membership in a respective said segment from the plurality of segments;
generating, using the trained algorithmic model, second outcome data for a second individual member of the audience describing the effect likely resulted from one or more of the plurality of components included, respectively, in the plurality of content;
outputting the first outcome data as associated with the first individual member and the second outcome data as associated with the second individual member for output to a user; and
automatically generating, a first additional content for display to the user based on the first outcome data and a second additional content for display to the user based on the second outcome data.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely advertising, marketing, or sales related 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 because the claim only recites the additional elements of:
a computer processing device comprising a processing system and a computer-readable storage medium storing instructions (a general-purpose computer with generic computer components);
a user interface (a generic computer element); and
merely applying generic machine learning (a generic computer element).
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving, by the processing device, user interaction data for an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action (receiving data); and
outputting, by the processing device, the first outcome data as associated with the first individual member and the second outcome data as associated with the second individual member for output in a user interface (transmitting and/or displaying data).
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 processing, communicating (e.g., transmitting and receiving), and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and/or one or more generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because 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 a computer processing device with a processing system and a computer-readable storage medium which stores instructions, a user interface, and machine learning (a general-purpose computer with generic computing components) to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and/or one or more generic computer components.
“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 one or more general-purpose computers and/or one or more generic computer components (as evidenced from figures 1 and 11, as well as, paragraphs 38 and 112-114 of the applicant’s specification; the Intellectual Ventures I v. Capital One which discloses that a generic interactive interface that provides information to and accepts user input is a generic computer element; and Abstract Idea Examples 47-49, as well as the Recentive Analytics v. Fox Corp decision which disclose that applying generic machine learning in a particular environment is incapable of being considered significantly more); 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. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving, by the processing device, user interaction data for an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action (receiving data); and
outputting, by the processing device, the first outcome data as associated with the first individual member and the second outcome data as associated with the second individual member for output in a user interface (transmitting and/or displaying data).
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No).
The dependent claims 2-9, 11-17, 21 and 25 appear to merely further limit the abstract idea by further limiting the identifying, the first outcome data, and/or the second outcome data which are all considered part of the abstract idea (Claims 2, 5-6, 11, 14-15 and 25); further limiting the one or more attributes which is considered part of the abstract idea (Claims 3-4 and 12-13); further limiting the first additional content and the second additional content which are both considered part of the abstract idea (Claims 7 and 16); further limiting the second additional content which is considered part of the abstract idea (Claims 8 and 17); and adding an additional step of generating a content graph which is considered part of the abstract idea (Claims 9 and 21), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 1-17, 21 and 24-25 are not patent eligible.
Possible Allowable Subject Matter
Claims 1-17, 21 and 24-25 contain subject matter that would be allowable over the prior art if the applicant were to be able to overcome the 35 USC 101 rejections, and the 35 USC 112 rejections identified above.
The following is a statement of reasons for the indication of allowable subject matter: The examiner has found prior art (see Gupta et al. - 2016/0140623 and Datta et al. 2017/0364948) which discloses a method, a system implemented by at least one computing device, and one or more computer-readable storage media storing instructions executed by a processing system, the comprising: the processing system; and the one or more computer-readable storage medium storing instructions that, responsive to execution by the processing system, causes the processing system to perform operations including:
receiving, by the at least one computing device, user interaction data for multiple segments of an audience, the user interaction data describing an effect of user interaction with a plurality of items of content on achieving an action.
identifying, by the at least one computing device, which of a plurality of components are included in respective ones of the plurality of items of content
generating, by the at least one computing device outcome data for the individual members of the audience describing whether the effect likely resulted from the multiple segments indicated in the segment data; and
outputting, by the at least one computing device, a result based on the outcome data for output in a user interface.
The examiner has also found prior art (see Zhang et al. – 2018/0121953) that discloses:
generating, by the at least one computing device outcome data describing whether the effect likely resulted from one or more of the plurality of components included in the respective ones of the plurality of items of content, the generating performed using a model trained using machine learning based on training data.
However, the claim requires that the model trained using machine learning based on training data, provide a single output for each individual that indicates that the effect resulted from membership in a respective segment or that the effect resulted from one or more of the plurality of components. It would not have been obvious to one of ordinary skill in the art to combine the inventions of Gupta, Datta, and Zhang to arrive at the applicant’s invention without using the applicant’s claims as a roadmap and, thereby, using impermissible hindsight because it would not only require just combining the two machine learning models but also training them with new associations that allow it to distinguish whether the outcome data should indicate that the membership in a respective segment was the factor resulting in the effect or the one or more component was the factor that resulted in the effect.
As such, claims 1-17, 21 and 24-25 would be allowable over the prior art if the applicant were to be able to overcome the 35 USC 101 rejections, and the 35 USC 112 rejections identified above.
