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 .
Status of Claims
This Office Action is in response to the communication filed on 07/13/2026.
Claims 1, 6-9, 11-14, 16 and 18 have been amended.
Claims 2-5, 10, 15, 17 and 19 have been cancelled.
New claims 21-28 have been added.
6. Claims 1, 6-9, 11-14, 16, 18 and 21-28 are currently pending and are considered below.
Information Disclosure Statement
6. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1, 6-9, 11-14, 16, 18 and 21-28 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 22, recites a computer-implemented method for campaign feedback based incremental hybrid audience suggestion, the method comprising:
receiving one or more goals input by a user, the goals including one or more of:
personalizing live customers, acquiring new customers, interest based audience targeting, contacting live audience, contextual targeting, analyzing customer behavior, reactivating dormant customers, and increasing revenue through increased conversions;
processing campaign performance feedback data generated from executing one or more prior campaigns in association with the input user goals;
generating a pregenerated set of audiences based on the input user goals, the generating using an incremental hybrid audience suggestion model that determines an audience composition for each of the pregenerated audiences, the audience composition including a composition percentage for multiple different audience types, the composition percentage identifying a percentage of a total pregenerated audience that is made up of users included in each particular audience type;
receiving a selection of at least one of the pregenerated set of audiences as a target audience; and
initiating a content delivery campaign using the selected pregenerated audience by
transmitting the target audience to a campaign delivery system;
collecting real time performance feedback during deployment of the content delivery
campaign to the target audience; and
adjusting, by the incremental hybrid audience suggestion model, an audience composition of the target audience by dynamically modifying a composition percentage of two or more of the audience types included in the target audience to increase or decrease a total number of users of the two or more audience types at are included in target audience.
The steps of,
receiving one or more goals input by a user, the goals including one or more of:
personalizing live customers, acquiring new customers, interest based audience targeting, contacting live audience, contextual targeting, analyzing customer behavior, reactivating dormant customers, and increasing revenue through increased conversions;
processing campaign performance feedback data generated from executing one or more prior campaigns in association with the input user goals;
generating a pregenerated set of audiences based on the input user goals, the generating using an incremental hybrid audience suggestion model that determines an audience composition for each of the pregenerated audiences, the audience composition including a composition percentage for multiple different audience types, the composition percentage identifying a percentage of a total pregenerated audience that is made up of users included in each particular audience type;
receiving a selection of at least one of the pregenerated set of audiences as a target audience; and
initiating a content delivery campaign using the selected pregenerated audience by
transmitting the target audience to a campaign delivery system;
collecting real time performance feedback during deployment of the content delivery
campaign to the target audience; and
adjusting, by the incremental hybrid audience suggestion model, an audience composition of the target audience by dynamically modifying a composition percentage of two or more of the audience types included in the target audience to increase or decrease a total number of users of the two or more audience types at are included in target audience,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for collecting data from websites through tags, click logs, email extensions, identity graphs, and other sources. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to improving timeline of events for product location pairs.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, a memory, a first user interface and an AI engine. The processor and the memory is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of a first interface via which a user can input one or more goals; receiving one or more goals input: personalizing live customers, acquiring new customers; processing campaign performance feedback data; generating a pregenerated set of audiences; receiving a selection of at least one of the pregenerated set of audiences; and initiating a content delivery campaign; collecting real time performance feedback; and adjusting an audience composition of the target audience) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation processing and generating by an AI engine, this feature is considered math, and therefore is a part of the abstract idea. Because the AI engine in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. 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. The claims are directed to an abstract idea.
The claim does 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 a processor, a memory, a first user interface and an AI engine amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is already present when the steps are considered separately. That is, a processors, a memory and a machine learning model, performing commercial interactions including:
receiving one or more goals input: personalizing live customers, acquiring new customers; processing campaign performance feedback data; generating a pregenerated set of audiences; receiving a selection of at least one of the pregenerated set of audiences; and initiating a content delivery campaign; collecting real time performance feedback; and adjusting an audience composition of the target audience, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1 and 22 are not eligible.
