Prosecution Insights
Last updated: April 19, 2026
Application No. 16/998,682

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED DOCUMENT CLASSIFICATION

Final Rejection §101
Filed
Aug 20, 2020
Examiner
VASQUEZ, MARKUS A
Art Unit
2121
Tech Center
2100 — Computer Architecture & Software
Assignee
Nationstar Mortgage LLC D/B/A/ Mr Cooper
OA Round
6 (Final)
50%
Grant Probability
Moderate
7-8
OA Rounds
4y 3m
To Grant
82%
With Interview

Examiner Intelligence

Grants 50% of resolved cases
50%
Career Allow Rate
100 granted / 201 resolved
-5.2% vs TC avg
Strong +32% interview lift
Without
With
+31.7%
Interview Lift
resolved cases with interview
Typical timeline
4y 3m
Avg Prosecution
19 currently pending
Career history
220
Total Applications
across all art units

Statute-Specific Performance

§101
27.0%
-13.0% vs TC avg
§103
38.6%
-1.4% vs TC avg
§102
7.0%
-33.0% vs TC avg
§112
22.4%
-17.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 201 resolved cases

Office Action

§101
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 Claims 1, 3-5, 7-14, and 16-23 are pending and are examined herein. Claims 1, 3-5, 7-14, and 16-23 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more. Response to Arguments Applicant’s arguments filed 12/08/2025 with respect to the interpretation under 35 USC 112(f) have been fully considered and are persuasive. In particular, Applicant argues at least on page 9 that “receiver” as it is used in the art connotes specific structure. The limitation is no longer being interpreted as invoking 35 USC 112(f) and the corresponding rejection under 35 USC 112(b) is withdrawn. Applicant’s arguments filed 12/08/2025 with respect to the rejection under 35 USC 101 have been fully considered, but are not persuasive. Applicant argues that the claims reflect an improvement to classification accuracy, speed and efficiency for computer vision and document classification. Examiner respectfully disagrees that the claims are eligible. This argument was substantively addressed in the previous office action. To the extent that the claims reflect an improvement, it is an improvement to performing document classification, which is a mental process. The claims use the computer as a tool to perform the mental process. Using the computer as a tool to perform an improved abstract idea is not an improvement to computer technology or any other technology in the sense of MPEP 2106.05(a). Applicant further argues that, as in In re Desjardins, the claims are directed to AI innovation and are consequently eligible. Examiner respectfully disagrees. The following is an excerpt from the 5 December 2025 Memorandum providing Advance notice of change to the MPEP in light of Ex Parte Desjardins (emphasis added): In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO). Unlike the claims at issue in Ex Parte Desjardins, the instant claims do not recite any limitations directed to training or adjusting the machine learning models that could plausibly be interpreted as reflecting an improvement to how the machine learning model itself operates, which was critical in the eligibility determination in Ex Parte Desjardins. 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, 3-5, 7-14, and 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis Each of the claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2 Analysis Claim 1 includes the following recitation of an abstract idea: A method for ... document sorting, comprising: (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) ...for each candidate document of the plurality of documents: iteratively,...: (a) selecting a subset of classifiers from a plurality of classifiers executable by a processor of the computing device, (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) (b) extracting a corresponding set of feature characteristics from the candidate document..., responsive to the selected subset of classifiers, the feature characteristics comprising text or visual content extracted from the candidate document; (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) (c) classifying, ..., the candidate document (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) ...(d) repeating steps (a)-(c) until a predetermined number of the selected subset of classifiers at each iteration agrees on a classification, (Each of steps (a)-(c) includes a mental process. Repeating a mental process is still a mental process.) ...classifying, ..., the candidate document according to the agreed-upon classification; and (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: ...machine learning-based (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) receiving, by a computing device, a plurality of candidate documents for sorting, (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.) the received candidate documents stored in a memory of the computing device, (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, storing or retrieving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example iv. Storing and retrieving information.) each candidate document lacking an identifier of a related candidate document; (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).) ... by the computing device (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) ... stored in the memory of the computing device (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).) ... by the processor of the computing device... according to each classifier of the selected subset of classifiers, and (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) wherein a number of classifiers in the selected subset of classifiers in a first iteration is different from a number of classifiers in the selected subset