Response to Arguments
Applicant's arguments filed February 27, 2026 have been fully considered but they are not persuasive.
The applicant argues, with respect to Step 2a, Prong 1 of the 35 USC 101 rejection, that the examiner’s characterization of the claims as “Certain Methods of Organizing Human Activity” is overly broad and inconsistent with the applications technical focus. The examiner disagrees. The applicant appears to be misconstruing the Appeals Review Panel in In re Desjardins. The Desjardins decision neither changes, nor effects the analysis of claims under Step 2a, Prong 1. Likewise, the Desjardins decision does not caution against characterizing an abstract idea well beyond their actual technical content or evaluating them at a level of generality. Instead, the Desjardins decision cautioned against including “additional elements” of a claim as part of the abstract idea itself. In Desjardins, the original decision included the improved machine learning model which the applicant invented as part of the abstract idea itself. The applicant had invented a new type of machine learning model that operated internally in a manner different from traditional machine learning models. Traditional machine learning models do not maintain the original state of the machine learning model when retrained with additional data. Thus, the original algorithm determined by the machine learning model when first trained and/or the original parameters are lost when traditional machine learning models are retrained and/or trained to perform a second task. This is referred to as “catastrophic forgetting”. The machine learning model invented by Desjardins, maintains the original algorithm and parameters (e.g., original state), when it is retrained with different data to perform a different task and using different parameters. Thus, the inner workings of the machine learning model invented by Desjardins represents a new machine learning model that operates in a manner different from traditional machine learning models in that it maintains this original state information and, thereby, eliminates the catastrophic forgetting problems associated with traditional machine learning models. Therefore, the issue addressed in Desjardin was that the earlier decision included “while protecting performance of the machine learning model on the first machine learning task” as part of the abstract idea, when traditional machine learning models could provide no such protecting. Instead, of merely applying the abstract idea using a traditional machine learning model, the “additional element” should have been a machine learning model capable of protecting performance of the machine learning model on the first machine learning task when the machine learning model is trained on a second machine learning task and using adjusted parameters, because this is how the inner workings of the machine learning model invented by Desjardins worked. As such, the claims in the Desjardins recited a newly invented machine learning model that was an improvement to traditional machine learning models, which is why it overcame the 101 rejection similar to the claims in the Enfish decision which recited a newly invented self-referential database that operated in a manner different from traditional databases. In contrast, the claims of the instant invention merely apply an abstract idea using a traditional machine learning model. As such, the examiner has not included any limitation in the abstract idea which should be considered an “additional element” of the claims. The instant claims do not recite a newly invented machine learning model that operates in a manner different from traditional machine learning models. Therefore, the instant claims are not similar to the claims in the Desjardins decision or the claims in the Enfish decision. Instead, they are much more similar to the claims in the Recentive Analytics decision because they merely apply the identified abstract idea using a generic machine learning model. Thus, the rejections have been maintained
The applicant argues that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 2 because they solve the technical problem of conventional analytics systems treating digital content as indivisible units and rely primarily on static audience segmentation thereby failing to account for the effects of individual content components, particularly components shared across different content items, on user outcomes which means they cannot determine whether an observed effect is attributable to audience characteristics or to particular content components, nor can they leverage that distinction to automatically generate improved content. The examiner disagrees. Each and every limitation of the claim that is involved in treating digital content as indivisible units with individual content components, comparing interactions of a plurality of segments with the digital content with individual content components to user interactions of an audience with the digital content with individual content components, generating outcome determinations based on said comparing, and generating additional content are part of the abstract idea itself which is merely applied using a general-purpose computer with generic computer components executing generic machine learning software. As such, the argued improvement is rooted in the abstract idea itself which is merely applied using a general-purpose computer with generic computer components executing generic machine learning software. Improvements of this nature are improvements to an abstract idea which is an improvement in ineligible subject matter (see MPEP 2106.05(a) - “It is important to note, the judicial exception alone cannot provide the improvement”; and MPEP 2106.05(a)(II) - “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”; and the SAP v Investpic decision - 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 they 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.). The argued improvement is not an improvement rooted in the “additional elements” of the claim because the additional elements of the claim are merely used as a tool to apply the abstract idea. As such, the “additional elements” of the claims are incapable of transforming the abstract idea into a practical application under Step 2a, Prong 2. Thus, the applicant’s arguments are not convincing and the rejections have been maintained.
The applicant argues that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 2 because the claims recite concrete downstream computer functionality, no mere analysis. The examiner disagrees.
