Prosecution Insights
Last updated: August 15, 2026
Application No. 18/591,797

SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE TO GENERATE MARKETPLACE LISTINGS

Non-Final OA §101§DP
Filed
Feb 29, 2024
Examiner
BAGGOT, BREFFNI
Art Unit
3621
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Pattern Inc.
OA Round
3 (Non-Final)
35%
Grant Probability
At Risk
3-4
OA Rounds
1y 0m
Est. Remaining
60%
With Interview

Examiner Intelligence

Grants only 35% of cases
35%
Career Allowance Rate
149 granted / 425 resolved
-16.9% vs TC avg
Strong +25% interview lift
Without
With
+25.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
29 currently pending
Career history
459
Total Applications
across all art units

Statute-Specific Performance

§101
29.5%
-10.5% vs TC avg
§103
47.9%
+7.9% vs TC avg
§102
4.8%
-35.2% vs TC avg
§112
11.6%
-28.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 425 resolved cases

Office Action

§101 §DP
AIA DETAILED ACTION Status of claims Claims 1,3-7,9-14 and 16-17, 19-20 examined. Dependents rejected claims apriori rejected. Amended 1 3 7 14 16 17 19 20 Canceled 2 8 15 18 New none Response to Remarks Applicant amendment remarks fully considered but unfortunately not fully persuasive. Examiner thanks Attorney for the amendment to advance prosecution. Double patenting maintained. Examiner understands Applicant would like to address the issue later on when the application claims may have diverged, obviating the need for the rejection. As to applicant argument that Not abstract idea Examiner Independent Claims 1 14 20 are machine process manufacture versions of same idea. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY Alice clearinghouse via computer Bilski hedge via computer Here marketing via computer The Claims: rejected under 35 U.S.C. 101 as directed to an abstract idea (Certain Methods of Organizing Human Activity) without significantly more. The claims are directed to CERTAIN METHODS OF ORGANIZING HUMAN BEHAVIOR. 1) managing personal behavior or relationships or interactions between people 2) fundamental economic practice and a commercial interaction 3) long-standing commercial practice. The additional elements are claimed at a high level of generality. Applicant simply computer implements a business process, solving a business problem not a technical problem. NONSTATUTORY OBVIOUSNESS DOUBLE PATENTING The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. The claims are rejected on the ground of nonstatutory double patenting as unpatentable over claims of US Serial No. 18591797 US Serial No. 18591797 US Serial No. 18984310 1. A system comprising: [ a processor and O a memory including instructions that, when executed by the processor, cause the processor to: ] O receive input data indicating at least one target product and at least one target marketplace O retrieve, from a product information management database, product data associated with the at least one target product O retrieve, from the product information management database, marketplace data associated with the at least one target marketplace O generate at least one prompt for the at least one target product based on the product data and the marketplace data , wherein the marketplace data includes at least marketplace specific compliance requirements associated with the target marketplace O provide the at least one prompt to a [large language model], the [large language model] being configured to receive one or more prompts and to generate one or more marketplace listings based on the one or more prompts Oreceive, from the [large language model], at least one marketplace listing corresponding to the at least one target product and configured for the at least one marketplace and O in response to receiving the at least one marketplace listing from the large language model, provide the at least one marketplace listing to a machine learning model, wherein the machine learning model generates a marketplace listing score for the at least one marketplace listing O use the marketplace listing score generated by the machine learning model to train the large language model O provide, at a display, an actionable report including the at least one marketplace listing. Claim 1 A system configured to control a bidding lurking process in an advertising bidding environment, the system comprising: [ a processor; and a memory including instructions that, when executed by the processor, cause the processor to: ] o receive inputs, wherein the inputs include data indicating at least one of a product to be advertised, a type of product to be advertised, a marketplace to advertise the product or type of product, a desired amount to spend advertising the product or type of product, a maximum amount to spend advertising the product or type of product, a type of performance metric associated with advertising the product or type of product, a minimum required performance metric associated with advertising the product or type of product, and a risk threshold associated with advertising the product or type of product, o calculate at least one of product targets and keyword targets based on the inputs, o calculate probabilities of outages of products associated with the at least one of the product targets and the keyword targets, o based on the received inputs, calculate (i) a prediction of a performance output for at least one bid to be executed by the bidding lurking process and (ii) an estimate of a cost per target for the at least one bid, and o control the bidding lurking process to selectively execute bids on the at least one of the product targets and keyword targets based on (i) the calculated probabilities, (ii) the prediction of the performance output, and (iii) the estimate of the cost per target. It would be obvious looking at 18591797’s listing to see that as the underlying for a bid and conversely looking at 18984310 that there’d be an underlying listing for the bid and basically what it would have been composed of. Analogizing to Bilski, it would have been obvious given a put to hedge with a call and obvious given a call to hedge with a put. 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 The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) is/are directed to one or more abstract idea(s). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the abstract idea(s). Step 1: (MPEP 2106.03) The claims and dependents are directed to statutory classes (1 machine, 14 process, 20 manufacture). The claims herein are directed to subject matter 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). Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception -- law of nature, natural phenomenon, abstract idea. (MPEP 2106.04). Collecting info, analyzing it, displaying certain results. Elec. Power Group (CAFC 2016) 18591797 EPG PNG media_image1.png 423 472 media_image1.png Greyscale 1. A system comprising: [ a processor and O a memory including instructions that, when executed by the processor, cause the processor to: ] O receive input data indicating at least one target product and at least one target marketplace O retrieve, from a product information management database, product data associated with the at least one target product O retrieve, from the product information management database, marketplace data associated with the at least one target marketplace O generate at least one prompt for the at least one target product based on the product data and the marketplace data , wherein the marketplace data includes at least marketplace specific compliance requirements associated with the target marketplace O provide the at least one prompt to a [large language model], the [large language model] being configured to receive one or more prompts and to generate one or more marketplace listings based on the one or more prompts O receive, from the [large language model], at least one marketplace listing corresponding to the at least one target product and configured for the at least one marketplace O in response to receiving the at least one marketplace listing from the [large language model], provide the at least one marketplace listing to a machine learning model, wherein the [machine learning model] generates a marketplace listing score for the at least one marketplace listing O use the marketplace listing score generated by the [machine learning model] to train the large language model O provide, at a display, an actionable report including the at least one marketplace listing Collect info, analyze it, display certain results. Independent Claims 1 14 20 are machine, process, manufacture versions of same idea. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY Alice clearinghouse via computer Bilski hedge via computer Here marketing via computer & LLM The Claims: rejected under 35 U.S.C. 101 as directed to an abstract idea (Certain Methods of Organizing Human Activity) without significantly more. The claims are directed to CERTAIN METHODS OF ORGANIZING HUMAN BEHAVIOR. 1) managing personal behavior or relationships or interactions between people 2) fundamental economic practice and a commercial interaction 3) long-standing commercial practice. The additional elements are claimed at a high level of generality. Applicant simply computer implements a business process, solving a business problem not a technical problem. The elements don’t need a computer. Here, the innovative concept is an abstract idea using additional elements which are generic and generally applied. These additional elements do not add significantly more. Training/processing with machine learning is an idea itself of organizing information through mathematical correlations, using categories to organize, store and transmit information. Machine learning is old and well-known (NPL: “Approaches to Machine Learning, P. Langley at Carnegie-Mellon University (1984) and the references it refers to from MORE THAN A HALF-CENTURY AGO). The limitation of machine learning is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind, but for the recitation of generic computer components. Other than reciting ‘processor’, ‘memory’, ‘medium’, ‘computer’, nothing in the claim precludes the steps from being performed in the mind. For example, but for the computer, training in the context of this claim encompasses user manually calculating to generate a model that reflects importance of attributes. This judicial exception is not integrated into a practical application. In particular, the claim recites additional element – user interface to perform the claim steps. The elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application for lack of any meaningful limits on practicing the abstract idea. Applicant uses certain methods of organizing human activity followed by extra-solution activity (display). The additional elements present only a particular technological environment. The additional elements are not sufficient to amount to significantly more than the judicial exception because the claims do not provide improvements to another technology or technical field, improvements to the functioning of the computer itself, and do not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. The limitations (those beyond the abstract idea) do not improve the technical field that the abstract idea limitations invoke. Moreover, these generic limitations do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment, not meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Alice Corp p.16 slip op. noting that none of the hardware recited "offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment', that is implementation via computers" (citing Bilski 561 US at 610). The machine learning is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind, but for the recitation of generic computer components. MPEP 2106.05(a) Examples not sufficient to show an improvement in computer-functionality: iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential) 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). The claim says one is to take the idea and “apply it” with generic elements generally applied. This judicial exception is not integrated into a practical application. In particular, the claim only recites an additional elements – e.g. processor, memory to perform data gathering, math, mental steps. The additional elements e.g. is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional elements add MPEP 2106.05 is “iii. Mere automation of manual processes”. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”). Dependent claims 3-7,10-13 and 16-17, 19-20-- the 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 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 amount to no more than mere instructions to apply the exception using a generic computer component. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. POC Pertinent prior art cited by not relied upon: Fuchs US20230385887 Any inquiry concerning this communication or earlier communications from the examiner should be directed to BREFFNI X BAGGOT whose telephone number is (571)272-7154. The examiner can normally be reached M-F 8a-10a, 12p-6p. 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, Waseem Ashraf can be reached at 571-270-3948. 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. BREFFNI BAGGOT Primary Examiner Art Unit 3621 /BREFFNI BAGGOT/Primary Examiner, Art Unit 3621
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Prosecution Timeline

Show 2 earlier events
Feb 19, 2026
Response Filed
Mar 27, 2026
Final Rejection mailed — §101, §DP
May 27, 2026
Response after Non-Final Action
Jun 18, 2026
Request for Continued Examination
Jun 26, 2026
Response after Non-Final Action
Jul 15, 2026
Non-Final Rejection mailed — §101, §DP
Aug 06, 2026
Applicant Interview (Telephonic)
Aug 06, 2026
Examiner Interview Summary

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Prosecution Projections

3-4
Expected OA Rounds
35%
Grant Probability
60%
With Interview (+25.2%)
3y 5m (~1y 0m remaining)
Median Time to Grant
High
PTA Risk
Based on 425 resolved cases by this examiner. Grant probability derived from career allowance rate.

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