of classifiers in a second iteration; and (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).) ... by the computing device (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) collating a subset of the plurality of candidate documents, by the computing device, into a single multi-page document, responsive to each document of the subset having the same classification. (This is a mere instruction to apply the judicial exception, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f). The claim recites only the idea of a solution or outcome (i.e., the collation of the subset of documents) and uses the computer as a tool to perform an existing process (i.e., collating documents).) Claim 1 does not reflect an improvement to computer technology or any other technology. Claim 3 recites at least the abstract idea identified above in the claim upon which it depends. Claim 3 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: wherein each classifier in a selected subset utilizes different feature characteristics of the candidate document. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).) Claim 3 does not reflect an improvement to computer technology or any other technology. Claim 4 recites at least the abstract idea identified above in the claim upon which it depends. Furthermore, claim 4 recites wherein in a final iteration, ...classify the candidate document with a first classification, and ...classify the candidate document with a second classification. (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 4 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: a first number of the selected subset of classifiers (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) ...a second number of the selected subset of classifiers (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) Claim 4 does not reflect an improvement to computer technology or any other technology. Claim 5 recites at least the abstract idea identified above in the claim upon which it depends. Furthermore, claim 5 recites wherein classifying the candidate document according to the agreed-upon classification is responsive to a confidence score of the classification exceeding a threshold. (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 5 does not recite further additional elements which might integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Claim 5 does not reflect an improvement to computer technology or any other technology. Claim 7 recites at least the abstract idea identified above in the claim upon which it depends, and further recites wherein step (d) further comprises repeating steps (a)-(c) responsive to a classifier of the selected subset of classifiers returning an unknown classification. (Repeating mental processes is still a mental process. Moreover, returning an unknown classification is practical to perform in the human mind and is a recitation of a mental process.) Claim 7 does not recite further additional elements which might integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Claim 7 does not reflect an improvement to computer technology or any other technology. Claim 8 recites at least the abstract idea identified above in the claim upon which it depends. Furthermore, claim 8 recites wherein during at least one iteration, step (d) further comprises repeating steps (a)-(c) responsive to all of the selected subset of classifiers not agreeing on a classification. (Repeating mental processes is still a mental process.) Claim 8 does not recite further additional elements which might integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Claim 8 does not reflect an improvement to computer technology or any other technology. Claim 9 recites at least the abstract idea identified above in the claim upon which it depends. Furthermore, claim 9 recites wherein extracting the corresponding set of feature characteristics from the candidate document further comprises at least one of extracting text of the candidate document, identifying coordinates of text within the candidate document, or identifying vertical or horizontal edges of an image the candidate document. (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 9 does not recite further additional elements which might integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Claim 9 does not reflect an improvement to computer technology or any other technology. Claim 10 recites at least the abstract idea identified above in the claim upon which it depends. Claim 10 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: wherein the plurality of classifiers comprise a gradient boosting classifier, a neural network, a time series analysis, a regular expression parser, or one or more image comparators. (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) Claim 10 does not reflect an improvement to computer technology or any other technology. Claim 11 recites at least the abstract idea identified above in the claim upon which it depends. Claim 11 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: wherein the predetermined number of the selected subset of classifiers in at least one iteration is equal to a majority of the classifiers in the at least one iteration. (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).) Claim 11 does not reflect an improvement to computer technology or any other technology. Claim 12 recites at least the abstract idea identified above in the claim upon which it depends. Claim 12 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: wherein the predetermined number of the selected subset of classifiers in at least one iteration is equal to a minority of the classifiers in the at least one