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 .
This office action is in response to communication filed on 5/30/2025.
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1-7 describe a series of steps, thus falling within one of the four statutory classes; i.e., a process. Claims 8-14 tangible system components, thus falling within one of the four statutory classes; i.e., machine. Claims 15-20 describe non-transitory computer readable medium, thus falling within one of four statutory classes; i.e. manufacture.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 1 recites:
retrieving, by an attribution, from a business manager over a data communication, a set of candidate posts;
tracking using the business manager, traffic from the set of candidate posts based on first-party data and social media posts, includes a number of user interactions, the user interactions include clicks, and views on the set of candidate posts, and the first-party data includes user data from user associated with the set of candidate posts;
identifying a one or more candidate posts from the set of candidate posts that have the number of user interactions below a pre-defined threshold value;
applying a boost signal to the one or more candidate posts based on a predetermined number of user interactions to generate a one or more boosted candidate posts;
tracking the traffic from the one or more boosted candidate posts, wherein the l traffic is tracked using an analytics to identify a source of the one or more boosted candidate posts, and the analytics uses tags to identify the source of the one or more boosted candidate posts;
identifying at least one purchase by a set of users initiated using the one or more boosted candidate posts;
identifying, by the attribution owners of the one or more boosted candidate posts associated with at least one purchase based on the source of the one or more boosted candidate posts;
and calculating, by the attribution, an attribution amount for each owner of the one or more boosted candidate posts based on the at least one purchase using the source identified by using the tags.
The claim as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. For example, in the context of this claim encompasses actions that a human could perform; e.g., a human can identify posts with low user interactions and apply a boost to increase user actions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In addition, the limitations mentioned above (i.e., “retrieving”, “tracking”, “identifying”, “applying”, and “calculating”, in the context of this claim) as drafted, are processes that, under their broadest reasonable interpretations, exemplify managing personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, advertising, marketing or sales activities then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Independent claims 8 and 15 recite the same abstract idea as identified above and dependent claims 2-7, 9-14 and 16-20 further narrow it.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Pixel traffic (claims 1, 8 and 15);
Server (claims 1, 8 and 15);
Application Programming Interface (API) (claims 1, 8 and 15);
Network (claims 1, 8 and 15);
Engine (claims 1, 18 and 19);
Processor (claim 15);
A non-transitory computer-readable medium (claim 15);
Machine learning model algorithm (claims 5, 12 and 19);
The pixel traffic, server, Application Programming Interface, network, engine, processor and non-transitory computer readable medium are recited at a high-level of generality (i.e., as generic processors) such that they amount no more than mere instructions to apply the exception using generic computer components. They are no more than a tool to perform the ranking, applying and characterizing steps, and are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations pixel traffic, server, Application Programming Interface, network, engine, processor and non-transitory computer readable medium, learning model algorithm as seen above, these limitations have been interpreted as “apply it”. However, this limitation can be additionally interpreted as insignificant extra-solution activity. As such, this limitation alone and in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of pixel traffic, server, Application Programming Interface, network, engine, processor and non-transitory computer readable medium, machine learning algorithm alone and in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations of pixel traffic, server, Application Programming Interface, network, engine, processor and non-transitory computer readable medium, machine learning algorithm; it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, these additional elements do not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-11, 13-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haaland (2022/0215423).
With respect to claims 1, 8, and 15, Haaland teaches methods, systems and computer readable medium for:
boosting pixel traffic of candidate posts and generating a commission for purchases initiated on boosting the pixel traffic (Abstract).
retrieving, by an attribution server, from a business manager using an Application Programming Interface (API) over a data communication network, a set of candidate posts (see Figure 2);
tracking using the business manager, pixel traffic from the set of candidate posts (figure 1, 116 posts and paragraph 0056 for tracking pixels, cookies, and various tracking mechanisms, the customer's engagement may be measured. When a customer is exposed to an advertisement, various tracking mechanisms may identify the connection, then may monitor when the customer engages with additional content) based on first-party data and social media posts (see figure 1, 126 performance metric for the influencers 112, 114) , wherein the pixel traffic includes a number of user interactions, the user interactions include clicks, and views on the set of candidate posts (see Figure 1, 124 statistics), and the first-party data includes user data from user applications associated with the set of candidate posts ( see figure 1);
identifying one or more candidate posts from the set of candidate posts that have the number of user interactions below a pre-defined threshold value (see figure 1 showing that Sally’s performance metric 126 is underperforming other influencers 114);
applying a boost signal to the one or more candidate posts based on a predetermined number of user interactions to generate one or more boosted candidate posts (see paragraph 0029 for influencer ranking, as well as other components of the dashboard, may help motivate influencers to improve their performance. Not only are the influencers receiving feedback about their performance, the influencers may be incented to perform better by direct comparisons to other influencers);
tracking the pixel traffic from the one or more boosted candidate posts, wherein the pixel traffic is tracked using an analytics engine to identify a source of the one or more boosted candidate posts, and the analytics engine uses tags to identify the source of the one or more boosted candidate posts and identifying at least one purchase by a set of users initiated using the one or more boosted candidate posts (the funnel steps 808 may be listed as “awareness,” “squeeze,” and “sale.” Awareness in this example may be the impressions that an influencer's post may have had in social media or other venue. The squeeze step may be a landing page where a customer may be pointed from an impression, and the conversion may be some sort of offer. Typically, a squeeze page may make a small offer in exchange for a user's email address or some other interaction. The sale may be a purchase that may be the objective of the marketing campaign)(paragraph 0144);
identifying, by the attribution server, owners of the one or more boosted candidate posts associated with at least one purchase based on the source of the one or more boosted candidate posts; and calculating, by the attribution server, an attribution amount for each owner of the one or more boosted candidate posts based on the at least one purchase using the source identified by using the tags ( the influencer statistics 812 may show how well the influencer's posts have been performing in the campaign. In this example, the influencer may have 100,000 impressions, 10,000 conversions on the squeeze page, and 6000 sales. The influencer's performance on the squeeze page may be trailing the desired conversion percentage of the campaign. The campaign conversion goal at the squeeze step may be 15%, but the influencer may be underperforming at 10%. The influencer's post 814 may be shown as icons. In some cases, the influencer may be able to click on the posts and browse the posts as well as statistics for each one. A new posts button 820 may expose an interface by which an influencer may upload new content to be used as posts)(see paragraphs 0147-0148).
