Prosecution Insights
Last updated: April 19, 2026
Application No. 18/543,666

SEGMENT DISCOVERY AND CHANNEL DELIVERY

Final Rejection §101
Filed
Dec 18, 2023
Examiner
BAGGOT, BREFFNI
Art Unit
3621
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Adobe Inc.
OA Round
2 (Final)
35%
Grant Probability
At Risk
3-4
OA Rounds
3y 6m
To Grant
58%
With Interview

Examiner Intelligence

Grants only 35% of cases
35%
Career Allow Rate
146 granted / 418 resolved
-17.1% vs TC avg
Strong +24% interview lift
Without
With
+23.6%
Interview Lift
resolved cases with interview
Typical timeline
3y 6m
Avg Prosecution
34 currently pending
Career history
452
Total Applications
across all art units

Statute-Specific Performance

§101
36.2%
-3.8% vs TC avg
§103
34.9%
-5.1% vs TC avg
§102
3.4%
-36.6% vs TC avg
§112
12.4%
-27.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 418 resolved cases

Office Action

§101
AIA DETAILED ACTION Status of claims This action responds to amendment of 12/30/25. Claims 1-20 examined. Parent data 18271153 filed 07/06/2023 is a National Stage entry of PCT/KR2022/020779, Intl Filing Date: 12/18/2023 Applicant(s): Adobe, San Jose PNG media_image1.png 312 642 media_image1.png Greyscale Response to Remarks Applicant amendment remarks fully considered but unfortunately not fully persuasive. Examiner thanks attorney for amendment to advance prosecution. As to applicant argument that Cannot be practically performed (remarks p11) Examiner Unfortunately, conclusory and unsubstantiated and what proof there is comes from the Spec (remarks p11-12) not the claim. The conversion dynamic and static is unfortunately just data labels, description of data MPEP 2111.05. As to applicant argument that Machine learning … not performable….mind (remarks p12) Examiner Machine learning doesn’t even need a machine. ML is generic additional element generally applied. ML e.g regression, least squares fit can be done mentally if data sets are small. As to applicant argument that Technical operations (remarks p12-13) Examiner Unfortunately, conclusory and unsubstantiated and what proof there is comes from the Spec (remarks p11-12) not the claim. As to applicant argument that practical application …. mapping (remarks p14-15) Examiner Mapping is indexing, and therefore unfortunately ineligible. Creating an index, and using that index to search for and retrieve data (Int. Ventures v. Erie Indemnity I: ‘434 patent) 101 maintained. 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 pending claims are rejected under 35 USC 101. 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 process 9 manufacture 15 machine). 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). CLAIM 1 (9 15 similar) 1. A method, comprising: O obtaining activity data from a [ user device ] associated with a user O selecting, using of a machine learning model, a user segment for the user based on the activity data O mapping, using a mapping function of the machine learning model, the activity data for the user segment to features defined by multiple media channels, the mapping function transforming dynamic behavioral characteristics of the activity data into static characteristics used by the media channels as part of user selection for content delivery O generating, using an objective predictor of the machine learning model, an objective prediction for the user segment based on the features and resource components of the media channels subject to a defined resource constraint , the objective prediction identifying a media channel from the multiple media channels with a composite scalar metric above a defined threshold and O providing content to the user device via the media channel bold = judicial exception [ apply it ] Here, an abstract idea for an ads, necessarily organizing human behavior. The claim is ad, and necessarily involves collect data, analyze it, display results. Machine learning is generic element generally applied See Approaches to ML Carnegie Mellon (1984) Trinity Info Media v Covalent (CAFC 2023) (survey ineligible). Collecting info, analyzing it, displaying certain results. Elec. Power Group (CAFC 2016) The pending claims: rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites an abstract idea, CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY, MENTAL PROCESSES Alice clearinghouse via computer Bilski hedge via computer Here targeted marketing via computer US Ser 18543666 US Ser 18543666 EPG O obtaining activity data from a [ user device ] associated with a user; O selecting, using a selector of a machine learning model, a user segment for the user based on the activity data; O mapping, using a mapping function of the machine learning model, the activity data for the user segment to features defined by multiple media channels, each media channel assigned a resource component; O generating, using an objective predictor of the machine learning model, an objective prediction for the user segment based on the features and resource components of the media channels, the objective prediction identifying a media channel from the multiple media channels with a composite scalar metric above a defined threshold; and O providing content to the user device via the media channel. PNG media_image2.png 681 529 media_image2.png Greyscale Collect info, analyze it, display certain results. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Collecting user data, assigning the user to a segment, determining a prediction, selecting a channel and providing content based on the selection recites a concept performed in the human mind. But for the “machine learning model”, the claim encompasses collecting data, segmenting or classifying the users activity, generating a prediction, and providing content to a user over best media channel using his/her mind. The mere nominal recitation of prediction being performed by generic trained machine learning does not take the limitations out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The processor and memory including instructions executed by the processor is just applying generic computer components to the recited abstract limitations. (Step 2A-Prong 1: YES. The claims are abstract) DEPENDENT CLAIMS CLAIM 2 2. The method of claim 1, wherein the composite scalar metric comprises an effective resource consumption metric that combines a first metric representing predictive target accuracy and a second metric representing resource consumption per unit objective. Examiner Idea itself, survey to targeted advertising including collecting info, analyzing it, displaying certain results and description of data (composite scalar metric) CLAIM 3 3. The method of claim 1, wherein the composite scalar metric comprises an efficiency-effectiveness metric that combines a first metric representing reach-efficiency and a second metric representing accuracy of prediction. Examiner Idea itself, survey to targeted advertising including collecting info, analyzing it, displaying certain results and description of data (composite scalar metric) CLAIM 4 4. The method of claim 1, comprising generating a conversion prediction for the user segment using the machine learning model, wherein the content is provided based on the conversion prediction. Examiner Idea itself, targeted advertising including collecting info, analyzing it, displaying certain results using generic tool, ML CLAIM 5 5. The method of claim 1, comprising encoding, using of the machine learning model, the activity data for the user to obtain a user embedding for the user, the user embedding to comprise a behavioral embedding. Examiner Idea itself, survey to simulate advertising including collecting info, analyzing it, displaying certain results using generic tool, ML CLAIM 6 6. The method of claim 5, wherein the machine learning model is trained using content objective data and resource data. Examiner Idea itself, survey to targeted advertising including collecting info, analyzing it, displaying certain results using generic tool, ML and description of data (content objective data and resource data.) CLAIM 7 7. The method of claim 1, further comprising generating a targeted content element based on the user segment, wherein the content includes the targeted content element. Examiner Idea itself, survey to simulate advertising including collecting info, analyzing it, displaying certain results CLAIM 8 8. The method of claim 1, further comprising presenting the content on a graphical user interface (GUI) of an electronic display of a client device. Examiner The idea of math as part of analysis for the Idea itself, survey to simulate advertising including collecting info, analyzing it, displaying certain results using generic tool, GUI 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 210604) 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 generic additional elements generally applied The additional elements -- 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, 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 element is mere applying the idea on a computer See MPEP 2105, 2106 The elements are recited at a high-level of generality (eg 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, these additional elements do not integrate the abstract idea into a practical application for lack of any meaningful limits on practicing the abstract idea. The steps are computer-implemented, but one could do them with pen and paper, abacus, slide-rule etc 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 See Alice Corp p 16 of 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) 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 (ie, amount to significantly more than the judicial exception(s)) (MPEP 210605) The additional elements present only a particular technological environment. The claim recites additional elements 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 element(s) amounts to no more than mere instructions to apply the exception using a generic computer component See MPEP 2105605 Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept The claim is not patent eligible During prosecution, applicant has an opportunity and a duty to amend ambiguous claims to clearly and precisely define the metes and bounds of the claimed invention The claim places the public on notice of the scope of the patentee’s right to exclude See, eg, Johnson & Johnston Assoc Inc v RE Serv Co, 285 F3d 1046, 1052, 62 USPQ2d 1225, 1228 (Fed Cir 2002) (en banc) As stated in Halliburton Energy Servs, Inc v M-I LLC, 514 F3d 1244, 1255, 85 USPQ2d 1654, 1663 (CAFC 2008): “We note that the patent drafter is in the best position to resolve the ambiguity in the patent claims, and it is highly desirable that patent examiners demand that applicants do so in appropriate circumstances so that the patent can be amended during prosecution rather than attempting to resolve the ambiguity in litigation” CONCLUSION 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. 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

Dec 18, 2023
Application Filed
Oct 01, 2025
Non-Final Rejection — §101
Nov 18, 2025
Applicant Interview (Telephonic)
Nov 19, 2025
Examiner Interview Summary
Dec 30, 2025
Response Filed
Jan 25, 2026
Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
35%
Grant Probability
58%
With Interview (+23.6%)
3y 6m
Median Time to Grant
Moderate
PTA Risk
Based on 418 resolved cases by this examiner. Grant probability derived from career allow rate.

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