iteration. (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).) Claim 12 does not reflect an improvement to computer technology or any other technology. Claim 13 recites substantially similar subject matter to claim 1 including substantially the same abstract idea. Claim 13 recites the following additional elements which, considered individually and as an ordered combination with the additional elements addressed above with respect to claim 1, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: a computing device comprising a storage device, processing circuitry, and a receiver; (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) Claim 13 does not reflect an improvement to computer technology or any other technology. Regarding claims 14 and 16-20, the rejection of claim 13 is incorporated herein. Claims 14 and 16-20 recite substantially similar subject matter to claims 3, 9-12 and 7, respectively, and are rejected with the same rationale. Claim 21 recites at least the abstract idea identified above in the claim upon which it depends. Claim 21 further recites wherein during at least one iteration, step (b) further comprises extracting feature characteristics of a parent document of the candidate document, the parent document comprising a document related to the candidate document that has been previously classified by the computing device, the feature characteristics of the parent document comprising text or visual content extracted from the parent document; and (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) step (c) further comprises classifying the candidate document according to the extracted feature characteristics of the parent document of the candidate document. (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 21 does not recite further additional elements which might integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Claim 21 does not reflect an improvement to computer technology or any other technology. Regarding claim 22, the rejection of claim 13 is incorporated herein. Claim 22 recites substantially similar subject matter to claim 21 and is rejected with the same rationale. Claim 23 includes the following recitation of an abstract idea ...generates an identification of a corresponding document type, the iterations being repeated until a predetermined number of generated identifications from different classifiers during an iteration match, the matching identification applied to the document; (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) selecting, ..., a first subset of the plurality documents having matching identifications; and (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.) Claim 23 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: receiving, by a computing system, a plurality of documents, (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.) each lacking an identifier of a related document of the plurality of documents; (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).) for each document, iteratively applying a plurality of different machine learning classifiers, by the computing system, (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) to feature characteristics comprising text or visual content extracted from the document, (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).) wherein a number of applied classifiers in a first iteration is different from a number of applied classifiers in a second iteration, wherein each classifier (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).) ... by the computing system (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) ... collating, by the computing system, the first subset of the plurality of documents into a single document. (This is a mere instruction to apply the judicial exception, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f). The claim recites only the idea of a solution or outcome (i.e., the collation of the subset of documents) and uses the computer as a tool to perform an existing process (i.e., collating documents).) Claim 23 does not reflect an improvement to computer technology or any other technology. Conclusion THIS ACTION IS MADE FINAL. 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 Markus A Vasquez whose telephone number is (303)297-4432. The examiner can normally be reached Monday to Friday 10AM to 2PM PT. 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, Li Zhen can be reached at (571) 272-3768. 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. /MARKUS A. VASQUEZ/ Primary Examiner, Art Unit 2121
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Prosecution Timeline

Aug 20, 2020
Application Filed
Jun 01, 2023
Non-Final Rejection — §101
Oct 16, 2023
Response Filed
Nov 09, 2023
Final Rejection — §101
Apr 16, 2024
Request for Continued Examination
Apr 17, 2024
Response after Non-Final Action
Dec 02, 2024
Non-Final Rejection — §101
Feb 25, 2025
Examiner Interview Summary
Feb 25, 2025
Applicant Interview (Telephonic)
Mar 05, 2025
Response Filed
Mar 25, 2025
Final Rejection — §101
Jun 25, 2025
Examiner Interview Summary
Jun 25, 2025
Applicant Interview (Telephonic)
Jul 18, 2025
Request for Continued Examination
Jul 21, 2025
Response after Non-Final Action
Sep 04, 2025
Non-Final Rejection — §101
Dec 08, 2025
Response Filed
Jan 30, 2026
Final Rejection — §101
Mar 25, 2026
Applicant Interview (Telephonic)
Mar 25, 2026
Examiner Interview Summary
Apr 09, 2026
Response after Non-Final Action

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

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Prosecution Projections

7-8
Expected OA Rounds
50%
Grant Probability
82%
With Interview (+31.7%)
4y 3m
Median Time to Grant
High
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