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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 29, 2026, has been entered.
Response to Amendment
The amendment filed on July 29, 2026, cancelled no claims. Claims 1, 7, 10, 16-17, 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 Objections
Claims 1-17, 21 and 24-25 are objected to because of the following informalities: Independent claims 1, 10, and 24 appear to include a typographical error. In the claim set filed on February 27, 2026, the step of generating the second outcome data included the phrase “one or more of the plurality of components included, respectively, in the plurality of content” following the phrase “likely resulted from”. However, in the claims as currently amended, the phrase “likely resulted from” has proper claim markings indicating its removal and added the phrase “of membership in the respective second segments on the user interaction data” with the proper claim markings. However, the phrase “one or more of the plurality of components included, respectively, in the plurality of content” is completely absent in the claims. The examiner believes this to be a typographical error because the amendment requires that the second outcome describe the effect of membership, which is a completely different outcome than the previously claimed second output data describing the effect resulting from one or more of the plurality of components. As such it appears that the applicant intended to remove the phrase “one or more of the plurality of components included, respectively, in the plurality of content” from the instant claim set.
Dependent claims 2-9, 11-17, 21 and 25 inherit the deficiencies of the claims from which they depend and, as such, are objected to by virtue of dependency.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-17, 21 and 24-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 10, and 24 have been amended to recited “generating…first outcome data describing the effect of membership in the respective first segment from a plurality of segments on the user interaction data” and generating…second outcome data describing the effect of membership in the respective second segment from the plurality of segments on the user interaction data”. The examiner has been unable to find support for these limitations in the applicants disclosure. Based on the applicant specification, in at least paragraphs 55-57 and figure 3, an effect is an effect of a user interaction such as a click, a conversion, a page view, a time viewed, “likes” in a social networking application, bounce rate, etc.. None of such effects are “effects of membership in a particular segment”, much less outcome data of an individual member. Instead, the model generates outcome data of a particular segment of the plurality of segments, wherein the outcome data describes the effect (e.g., desired action) that resulted from one or more components being displayed to the particular segment. The examiner has been unable to find support for any type of outcome data being generated for an individual member of a particular segment, the outcome data describing the effect of membership in the particular segment. The closest disclosure is probably paragraphs 105-106 and figure 9, where it discloses that outcome data is generated for individual members of the audience describing whether the effect (e.g., a click, a conversion, a page view, a time viewed, “likes” in a social networking application, bounce rate, etc.) likely resulted from the multiple segments indicated in the segment data, or from the components in the respective items of digital marketing content. Once again, this is outcome data may be for the individual member, but it describes whether the effect likely resulted from the multiple segments indicated in the segment data, or from the components in the respective items of digital marketing content. It does not support outcome data describing an effect of membership in a particular segment. The effect is the result of the interaction (e.g., a click, a conversion, a page view, a time viewed, “likes” in a social networking application, bounce rate, etc.) and the outcome data can attribute this effect as resulting from a segment. However, this is different from being an “effect of membership” as currently claims. As such, it is clear that independent claims 1, 10, and 24, as currently amended, fail to comply with the written description requirement.