First, abstract ideas that fall within the “Certain Methods of Organizing Human Activity” are not limited to merely analysis. The courts have found that gathering data, analyzing data, determining results based on the analysis, generating tailored content, transmitting the tailored content, and displaying the tailored content are all considered parts of such abstract idea (see at least the Electric Power Group decision, Digitech decision, Two-Way Media decisions, and Int. Ventures decisions). As such, the claim need not be directed to merely analysis to recite an abstract idea and the applicant’s arguments is not convincing.
Second, generating first and second additional content is merely generating tailored content. The generating step of the claims does not recite any type of concrete downstream computer functionality. “for display in a user interface based on the respective outcome data” is a recitation of intended use that describes what the generated content is intended to be used for. Such limitations neither limit the scope of the claim, nor require any type of downstream computer functionality to occur. Even if the applicant were to positively recite receiving, by a client device, the generated first and/or second content; and displaying, by the client device and in a user interface, the generated first and/or second content, the claims would not overcome the 35 USC 101 rejection. While the client device and user interface would be “additional elements” of a claim amended in such a manner, it would amount to no more than a second general-purpose computer with generic computer component which merely performs the insignificant extra-solution activities of receiving and displaying data. Thus, whether considered individual or as a whole, a claim amended in this manner would merely be two general-purpose computers with generic computer components which are merely used as a tool to apply the abstract idea. Contrary to the applicant’s argument, generating content items “on-the-fly” by selecting, resizing, rearranging, or omitting components is still just generating tailored content. Other than using a generic machine learning model to generate an outcome that is then used to generate the ”on-the-fly” content items is merely using the generic machine learning model as a tool to merely apply the abstract idea. Perhaps, if the applicant had invented some new type of technology that generated “on-the-fly” content items that was different from current known technology for generating such content, rather than merely claiming the use of known technology in a particular way, a convincing argument for overcoming the 101 rejections could be made. However, this is not what is claimed. In fact, the claims require no specific technology to generate the first and/or second content. Instead, they merely recite generating the first and/or second content based on specific data. As such, it is clear, that the argued limitations are part of the abstract idea itself, and any purported improvement obtained by practicing the claimed invention is rooted in the abstract idea which is merely being applied by the additional elements of the claim. Thus, it is immaterial whether the applicant considers the automated generation of new content to be a practical application because it is part of the abstract idea itself. It is also immaterial whether the applicant considered the automated generation to not be insignificant post-solution activity for the same reason. Under Step 2a, Prong 2, any purported practical application must be rooted in the “additional elements” of the claim in a manner other than merely using the additional elements of the claim as a tool to merely apply the abstract idea. Likewise, “not insignificant post-solution activity” that is part of the abstract idea itself is incapable of transforming an abstract idea into a practical application under Step 2a, Prong 2 and/or incapable of being considered “significantly more” under Step 2b. As such, the applicant’s arguments are not convincing and the rejections have been maintained.
Finally, the applicant’s arguments with regards to the generating of new content artifacts not being a mental or manual step is not convincing. There is no requirement that an abstract idea that falls within the “Certain Method of Organizing Human Activity” category be limited to a mental or manual step. The only abstract idea category which is required to be performed in the human mind or manually using pen and paper is a “Mental Process”. As the instant claims have not been rejected under the “Mental Process” category of abstract idea the argument is moot and the rejections have been maintained.
The applicant argues that the claims are analogous to Desjardins through an improvement in computer functionality, even though use of known ML techniques. The examiner strongly disagrees.
First, the use of known ML techniques in the instant claims means that it is not possible for the instant claims to be analogous to the claims of the Desjardins decision, just like the mere use of a known database cannot be considered analogous to the claims in the Enfish decision. As explained in the previous response to argument above, the claims in the Desjardin decision recited a new type of machine learning model whose internal workings do not work in the same manner as known machine learning models, just like the self-referential database in the claims in the Enfish decision was an applicant invented database that did not operate in the same manner as traditional databases. This is because the use of known ML techniques precludes the claim from reciting a new machine learning model whose internal working operates in a manner different from traditional machine learning models. As such, the applicant’s arguments are not convincing and the rejections have been maintained.
Second, any purported improvement in computer functionality that is obtained by practicing the claimed invention is achieve by merely applying the abstract idea using the general-purpose computer with generic computer components executing the generic machine learning model as a tool. As such, the purported improvement is rooted solely in the abstract idea itself which is merely applied using the additional elements of the claim. Improvements of this nature are improvements to an abstract idea which is an improvement in ineligible subject matter. In the claims of the instant invention, the general-purpose computer still operates in the manner it always has, the generic computer components still operating the manner it always has, and the internal working of the known machine learning model still operate in the same manner it was programmed to operate. Thus, nothing in the claims change the functioning of any of the “additional elements” themselves. As such, the applicant’s arguments are not convincing and the rejections have been maintained.