As for dependent claims 6-9, 11-14, 16, 18, 21 and 23--28, these claims recite limitations that further define the same abstract idea in claims 1 and 22. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1, 6-9, 11-14, 16, 18 and 21-28 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
9. Applicant’s arguments filed 07/13/2026, with respect to the rejection of claims 1-20 have been fully considered and are persuasive. The rejection of now amended claim 1 has been withdrawn.
10. Applicant's arguments filed 07/13/2026 with respect to the rejection of claims 1, 6-9, 11-14, 16, 18 and 21-28 have been fully considered but they are not persuasive.
See new rejection on 101 with regards to the amendments.
11. Applicant’s arguments filed 07/13/2026, with respect to the rejection of claims
1, 6-9, 11-14, 16, 18 and 21-28 have been fully considered and are persuasive. The rejection of claims 1, 6-9, 11-14, 16, 18 and 21-28 has been withdrawn. The prior art Myers et al. (U.S. Pub. No. 2014/0029103) in view of in view of Bhatia et al. (U.S. Pub. No. 2012/0089697) do not teach or suggest the amended claims “ generating a pregenerated set of audiences based on the input user goals, the generating using an incremental hybrid audience suggestion model that determines an audience composition for each of the pregenerated audiences, the audience composition including a composition percentage for multiple different audience types, the composition percentage identifying a percentage of a total pregenerated audience that is made up of users included in each particular audience type; and adjusting, by the incremental hybrid audience suggestion model, an audience composition of the target audience by dynamically modifying a composition percentage of two or more of the audience types included in the target audience to increase or decrease a total number of users of the two or more audience types at are included in target audience.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
13. Vakil et al. (U.S. Pub. No. 2024/0370898 (discloses (1) receive, by a processor, a first optimization objective for a first campaign and access to customer information; (2) segment, at the processor, customers identified in the customer information into a plurality of nanosegments using a cluster analysis algorithm that identifies clusters in the customer information based on similar characteristics and predefined rules; (3) select, by a machine learning (ML) optimization model and based on the first optimization objective, a first set of nanosegments from the plurality of nanosegments for inclusion in the first campaign; (4) pass, from the processor, the first set of nanosegments to a first generative artificial intelligence (AI) component; (5) automatically generate, via the first generative AI component, digital content including a first element for a first offer in response to the customer information for only the first set of nanosegments, the first element including one of a tagline, image, and content; and (6) provide, to a first client computing device, first data that causes the first client computing device to present a visual representation of the first element as part of the first offer, the first client computing device being associated with a first customer identified in the first set of nanosegments (see at least paragraphs 0012- 0013).
14. Yates (U.S. Patent no. 12,307492) discloses the system uses the piece of creative content in ranking and allocation models for use in one or more of: search engine optimization, advertisement ranking optimization, and advertisement bidding auctions. In some embodiments, the system is configured to utilize the generated creative content in search engine optimization (hereinafter “SEO”). By incorporating the creative content into various SEO strategies, the method enhances the discoverability of the product listings on search engines. This is particularly crucial as, for example, users often begin their online shopping journeys with search queries (see at least column 11 line 66 through column 12 line 23).
15. Update search found:
16. Herz et al. (U.S. Pub. No. 2001/0014868) discloses sequential hybrid method. First apply the k means procedure to do (1a) above, so that offers are labeled by cluster based on which shoppers were interested in them, then use supervised clustering (maximum likelihood discriminant methods) using the offers' other attributes to do the process of method (2a) described above. This tries to use knowledge of who read what to do a better job of clustering based on word frequencies. One could similarly combine the methods (1b) and (2b) described above. Hierarchical clustering of offers is often useful. Hierarchical clustering produces a tree which divides the offers first into two large dusters of roughly similar offers; each of these clusters is in turn divided into two or more smaller clusters, which in turn are each divided into yet smaller clusters until the collection of offers has been entirely divided into "clusters" consisting of a single offer each, as diagramed in FIG. 2. (see at least paragraph 0164).
17. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached at 571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 01/08/2026