Independent claims 1, 10, and 24 have been amended require that the determining of the effect of the one or more of the plurality of components included, respectively, in the plurality of content be performed by “comparing the first outcome data and the second outcome data”. The examiner has been unable to find support for this limitation in the applicant’s disclosure. According to the claim, the first and second outcome data only describe the effect of membership in the respective first and second segments on the user interaction data. It does not include any type of information regarding the components included in any one or more content of the plurality of content the a user interaction resulted from. Therefore, the claim requires that an effect of the one or more of the plurality of components be determined by comparing just these outcomes that describe said effects of said membership. There is no support in the applicant’s disclosure of an invention that is capable of determining the effect of one or more components in this manner. Paragraphs 30 and 38 of the applicant’s specification discloses comparing user interaction data for a component shared between to different pieces of content or similar components in both pieces of content and determining that the component and/or similar component had an effect. Paragraph 99 discloses that effects resulting from interactions, along with information about the users are incorporated into a content graph, wherein the information about the users may be used generate outcome data for metrics regarding types of users that responded positively to a modified digital marketing content item. Data on the users responses to the modified digital marketing content item can then be compared with data on responses or outcomes of other users to the digital marketing content item. The comparison of the two digital marketing content items can be used to determine a likely effect of different components of the two digital marketing content items, such as which audience segments had a more positive or negative experience with the two digital marketing content items, which channels of consumption of the two digital marketing content items had the greatest effect on outcomes of the digital marketing content items across audience segments, which environments of consumption of the two digital marketing content items had the greatest effect on outcomes of the digital marketing content items across audience segments. However, in each of these examples the comparison is not merely a comparison of the effects of membership in a segment. Instead, they each require additional user information, as well as information on the specific components in the different marketing content to determine that the component resulted positively or negatively to the effect. As such, it is clear that independent claims 1, 10, and 24, as currently amended, fail to comply with the written description requirement.
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.
Claims 10-17 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 10, has been amended to require that the “processing system” performs the training of the machine learning model; and the “analytics processing system” performs the generating of the outcomes using the trained machine learning model, as well as, performing the receiving of user interaction data, identifying the components included each of the plurality of content, the determining of the effect, the presenting of the outcome data, and the generating of additional content. The examiner has been unable to find support in the applicant’s disclosure for this specific arrangement of devices performing the claimed steps, much less support for “a processing system” performing the training and “an analytics processing system” that performs the remainder of the steps. According to the applicant’s specification, in at least paragraphs 34 and 93, it is the analytics processing system that performs the training. Furthermore, independent claim 10 requires that the “processing system” comprise two distinct computer readable mediums. The first of which store instructions that when executed by the “processing system” cause the “processing system” to perform the claimed training. The second of which stores instructions which when executed by “the processing system” cause the “analytics processing system” to perform the remainder of the claimed steps. First, the examiner has been unable to find support in the applicant’s disclosure for a “processing system” comprising two distinct computer-readable storage medium, wherein the first medium stores instructions that when executed perform the claimed training, and the second medium stores instructions for performing the remainder of the claimed steps. While the applicant’s disclosure does support a processing system with one or more computer-readable media, there is no disclosure in which the processing system comprises two specific and distinct media, the first of which performs the claimed training and the second of which performs the remainder of the claims. As such, the applicant’s disclosure does not support the claimed subject matter. Second, the claim as currently written requires that the “processing system” executing the instructions stored on the second computer-readable medium cause the “analytics processing system” to perform the claimed steps. The examiner has been unable to find support in the applicant’s disclosure for instructions executing on a first computer causing a second computer to perform any functions, much less the specific functions claimed. As such, the applicant’s disclosure does not support the claimed subject matter. Therefore, it is clear that independent claim 10, as currently amended, fails to comply with the written description requirement.
Dependent claims 11-17, and 21 fail to cure all the deficiencies of the claims from which they depend and, as such, are rejected by virtue of dependency.
Claims 24-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 24, as currently amended, is broad enough to encompass a single computer readable storage medium that is executable by both “a processing system” to perform the claimed training, and an “analytics processing system” to perform the remainder of the limitations of the claim. The examiner has been unable to find support in the applicant’s disclosure for a single computer-readable storage media that is executable by two different computers with two different processors, much less one single computer-readable storage media executable by “a processing system” to perform training and executable by an “analytic processing system” to perform the remainder of the claimed steps. As such, the applicant’s disclosure does not support the claimed subject matter. Furthermore, claim 24 has been amended to require that the “processing system” performs the training of the machine learning model; and the “analytics processing system” performs the generating of the outcomes using the trained machine learning model, as well as, performing the receiving of user interaction data, identifying the components included each of the plurality of content, the determining of the effect, the presenting of the outcome data, and the generating of additional content. The examiner has been unable to find support in the applicant’s disclosure for this specific arrangement of devices, much less support for “a processing system” performing the training and “an analytics processing system” that performs the remainder of the claimed steps. According to the applicant’s specification, in at least paragraphs 34 and 93, it is the analytics processing system that performs the training. As such, the applicant’s disclosure does not support the claimed subject matter. Therefore, it is clear that independent claim 24, as currently amended, fails to comply with the written description requirement.