Finally, it appears that the applicant has misconstrued the Desjardins decision. Keep in mind that there are at least two significant precedential 101 machine learning decisions and both of these decision must hold true when analyzing a machine learning claim for the purpose of making a determination under 35 USC 101. The first decision is the Recentive Analytics decision and the other is the Desjardins decision. In the Recentive Analytics decision we learn:
Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
Page 12, lines 1-4: The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.
Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.
As such, it is abundantly clear that using an already available machine learning model with its already available basis function as a tool to execute an abstract idea does not overcome a 35 USC 101 rejection under Step 2a, Prong 2 and/or Step 2b. Next, we turn to the Desjardins decision, where we find that we must be cautious not to include parts of the inner working of a machine learning model as part of the abstract idea itself and that claim limitations that recite the inner workings of a machine learning model must be evaluated as an “additional element” to determine whether an applicant is claiming the traditional inner workings of a known machine learning model or is claiming a newly invented machine learning model whose inner workings are different from the inner workings of traditional machine learning models. Therefore, when both decisions are properly construed, the decisions do not conflict with one another. If one were to interpret the Desjardins decision in the manner suggested by the applicant, such an interpretation would directly conflict with the Recentive Analytics decision because the applicant asserts that merely structuring the training data in a specific way to train a known ML model, executing the trained model to output outcomes based on the training, and using the outcome to generate tailored content would be sufficient to overcome a 35 USC 101 rejection. Thus, such an interpretation is clearly inconsistent with the current case law. As such, the applicant’s arguments are not convincing and the rejections have been maintained.
The applicant asserts that the examiner’s “additional element” analysis improperly strips the claims of their integrated structure. The examiner strongly disagrees. The analysis of the claims performed by the examiner is consistent will all USPTO procedures for 101 analysis, MPEP 2106, and all existing precedential case law. The claims, as analyzed, still maintain all of their integrated structure. The examiner has included each an every limitation of the claims in the analysis. Identified those limitations that are part of the abstract idea itself and identified the “additional elements” of the claims. The “additional elements” of the claims have been analyzed under Step 2a, Prong 2 and Step 2b in the proper manner. Whether considered individually or as a whole the claims merely require using the additional elements of the claim as a tool to merely apply the abstract idea which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 and insufficient to be considered significantly more under Step 2b. What the applicant considered a technical pipeline is the abstract idea itself which is then merely applied using the additional elements of the claim. As such, any purported improvement obtained by practicing the claimed invention is an improvement to the abstract idea and, as such, cannot be an improvement in technology (see MPEP 2106.05(a)(II) - “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. As such, the applicant’s arguments are not convincing and the rejections have been maintained.
The applicant argues that the way in which the machine learning model is trained, the outputs it generates, and the manner in which the outputs are used results in “significantly more” than the abstract idea under Step 2b. The examiner disagrees. The claims in the Recentive Analytics decision recited specific data with which the machine learning model trained, and using the output of the machine learning model in a specific way and the court found that such limitations are merely describing the abstract idea that is merely applied using a known machine learning model and not an improvement to a machine learning model. As such, the training data and the output are both part of the abstract idea itself, as is the generating of the first and second additional content based on the output. Therefore, the ordered combination of features of the claim amount to no more than applying the claimed abstract idea using a general-purpose computer with generic computer component executing generic machine learning software as a tool, which is insufficient to be considered “significantly more” under Step 2b. In order for a claim to overcome a 35 USC 101 rejection under Step 2b, the “additional elements” of the claim that must be considered “significantly more” than the abstract idea. Thus, the applicant’s arguments are not convincing and the rejections have been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McDevitt et al. (PGPUB: 2018/0101611) which discloses receiving user interaction data, user consumption data, and information about content items; generating outcome data for an audience describing whether the effect likely resulted from the environment of consumption or the channel of consumption indicated in the consumption data.
Gurha (PGPUB: 2016/0007083) which discloses receiving user interaction data and identifying features of a content item including a plurality of components included in said content items.
Ferber (PGPUB: 2018/0225705) which discloses generating outcome data describing which components of a plurality of components caused an action.
Moore (PGPUB: 2014/0130076) which discloses a machine learning model that identifies which aspect of a plurality of aspects contributed toward achieving an action characterized by user interactions; recommending a component of a plurality of components of a content item to include in an additional item of digital content.
Menon (PGPUB: 2008/0097843) which disclose receiving user interaction data with content; analyzing the content to determine a plurality of components of said content; generating a plurality of target specification based on said interaction data and plurality of components and using machine learning to determine the optimal targeting specification for each content items based on the interaction data.
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 JOHN W VAN BRAMER whose telephone number is (571)272-8198. The examiner can normally be reached Monday-Thursday 5:30 am - 4 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Spar Ilana can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/John Van Bramer/Primary Examiner, Art Unit 3622
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