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
2. This Office Action is in response to the initial filing of application #18/766547 on 07/08/2024.
3. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
4. 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
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a campaign feedback based incremental hybrid audience suggestion AI engine comprising: a first interface via which a user can input 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;
pregenerating a set of audiences based on the user input goals;
allowing a user to select at least one of the pregenerated set of audiences;
and allowing a user to launch a campaign.
The steps 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;
pregenerating a set of audiences based on the user input goals;
allowing a user to select at least one of the pregenerated set of audiences;
and allowing a user to launch a campaign,
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 an AI engine, a first interface, a machine learning model, optimization engine, a processor and a memory. 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; pregenerating a set of audiences based on the user input goals; allowing a user to select at least one of the pregenerated set of audiences; and allowing a user to launch a campaign) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation the engine uses an incremental learning machine model to facilitate looking at a universal set of users through singular focus lens, this feature is considered math, and therefore is a part of the abstract idea. Because the machine learning model 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 claim is 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 an AI engine, a first interface, a machine learning model, optimization engine, a processor and a memory 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:
a first interface via which a user can input one or more goals; pregenerating a set of audiences based on the user input goals; allowing a user to select at least one of the pregenerated set of audiences; and allowing a user to launch a campaign, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claim 1 is not eligible.
As for dependent claims 2-20, these claims recite limitations that further define the same abstract idea in claim 1. 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 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
7. Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the
four categories of patent eligible subject matter because the claim is directed to
software per se. A computer program can be eligible for patent protection if it is
tangibly embodied on a non-transitory computer readable medium and, when
executed by a computer, performs the steps of the invention. Dependent claims 2-20 are also rejected under 35 U.S.C. 101 for being dependent upon independent claim 1.
Claim Rejections - 35 USC § 102
8. 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.
9. 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 –
10. Claims 1-12 and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Myers et al. (U.S. Pub. No. 2014/0029103) (hereinafter ‘Myers’).
Claim 1: Myers discloses a campaign feedback based incremental hybrid audience suggestion AI engine comprising:
a first interface via which a user can input one or more goals, 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, Myers teaches client intake data may also include client goals and relevant metrics, which may inform which third-party sources to request data from at step 402. The data collection module 108 may retrieve data from the client database 126. The data collection module 108 may collect all data from the client database 126 or may only request a subset of data and Myers further teaches a client may be more interested in ad revenue, CTR, and lead generation than in brand awareness, repeat customers, and ad cost optimization (see at least paragraphs 0021, 0060, 0091 and 0096);
pregenerating a set of audiences based on the user input goals, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029);
allowing a user to select at least one of the pregenerated set of audiences, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029);
and allowing a user to launch a campaign, Myers teaches AI technology can
effectively analyze many data sources from CRM, client, and third party sources to determine the probability of a consumer taking a specific action, thereby making campaigns more effective and providing actionable and highly refined iterative results. AI can create micro-targeted look alike audiences based on past campaigns to target new contacts and accelerate sales (see at least paragraphs 0029, 0030 and 0073).
Claim 2. Myers teaches the Al engine according to claim 1, and Myers further teaches where after a campaign is launched, real-time campaign performance is used, and click and conversion signals map back to the best performing audiences in the campaign, Myers teaches create advertising messaging and creative executions from an inventory of images and video clips. This creative is based on what would work best to meet certain marketing goals and objectives, matched with pre-identified
enhanced personas and audience segmentation criteria (see at least paragraphs 0073 and 0076).
Claim 3: Myers teaches the Al engine according to claim 1, and Myers further teaches including generating additional audiences based on audiences that work best with the campaign, Myers teaches create advertising messaging and creative executions from an inventory of images and video clips. This creative is based on what would work best to meet certain marketing goals and objectives, matched with pre-identified enhanced personas and audience segmentation criteria (see at least paragraphs 0073 and 0076).
Claim 4: Myers teaches the Al engine according to claim 1, and Myers further teaches where the engine uses at least one incremental learning machine learning model, facilitating looking at a universal set of users through a singular focus lens, Myers teaches machine-learning technologies have improved, advances in data analytics and Artificial Intelligence (AI), marketing personas can now be created and managed with
minimal human intervention (See at least paragraph 0032).
Claim 5: Myers teaches the Al engine according to claim 1, and Myers further teaches including adjustment of the composition of audiences based on real-time or other campaign performance or feedback, Myers teaches create advertising messaging and creative executions from an inventory of images and video clips. This creative is based on what would work best to meet certain marketing goals and objectives, matched with pre-identified enhanced personas and audience segmentation criteria (see at least paragraphs 0073 and 0076).