Dependent claim 25 fail to cure all the deficiencies of the claim from which it depends and, as such, is rejected by virtue of dependency.
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 are replete with numerous antecedent basis and continuity issues that it is impossible for one of ordinary skill in the art to determine the intended metes and bounds of the claims. As such, claims 1, 10, and 24 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. Below is a listing of the 112(b) issues found in the claims (Note: The examiner has described the issue using claim 1 as the representative claim. However, claims 10 and 24 are substantially similar and suffer from the same issues as those identified for claim 1):
Claim 1 has two different instances of “an audience” and four different instances of “the audience. It is impossible to determine whether the applicant intends to claim two different audiences or whether the second claimed audience is intended to have antecedent basis to the first claimed “an audience”. If two different audiences are intended, then it is impossible to determine which of the two audiences each of the claimed “the audience” is intended to have antecedent basis.
Claim 1 has an instance of “data describing user interactions”, a first instance if “user interaction data”, a second instance of “user interaction data”, three instances of “the user interaction data”, and one instance of “user interaction”. One of ordinary skill in the art would not be able to determine the intended difference between “data describing user interactions” and “user interaction data”. Likewise, even if one could determine the intended difference between these claimed terms, one of ordinary skill in the art would not be able to determine whether the applicant intends there to be three different types of such data (i.e., data describing user interactions, the first claimed “user interaction data”, and the second claimed “user interaction data”) or whether all of these are intended to describe the same user interaction data. If the applicant intends for there to be more than one “user interaction data”, then it is impossible to tell which of the three different possible user interaction data each of the three “the user interaction data” is intended to have antecedent basis. Finally, the claimed instance of “user interaction” may be intended to be a new user interaction, it may be intended to have antecedent basis to the claimed “data describing user interactions” or even might be expected to have antecedent basis to one of the two instances of “user interaction data”.
Claim 1 recites a single instance of “a plurality of content” and five instances of “the plurality of content”. Thus, each instance of “the plurality of content” is expected to have antecedent basis to the instance of “a plurality of content”. However, the claims requires that the machine learning model be trained, by a processing device of an analytics system, using training data the includes: data describing user interactions with “a plurality of content”, and the one or more indications identifying a plurality of components included in “the plurality of content”. According to the claims this training would occur before the processing device of the analytics system receives “user interaction data of an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action”. As well as before the processing device of the analytics system identifies “which of the plurality of components are included in respective ones of the plurality of content”. One of ordinary skill in the art would not be able to determine how the processing device of the analytics system in applicant invention can be trained using training data that describes user interactions with “a plurality of content”, and the indications identifying a plurality of components in “the plurality of content”, before it even receives user interaction data of an audience, the user interaction data describing an effect of user interaction with “the plurality of content” on achieving an action. Likewise, if the machine learning model is already trained using indication identifying a plurality of components included, respectively in “the plurality of content”, one of ordinary skill in the art would not be able to figure out the intended purpose of later identifying which of the plurality of components are included in respective ones of “the plurality of content”, because the training data already has indications identifying each of these respective components, respectively in “the plurality of content”. The only reason such a second receiving and identifying step would occur after a model is trained is if the model is training on a first set of content with a first plurality of components, and then the invention obtains user interactions with a second set of content with a second plurality of components. Otherwise, the claim is merely receiving for a second time data it already has, and identifying components which it has already such identifications.