With respect to claims 2, 9, 16, Haaland further teaches acquiring user-related data from the pixel traffic using the API based on a user device identifier and user account information associated with users identified from the source of the pixel traffic; identifying at least one purchase by a plurality of users initiated using the set of candidate posts; allocating, by the attribution server, the attribution amount to each owner of the set of candidate posts for the at least one purchase initiated from the set of candidate posts (see paragraph 022 for the influencer to get paid, the customer's engagement with the influencer's advertisement may get tracked from when the customer viewed an advertisement all the way until the customer completed a purchase. The journey from advertisement to the purchase may be a series of interactions, sometimes referred to as a purchasing funnel. The dashboard may show the number of customers who may be at each stage of the funnel);
correlating the at least one purchase by the set of users initiated using the one or more boosted candidate posts with the user interactions on the one or more boosted candidate posts; and allocating, by the attribution server, the attribution amount to each owner of the one or more boosted candidate posts for the at least one purchase initiated from the one or more boosted candidate posts ( see payments earned 418).
With respect to claims 3, 10, 17 Haaland further teaches differentiating, by the attribution server, source links of the at least one purchase by the set of users initiated using the one or more boosted candidate posts based on the user data; calculating, by the attribution server, a first attribution amount for each owner of the one or more boosted candidate posts based on the source links (see paragraph 022 for the influencer to get paid, the customer's engagement with the influencer's advertisement may get tracked from when the customer viewed an advertisement all the way until the customer completed a purchase. The journey from advertisement to the purchase may be a series of interactions, sometimes referred to as a purchasing funnel. The dashboard may show the number of customers who may be at each stage of the funnel);
calculating, by the attribution server, a second attribution amount for each owner of the one or more boosted candidate posts based on the views on the one or more boosted candidate posts; and calculating, by the attribution server, a third attribution amount for each owner of the one or more boosted candidate posts based on the clicks on the one or more boosted candidate posts (see payments earned 418 for each influencer/advertiser owner).
With respect to claims 4, 11 and 18 Haaland further teaches wherein the first attribution amount is a function of a type of source link, and the type of source link is a payment link, a webpage, or a code used for the at least one purchase (see paragraph 0019 by providing tracking codes, specialized Uniform Resource Identifiers (URIs), tracking pixels, cookies, and various tracking mechanisms, the customer's engagement may be measured. When a customer is exposed to an advertisement, various tracking mechanisms may identify the connection, then may monitor when the customer engages with additional content).
With respect to claims 6-7, 13-14 and 20, Haaland further teaches wherein the one or more candidate posts are identified from the set of candidate posts based on Engagement Rate (ER), reach, video views, shares exceeding respective thresholds and having trackable conversions over time (see paragraph 0038 for a conversion may be a sale or other transaction. In still other campaigns, a conversion may be a visit to a webpage or other activity. In some campaigns, a conversion may occur over multiple steps).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Haaland (2022/0215423) in view of Official Notice.
Claims 5, 12 and 19 further recite using machine learning to boost performance. Haaland teaches a small offer in exchange for a user's email address or some other interaction to boost performance. Haaland is silent as to using machine learning. Official Notice is taken that it is old and well known to use machine learning to boost performance such as, automating workflows, reduce human errors and speed up processing time. It would have been obvious to a person of ordinary skill in the at the time of Applicant’s invention to have included in the boost performance of Haaland to have included, machine learning algorithms because such a modification would reduce human errors and speed up processing time.
Other references of record but not applied in the current rejections:
CN 114493674 A teaches solving the problem that the simple non-structured feature combination limits the explicit complex feature interaction capability between different feature domains, causes the problem of low accuracy of the advertisement click rate prediction, effectively enhances the learning and extraction of the complex feature of the marketing advertisement data, and obviously improves the precision of the advertisement click rate prediction.
Arnold titled “Influencer Marketing part 1-Who are influencers?” teaches influencer marketing can take many forms from word of mouth, earned media, social media, gamification to public relations by using social media now influencers can reach and impact millions of followers instantly.
Conclusion
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/RAQUEL ALVAREZ/ Primary Examiner, Art Unit 3622
