Prosecution Insights
Last updated: April 19, 2026
Application No. 17/194,920

Automatically Learning Process Characteristics for Model Optimization

Final Rejection §101
Filed
Mar 08, 2021
Examiner
TC 3600, DOCKET
Art Unit
3600
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Aible Inc.
OA Round
6 (Final)
4%
Grant Probability
At Risk
7-8
OA Rounds
1y 1m
To Grant
5%
With Interview

Examiner Intelligence

Grants only 4% of cases
4%
Career Allow Rate
5 granted / 142 resolved
-48.5% vs TC avg
Minimal +2% lift
Without
With
+1.5%
Interview Lift
resolved cases with interview
Fast prosecutor
1y 1m
Avg Prosecution
206 currently pending
Career history
348
Total Applications
across all art units

Statute-Specific Performance

§101
36.1%
-3.9% vs TC avg
§103
34.6%
-5.4% vs TC avg
§102
13.9%
-26.1% vs TC avg
§112
10.9%
-29.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 142 resolved cases

Office Action

§101
Detailed Action The following is responsive to Applicant’s submission filed on 30 September 2025. The following is a Final Office Action. Accordingly, Claims 1-28 and 33-36 are rejected below. Applicant’s Amendments Applicant’s amendments are acknowledged. Applicant’s Arguments Applicant argues AI cannot be practically performed in the human mind and cites training and assessing multiple models. Examiner responds in the independent claims AI is not actively recited, is just describing the model, and the model is not trained or used. AI and these steps generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). Applicant argues the claim provides a technical solution to evaluation of complex models and determination of models, which increases accuracy, speed, and conserving of resources. Examiner responds the steps of evaluating and determining are part of the abstract idea and improvements to a business process. Each of the additional elements are just being used as tools to implement the abstract idea and not improving the technology. Applicant argues the judicial exception is integrated into a practical application and cites the steps cited in the abstract idea. Examiner responds each of the steps beyond the abstract idea are generic computing elements performing generic computing functions and used as tools to implement the abstract idea. Applicant argues, “a novel and non-obvious functionality is one not well understood, routine, or conventional. "If the element is not widely prevalent or in common use or is otherwise beyond those elements recognized in the art or by the courts as being well-understood, routine or conventional, then the element will in most cases favor eligibility." See MPEP 2106.05(d) Examiner responds prior art has no impact on the 101. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 11 (Fed Cir. May 15, 2018). See MPEP 2106.05(I) - in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo,...(rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101"). 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 1-28 and 33-36 are rejected under 35 U.S.C. 101 because 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. Claims 1-28 and 33-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, Claims 1-28 and 33-36 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-28 and 33-36 fall within a statutory class of process, machine, manufacture, or composition of matter. With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claim 1, 10, 19 and 28 includes limitations reciting functionality that optimizing a predictive model based on a set of inputs, including: monitoring data characterizing inputs to a prediction process that classifies event and an output of the prediction process, wherein the prediction process comprises a set of models including at least one model... obtaining feedback data by monitoring user behavior, wherein the feedback data characterizes an accuracy of the output of the prediction process by monitoring user behavior... determining, from the monitoring and the obtained feedback data, a cost-benefit associated with the output of the prediction process, wherein the cost-benefit indicates a result of user compliance with the output... providing the determined cost-benefit, wherein the cost-benefit characterizes... modifying the determined cost-benefit based on user input received...the user input comprising a user selection... updating at least one model among the set of models with an updated training goal indicating at least one resource capacity value based at least on the modified cost-benefit...(Examiner notes “with an updated training goal” is just describing the model) in response to determining the cost-benefit, causing the predictive process to select a new model from the set of models or keep a current model, according to the determined cost-benefit, and deploying the predictive process on new input data which is an abstract idea reasonably categorized as Mental processes – as each of the steps can be performed in the human mind. Similarly, Claims 2-9, 11-18, and 20-27 each contain limitations describing mental processes that can be performed in the human mind and further narrow the abstract idea. With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claim 1, 10, 19 and 28 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processor, memory, instructions, computer readable medium, database, machine learning. When considered in view of the claim as a whole, Examiner submits that the additional elements are not additional elements that integrate the abstract idea into a practical application because, these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f). The “machine learning” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The steps of “received from the interactive graphical user interface, the user input comprising a user selection on the interactive graphical user interface including the user selection on a slider bar” does not change the function of the display in a meaningful way beyond a general link to display technology or beyond generic and routine display functions. Because the claimed display functions are broadly claimed and are directed to the basic functions of display technology, the claims do not improve the functioning of the display and do not improve the technical field of display technology. As a result, Claims 1, 11, 19, and 28 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two. Claims 2-6, 8, 11-15, 17, and 19-27 do not include any additional elements beyond those recited with respect to claim 1. Claims 7 and 16 further includes “deploying the trained model within an enterprise resource management system” which generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h); Similar to the display functions above, the steps in Claims 33-36, “wherein the selection comprises selection of one of a thumbs-up icon or a thumbs-down icon displayed in the interactive graphical user interface” does not change the function of the display in a meaningful way beyond a general link to display technology or beyond generic and routine display functions. As a result, Claims 2-9, 11-17, 19-27, and 33-36 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claim 1, 11, 19, and 28. With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processor, memory, instructions, computer readable medium, database, machine learning. Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. The machine learning is well-understood, routine, and conventional computer function in view of Figure 2, 0002, which describes the additional element in such a manner as to indicate that the additional element is sufficiently well-known in the art BACKGROUND [0002] Artificial intelligence models can be used to aid in decision making such as for deciding whether to replace a part of a machine, whether to classify an email as spam, and many other applications. In many approaches, an artificial intelligence model is trained on data that has been manually labeled. The steps of “received from the interactive graphical user interface, the user input comprising a user selection on the interactive graphical user interface including the user selection on a slider bar” is well-understood, routine, and conventional computer function in view of 0165, which describes display interactions in such a manner as to indicate that the additional element is sufficiently well-known in the art. The steps above do no more than generally link the user of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements individually. As a result, Claim 1 11 19 and 28 does not include additional elements amounting to significantly more than the abstract idea under Step 2B. Claims 2-6, 8, 11-15, 17, and 19-27 do not include any additional elements beyond those recited with respect to claim 1. Claims 7 and 16 further recite “deploying the trained model within an enterprise resource management system” which generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h), and are well-understood, routine, and conventional computer function in view of Spec,0002, which describes the additional elements in such a manner as to indicate that the additional elements are sufficiently well-known in the art. The steps in Claims 33-36 of “wherein the selection comprises selection of one of a thumbs-up icon or a thumbs-down icon displayed in the interactive graphical user interface” is well-understood, routine, and conventional computer function in view of 0165, which describes display interactions in such a manner as to indicate that the additional element is sufficiently well-known in the art. The steps above do no more than generally link the user of the recited abstract idea to a particular technological environment. As a result, Claims 2-9, 11-17, 19-27, and 33-36 do not include additional elements amounting to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claim 1. Accordingly, Claims 1-28 and 33-36 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PNG media_image1.png 58 1192 media_image1.png Greyscale 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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT ROSS whose telephone number is (571) 270-1555. The examiner can normally be reached on Monday-Friday 8:00 AM - 5:00 PM E.S.T.. 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, Rutao Wu, can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Scott Ross/ Examiner - Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
Read full office action