Claim 1 indicates the training data includes the data describing user interactions with a plurality of content displayed to “a plurality of segments” of an audience; and one or more indications identifying a plurality of components included, respectively in the plurality of content displayed to “the plurality of segments” of the audience. However, then the limitation indicates the first outcome data of “a first individual member” of “a first segment” of the audience is generated. Thus, the claimed “a first segment” appears to be some type of new segment of the audience because it does not have antecedent basis to the previously claims “a plurality of segments of an audience”. Is the first segment of the audience intended to have antecedent basis to the previously claimed “a plurality of segments of the audience”, or does the applicant’s invention perform some type of re-segmenting step that generates a new segment from the audience? It must be the same audience, so the first individual member must have been a member of the audience, but is this first segment one of the previously claimed a plurality of segments, or is it a new segment of the audience that the applicant’s invention somehow generated? Additionally, later in this same limitation the applicant also claims that the first outcome data describes the effect of membership in “the respective first segment” from “a plurality of segments” on “the user interaction data”. As such, it would appear that the “first segment” is intended to have antecedent based to a new “a plurality of segments” that are somehow generated “the user interaction data”, because the “a plurality of segments” does not have antecedent basis to the data describing user interactions with a plurality of content displayed to “a plurality of segments” of an audience. Thus, it would appear that the applicant intends for some new type of segmenting be performed on the audience. However, one of ordinary skill would not be able to determine the metes and bounds of how such a re-segmenting is intended to be performed or how this segmenting is different from the manner in which the previously claimed a plurality of segments of the audience was segmented.
Claim 1 indicates that the processing device of the analytics system trains the model using data describing user interactions with a plurality of content and receives user interaction data of an audience that describes an effect of user interaction with the plurality of content. It also indicates that a processing device of a service provider obtains user interaction data. However, the user interaction data obtained by the processor of the service provider is not utilized in any fashion in the claims. The service provider never provides this obtained information to the processor of the analytics system and there is no indication that the user interaction data obtained by the processing device of the service provider system is in any way associated with “the plurality of content”. As such, one of ordinary skill in the art would not be able to determine the applicant intended scope of this limitation. Is it intended to be merely a step performed by the service provider system, that has no bearing on the remainder of the steps in the claim which are performed by the analytics system? Is this user interaction data also suppose to be interpreted as “of the audience” and in response each member of “the plurality of segments” interacting with “the plurality of content”? Does the applicant intend that the analytics system receiving the user interaction data is receiving it from the service provider system? As currently written, it would be impossible for one of ordinary skill to determine the metes and bounds intended for the obtaining and receiving steps the intended relationship between the two steps.
Claim 1 trains a single machine learning model using two different types of data: data describing user interactions with a plurality of content and one or more indications identifying a plurality of components, respectively, in the plurality of content. Without providing any further input into the trained machine learning model, said model is apparently able to generate two different types of outcome data: the first outcome data which describes the effect of membership in the respective first segment (based?) on the user interaction data; and second outcome data which describes the effect of membership in a second segment (based?) on the user interactions data, wherein the first outcome data is of a first individual member of the first segment and the second outcome data is of a second individual member of the second segment . One of ordinary skill in the art would not be able to determine how the machine learning model can be implemented such that these two different types of outcome data is generated. There is no indication in the claims that the training model was trained with any type of data the indicates the plurality of segments of the audience. There is no indication that the machine learning model received any data indicating the users that interacted with the plurality of content were members of any specific segment of the plurality of segments. Likewise, once the machine learning model was trained, no additional input is ever provided to the machine learning model. As such, it would not appear to be possible for the machine learning model to generate either the first outcome data or the second outcome data. Since both types of outcome data require the machine learning model describe an effect of membership in a specific segment of the plurality of segments, the machine learning model would need to have been trained, at a minimum, using training data the describe each segment of the plurality of segments, and training data describing the specific segments of the plurality of segments that each user/member of the audience belonged. Without every having been trained on such data, it would not appear to be possible for said machine learning model to generate the claimed outcomes.