Claim 6.: Myers teaches the Al engine according to claim 1, and Myers further teaches including generating a new set of audiences, ranking them, and incorporating a portion of them, Myers teaches AIA market and advertising domain experts may review the preliminary creative and, based on the persona classifications plus the key data gathered from the content cube framework (namely product, brand, promotion, creative mix, platform medium, calendar, and all the other attributes defined for each discrete campaign) make a holistic determination of which 4-5 top 'preliminary creative' are best suited for each persona and adding additional tags manually and automatically to these selections. These are then tested through simulation to verify the right alignment of personas and creative has been made for each primary persona and any micro-target audience derivatives from this (see at least paragraph 0104).
Claim 7: Myers teaches the Al engine according to claim 1, and Myers further teaches including adjusting the engine based on one or more of: behavior or interest data of a site visitor, predictive probability of user action, and similarity of users to other users, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029).
Claim 8: Myers teaches the Al engine according to claim 1, and Myers further teaches including additional heuristics including one or more of high engagement clickers and converters, and prior user relationships to other products, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 9: Myers teaches the Al engine according to claim 1, and Myers further teaches including preliminary data, including one or more of: prior campaigns, data collected from web sites through tags, email service provider click logs, email extensions, identity graph attributes, offline graph attributes, propensity of individual hashes to engage with specific advertisers, online browsing patterns of users on an aggregate basis, and customer lifetime value of a user for an advertiser, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 10: Myers teaches the Al engine according to claim 1, and Myers further teaches including click and conversion signals from a plurality of campaigns, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 11: Myers teaches the Al engine according to claim 1, and Myers further teaches including multiple models for different heuristics, including one or more of: a model for contextual audiences based on advertiser attributes as inputs and outputs scores for users related to contextual relevance for an advertiser, and a model for ranking users similar to existing converters, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029).
Claim 12: Myers teaches the Al engine according to claim 1, and Myers further teaches including a model taking a days campaign performance as input and predicting audience composition to be active on the campaign for the next day, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029).
Claim 15: Myers teaches the Al engine according to claim 1, and Myers further teaches including dynamic adjustment of the audience composition of users that go into targeting for a campaign, including based on performance of a current campaign, or users who convert through a campaign, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029).
Claim 16: Myers teaches the Al engine according to claim 1, and Myers further teaches including periodically activating portions of the audience traffic in a campaign, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 17: Myers teaches the Al engine according to claim 1, and Myers further teaches where a hybrid audience is obtained from a variety of optimization functions including: models that generate an audience to optimize, and targeting based on interest behavior or ad page context, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 18: Myers teaches the Al engine according to claim 1, and Myers further teaches including incorporating feedback signals into models to generate more audiences of similar kind, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraph 0029).
Claim 19: Myers teaches the Al engine according to claim 1, and Myers further teaches where the engine refrains from adding audiences that do not work for a particular campaign setup, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim 20: Myers teaches the Al engine according to claim 1, and Myers further teaches including using a variety of precomputed audiences for a specific advertiser, including one or more of: predictive audiences relevant to an advertiser, contextual audiences relevant to an advertiser, email engagement audiences, site visitor audiences, and people who liked similar products, Myers teaches AI and geolocation data together with Google Analytics, social media analytics, messaging and chat feeds, business intelligence (BI) tools, CRM, digital marketing platforms, and other consumer data are also incorporated into audience segmentation and engagement predictors to drive sales, increase local retail foot traffic and encourage personalized recommendations based on a variety of purchase triggers (see at least paragraphs 0029, 0030 and 0073).
Claim Rejections - 35 USC § 103
11. 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.
12. 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.
13. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
14. Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Myers et al. (U.S. Pub. No. 2014/0029103) (hereinafter ‘Myers’) in view of Bhatia et al. (U.S. Pub. No. 2012/0089697) (hereinafter ‘Bhatia’).
Claim 13: Myers teaches the Al engine according to claim 1, but Myers does not explicitly teach including an auction mechanism utilizing strategy cards that are a combination of pricing types and optimization strategies. However, Bhatia teaches in an auction-based online advertising marketplace, advertisers may bid in connection
with placement of advertisements, although many other factors may also be included in determining advertisement selection or ranking. Bids may be associated with amounts the advertisers pay for certain specified occurrences, such as for placed or clicked-on advertisements, for example (see at least paragraph 0019). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Myers to modify to include the teaching of Bhatia in order for advertisers to determine ad placing that would result to better ad clicks or impressions.
Claim 14: Myers teaches the Al engine according to claim 1, but Myers does not explicitly teach including an optimization engine for implementing a strategy card, where the optimization engine uses historical campaign performance, takes into account campaign and/or advertiser attributes, and allows bidders to bid based on click probability and conversion probability of a user for a specific campaign. However, Bhatia teaches in an auction-based online advertising marketplace, advertisers may bid in connection with placement of advertisements, although many other factors may also be included in determining advertisement selection or ranking. Bids may be associated with amounts the advertisers pay for certain specified occurrences, such as for placed or clicked-on advertisements, for example (see at least paragraph 0019). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Myers to modify to include the teaching of Bhatia in order for advertisers to determine ad placing that would result to better ad clicks or impressions.
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
16. 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).
17. 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).
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 01/08/2026