Prosecution Timeline

Mar 08, 2021
Application Filed
Dec 10, 2022
Non-Final Rejection — §101
Apr 27, 2023
Applicant Interview (Telephonic)
May 08, 2023
Examiner Interview Summary
Jun 09, 2023
Response Filed
Jun 14, 2023
Final Rejection — §101
Dec 14, 2023
Request for Continued Examination
Dec 16, 2023
Response after Non-Final Action
Jan 27, 2024
Non-Final Rejection — §101
Jul 16, 2024
Response Filed
Nov 29, 2024
Final Rejection — §101
Jan 30, 2025
Response after Non-Final Action
Feb 20, 2025
Request for Continued Examination
Feb 21, 2025
Response after Non-Final Action
Mar 22, 2025
Non-Final Rejection — §101
Sep 30, 2025
Response Filed
Dec 05, 2025
Final Rejection — §101 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 8813663
SEEDING MACHINE WITH SEED DELIVERY SYSTEM
2y 5m to grant Granted Aug 26, 2014
Patent null
Interconnection module of the ornamental electrical molding
Granted
Patent null
SYSTEMS AND METHODS FOR ENTITY SPECIFIC, DATA CAPTURE AND EXCHANGE OVER A NETWORK
Granted
Patent null
Systems and Methods for Performing Workflow
Granted
Patent null
DISTRIBUTED LEDGER PROTOCOL TO INCENTIVIZE TRANSACTIONAL AND NON-TRANSACTIONAL COMMERCE
Granted
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

7-8
Expected OA Rounds
4%
Grant Probability
5%
With Interview (+1.5%)
1y 1m
Median Time to Grant
High
PTA Risk
Based on 142 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month