Given, the plethora of issues present in the independent claims 1, 10, and 24 it is clear that they 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 1-9 are directed to a method 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, claim 1-9 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 recite(s) the following abstract idea:
training an algorithmic model based on training data, the training data including:
data describing user interactions with a plurality of content displayed to a plurality of segments of an audience; and
one or more indications identifying a plurality of components included, respectively, in the plurality of content displayed to the plurality of segments of the audience, in which at least one component is shared between at least two of the plurality of content displayed to the audience;
obtaining user interaction data;
receiving user interaction data of 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 of a first individual member of a first segment of the audience, the first outcome data describing the effect of membership in the respective first segment from a plurality of segments on the user interaction data;
generating, using the trained algorithmic model, second outcome data of a second individual member of a second segment of the audience, the second outcome data describing the effect of membership in the respective second segment from the plurality of segments on the user interaction data;
determining the effect of the one or more of the plurality of components included, respectively, in the plurality of content by comparing the first outcome data and the second outcome data;
outputting the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface; and
automatically generating a first additional content for display in the user interface based on the first outcome data and a second additional content for display in the user interface 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:
an analytics system comprising a processing device (e.g., a general-purpose computer with a generic computer component) executing software (i.e. generic machine learning model);
a service provider system comprising a processing device (a general-purpose computer with generic computer components);
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)):
obtaining, by a processing device of a service provider system, user interaction data (receiving data);
receiving, by a processing device of an analytics system provider, user interaction data of an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action (receiving data);
outputting, by a processing device of an analytics system provider, the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface (transmitting data).
While the claims do recite an arrangement of devices the processing device of the service provider system is only required to perform the insignificant step of receiving data. Every significant step of the abstract idea is performed by the processing device of the analytics system. As such, the arrangement of devices cannot result in an improvement capable of transforming an abstract idea into a practical application under Step 2a, Prong 2.
Instead, 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 an analytics system comprising a processing device (e.g., a general-purpose computer with a generic computer component) executing software (i.e. generic machine learning model), and a service provider system comprising a processing device performing the insignificant step of receiving data (a general-purpose computer with generic computer 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; 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)):
obtaining, by a processing device of a service provider system, user interaction data (receiving data);
receiving, by a processing device of an analytics system provider, user interaction data of 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 a processing device of an analytics system provider, the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface (transmitting data).
While the claims do recite an arrangement of devices the processing device of the service provider system is only required to perform the insignificant step of receiving data. Every significant step of the abstract idea is performed by the processing device of the analytics system. As such, the arrangement of devices cannot be considered significantly more under Step 2b.
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, 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, and 5-6); further limiting the one or more attributes which is considered part of the abstract idea (Claims 3-4); further limiting the first additional content and the second additional content which are both considered part of the abstract idea (Claim 7); further limiting the second additional content which is considered part of the abstract idea (Claim 8); and adding an additional step of generating a content graph which is considered part of the abstract idea (Claim 9), 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 1-9 are not patent eligible.
Claims 10-17, 21 and 24-25\ are directed to a system and a computer program product 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 10-17, 21, and 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim(s) 10 and 24 recite(s) the following abstract idea:
training an algorithmic model based on training data, the training data including:
data describing user interactions with a plurality of content displayed to a plurality of segments of an audience; and
one or more indications identifying a plurality of components included, respectively, in the plurality of content displayed to the plurality of segments of the audience, in which at least one component is shared between at least two of the plurality of content displayed to the audience;
receiving user interaction data of 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 of a first individual member of a first segment of the audience, the first outcome data describing the effect of membership in the respective first segment from a plurality of segments on the user interaction data;
generating, using the trained algorithmic model, second outcome data of a second individual member of a second segment of the audience, the second outcome data describing the effect of membership in the respective second segment from the plurality of segments on the user interaction data;
determining the effect of the one or more of the plurality of components included, respectively, in the plurality of content by comparing the first outcome data and the second outcome data;
presenting the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface; and
automatically generating a first additional content for display in the user interface based on the first outcome data and a second additional content for display in the user interface 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 processing system comprising a processing device executing software (e.g., the machine learning model) which is merely a general-purpose computer with generic computer components; and
an analytics processing system executing software (e.g., the trained machine learning model) which is merely a general-purpose computer with generic computer components.
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 a processing device of an analytics system provider, user interaction data of an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action (receiving data); and
presenting, by the processing device of an analytics system provider, the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface (transmitting data).
While the claims do recite an arrangement of devices each device performing a significant step of the abstract idea, the claimed arrangement and the specific steps each performs does not appear to result in any type of improvement capable of transforming the abstract idea into a practical application under Step 2a, Prong 2. First, the applicant’s disclosure does not have support for the claimed arrangement of devices and the significant steps that each is required to perform (see 112(a) rejection above). As such, the applicant’s disclosure does not provide any indication of a technological improvement that is obtained by having one general-purpose computer train the machine learning model, and the other general-purpose computer perform the remainder of the steps. Additionally, the examiner can think of no possible technological improvement that might be obtained using such an arrangement of devices. The machine learning model must be trained before it can be used. Whether it is trained by the processing system or the analytics processing system appears to be business decision rather than a technological improvement. As such, the arrangement of devices does not appear to result in an improvement capable of transforming an abstract idea into a practical application under Step 2a, Prong 2
Instead, 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 processing system comprising a processing device executing software (e.g., the machine learning model) which is merely a general-purpose computer with generic computer components; and an analytics processing system executing software (e.g., the trained machine learning model) which is merely a general-purpose computer with generic computer 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; 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 a processing device of an analytics processing system, user interaction data of an audience, the user interaction data describing an effect of user interaction with the plurality of content on achieving an action (receiving data); and
presenting, by the processing device of an analytics processing system, the first outcome data associated with the first individual member and the second outcome data associated with the second individual member for output in a user interface (transmitting data).
While the claims do recite an arrangement of devices each device performing a significant step of the abstract idea, the claimed arrangement and the specific steps each performs does not appear to be ‘significantly more” than the abstract idea under Step 2b. First, the applicant’s disclosure does not have support for the claimed arrangement of devices and the significant steps that each is required to perform (see 112(a) rejection above). As such, the applicant’s disclosure does not provide any indication of such an arrangement of devices performing the claimed steps is significantly more than the abstract idea. Additionally, the examiner can think of no possible technological improvement that might be obtained using such an arrangement of devices. Instead, it would appear to be merely a business decision. As such, the arrangement of devices does not appear to be significantly more than an abstract idea under Step 2b.
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 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 11, 14-15 and 25); further limiting the one or more attributes which is considered part of the abstract idea (Claims 12-13); further limiting the first additional content and the second additional content which are both considered part of the abstract idea (Claim 16); further limiting the second additional content which is considered part of the abstract idea (Claim 17); and adding an additional step of generating a content graph which is considered part of the abstract idea (Claim 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 10-17, 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 Claim Objections, the 35 USC 112(a) rejections, the 35 USC 112(b) rejections, and the 35 USC 101 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:
obtaining, by the at least one computing device, user interaction data;
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, first outcome data of a first individual member of a first segment of the audience, the first outcome data describing the effect resulted from the first segment of the multiple segments indicated in the segment data;
generating, by the at least one computing device, second outcome data of a second individual member of a second segment of the audience, the second outcome data describing the effect resulted from the second segment of the multiple segments indicated in the segment data; and
outputting, by the at least one computing device, a result based on the first or second outcome data for output in a user interface.
The examiner has also found prior art (see Zhang et al. – 2018/0121953) that discloses:
the generating of the first outcome data and the second outcome data may be performed using a model trained using machine learning based on training data, including data describing user interactions with a plurality of content and one or more indications identifying a plurality of components included in the plurality of content, and
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.
However, the claim require the determining of the effect of the one or more of the plurality of components included respectively, in the plurality of content be performed by comparing the first outcome data and the second outcome data which only describe the effect of membership in the respective first segments from a plurality of segments on the user interaction data and second outcome data only describing effect of membership in the respective second segment from the plurality of segments on the user interaction data. The examiner has been unable to find prior art, which would be obvious to combine with Gupta, Datta, and Zhang, that discloses determining the effects of the components of the content using only outcome data describing the effect of membership in the respective first segment and in the respective second segment.
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 Claim Objections, the 35 USC 112(a) rejections, the 35 USC 112(b) rejections, and the 35 USC 101 rejections identified above.
Response to Arguments
Applicant's arguments filed July 29, 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 strongly 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. The applicant appears to assert that under Step 2a, Prong 1, the Desjardins decision indicate that one should focus on the character of the claims as a whole and whether they integrate any alleged abstract idea into a practical application. This assertion is incorrect. MPEP 2106 specifically requires under Step 2a, Prong, that a claim be broken down into its component parts so that the examiner can identify whether any limitations or portion there of recite an abstract idea that fall within one of the enumerated categories of abstract idea. Any limitations that are not part of the abstract idea itself are to be considered “additionial elements” of the claim which are to be analyzed under Step 2a, Prong 2 and Step 2b. Examiner’s may not, based on MPEP 2106, consider the claim as a whole and whether they integrate any alleged abstract idea into a practical application under Step 2a, Prong 1. According to MPEP 2106, such a consideration regarding the claims as a whole integrating the abstract idea into a practical application is the analysis performed under Step 2a, Prong 2. 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 itself, 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 an applicant invented 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 rejections under Step 2a, Prong 2 (not under Step 2a, Prong 1) similar to the way in which the claims in the Enfish decision recited a newly invented self-referential database that operated in a manner different from traditional databases. In contrast, the claims of the instant invention do not recite an applicant invented machine learning model and none of the limitations which the examiner has included as part of the abstract idea are describing the inner workings of such an applicant invented machine learning model such that it performs, internally, in a manner different from traditional machine learning models. Furthermore, the examiner has properly identified the claimed generic machine learning model as an “additional element” of the claims, and analyzed the “additional elements” of the claims both individually and as a whole under Step 2a, Prong 2 and Step 2b. The limitations identified by the examiner as part of the abstract idea itself are all steps performed for the purpose of advertising, marketing or sales activities or behaviors. As such, they have each been properly categorized as part of the abstract idea itself. As such, the applicant’s arguments with regards to Step 2a, Prong 1 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 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. The manner in which conventional analytics are performed is a business process not a technology because it describes the process for performing the analysis and not the underlying technology that makes it possible to perform the process. As such, any improvement obtained by improving such a process is an improvement 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 applicant merely describes the steps of the abstract idea and requires that they be applied using one or more general-purpose computer and a generic machine learning model as tools. The applicant has not invented a new machine learning model that operates, internally in a different manner from traditional machine learning models, nor has the applicant invented a new computer that operates in a manner different from a general-purpose computer. As such, the claims do not include any limitations directed to the underlying technology used to perform the abstract idea which the applicant has invented. The only such “additional elements” in the claims are a general-purpose computer with generic computer components which are merely used as tools to implement the abstract idea. As made clear in the Recentive Analytics decision, neither using a known machine learning model, nor training said model using a specific set of data are capable of transforming an abstract idea into a practical application or capable of being considered significantly more. As such, 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 analytics system may generate content items on the fly in real time by selecting, resizing, rearranging, or omitting components based on the model’s outcome attribution. The examiner disagrees. First, the applicant appears to be reading limitation from the specification into the claims. The claims do not require content items to be resized, rearrange, or omitted, much less doing so based the outcome data, or on the fly in real-time. As such, the applicant’s arguments are not directed to the limitations of the claims and, as such, are not convincing. Second, even if such limitations where present in the claims, these are not improvements in technology. As the applicant asserts, the improvements are obtained because of the manner in which the machine learning model is trained, and there is no indication in the applicant’s disclosure which indicates that said machine learning model is a newly invented machine learning model which operates, internally, in a different manner from traditional machine learning models. As such, the applicant is arguing that the manner in which the machine learning model is trained is providing the improvement. However, as made clear in the Recentive Analytics decision, training a known machine learning model using specific data is not an improvement in technology and is not capable of overcoming a 101 rejection under Step 2a, Prong 2 and/or under Step 2b. Furthermore, the generating of an additional item of content is nothing more than generating tailored content which the Int. Ventures v. Cap One Bank ‘382 decision makes clear is an abstract idea that falls within the Certain Methods of Organizing Human Activity category of abstract idea. Thus, the applicant’s arguments are not convincing, and the rejections have been maintained.
The applicant argues that the claims are analogous to Desjardins through an improvement in computer functionality, even through use of known machine learning techniques. The examiner strongly disagrees. The instant claim are in no way similar to the claims in the Desjardins decision. As the examiner has explained above, the claims in the Desjardins decision recited an applicant invented machine learning model which worked, internally, in a different manner than traditional machine learning models. It was the applicant invented machine learning model that was considered an “additional element” of the claim which overcame the 101 rejections because of the ability of this applicant invented machine learning model to protecting the performance of the machine learning model on the first machine learning task, when it is retrained to perform a second different task, thereby, eliminating the “catastrophic forgetting” that traditional machine learning models suffer from. In contrast, the applicant is merely claiming a known machine learning model trained using specific data, that is merely being used as a tool to apply the abstract idea which is incapable of overcome a 101 rejection under Step 2a, Prong 2 and incapable of being considered significantly more under Step 2b. As such, it is clear that the instant claims bear no similarities to the claims of the Desjardins decision.
The applicant asserts that the examiner “additional element” analysis improperly strips the claims of their integrated structure. The examiner disagrees. The examiner has followed proper procedure, as outlined in MPEP 2106, for identifying the limitations of the claims that recite the abstract idea and identifying the “additional elements” of the claims. As such, the examiner’s “additional elements” analysis is proper and, is in fact the manner in which claims are expected to be analyzed under 35 USC 101. When one considered the claims as a whole under Step 2a, Prong 2 and Step 2b, the claims merely take an abstract idea and apply it using one or more general-purpose computers and a generic machine learning model as a tool. It is possible for an arrangement of devices each performing at least one significant step of the abstract idea to overcome a 101 rejection under Step 2a, Prong 2 and/or Step 2b in the same manner that the arrangement of devices in the Bascom decision overcame the 101 rejections. However, this is not the case for the instant invention. As explained in the 101 rejections above, claiming a second general-purpose computer that performs an insignificant step such a receiving data will not result in an arrangement of devices that results in the improvement. Likewise, having one computer train a machine learning model and another use the machine learning model does not appear to result in an technological improvement over said training being performed on the same computer that uses the machine learning model. It appears to merely be a business decision and the applicant’s disclosure provides no indication otherwise. As such, any purported improvement obtained by practicing the claimed invention appears to be rooted solely in the abstract idea itself which is merely applied using the one or more general-purpose computers and the generic machine learning model as a tool which is incapable of transforming an abstract idea into a practical application under Step 2a, Prong 2 and incapable of being considered significantly more under Step 2b (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”). Thus, the applicant’s arguments are not convincing, and the rejections have been maintained.
The applicant argues that the claims recite significantly more under Step 2b because the ordered combination of features produce new digital content artifacts based on learned attribute relationships. The examiner strongly disagrees. Under Step 2b, the significantly more must be rooted in the additional elements of claim in a manner other than merely applying the abstract idea using the ”additional elements” as a tool. What the applicant considered to be an ordered combination of features is in fact an order combination of steps in an abstract idea. The argued improvements that the claimed automation improves efficiency, reduces guesswork, and enables real-time content optimization are all improvements which are obtained by merely applying the abstract idea using the “additional elements” as a tool. Thus, said improvement rooted in the abstract idea itself which is an improvement in ineligible subject matter. Improvements of this nature are incapable of being considered significantly more under Step 2b (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.). Thus, the arguments are not convincing, and the rejections have been maintained.
The applicant argues under Step 2b, that the claimed invention is not merely a mental process. This argument is moot as the examiner has identified the abstract idea as falling within the “Certain Methods of Organizing Human Activity” category of abstract idea. The examiner has not indicated that the abstract idea falls within the “Mental Process” category of abstract idea. As such, whether the claims recited a mental process is irrelevant to the 35 USC 101 rejection raised by the examiner. As such, 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.
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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/John Van Bramer/Primary Examiner, Art Unit 3622
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