Prosecution Insights
Last updated: April 19, 2026
Application No. 17/884,768

USING RESILIENT SYSTEMS INFERENCE FOR ESTIMATING HOSPITAL ACQUIRED INFECTION PREVENTION INFRASTRUCTURE PERFORMANCE

Non-Final OA §101§112
Filed
Aug 10, 2022
Examiner
SHELDEN, BION A
Art Unit
3685
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
The Research Foundation for the State University of New York
OA Round
5 (Non-Final)
22%
Grant Probability
At Risk
5-6
OA Rounds
4y 2m
To Grant
42%
With Interview

Examiner Intelligence

Grants only 22% of cases
22%
Career Allow Rate
69 granted / 311 resolved
-29.8% vs TC avg
Strong +20% interview lift
Without
With
+19.7%
Interview Lift
resolved cases with interview
Typical timeline
4y 2m
Avg Prosecution
50 currently pending
Career history
361
Total Applications
across all art units

Statute-Specific Performance

§101
32.9%
-7.1% vs TC avg
§103
32.9%
-7.1% vs TC avg
§102
7.3%
-32.7% vs TC avg
§112
22.4%
-17.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 311 resolved cases

Office Action

§101 §112
DETAILED ACTION Status of Claims This is a non-final office action on the merits in response to the arguments and/or amendments filed on 23 October 2025 and the request for continued examination filed on 23 October 2025. Claim(s) 7-9 is/are canceled. Claim(s) 1-4 and 10-13 is/are amended. Claim(s) 21-24 is/are new. Claims 14-20 were previously withdrawn. Claim(s) 1-6, 10-13, and 21-23 is/are currently pending and have been examined. 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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 23 October 2025 has been entered. Claim Objections Claim(s) 1 is/are objected to because of the following informalities: Claim 1 recites can specify a resilience level for each of the hospital plurality of acquired infection impact reduction strategies, which should recite can specify a resilience level for each of the hospital acquired infection impact reduction strategies. This issue occurs 1 additional time in claim 1. Claim 1 recites a potential hospital employing a respective one of plurality of specific hospital acquired, which should recite a potential hospital employing a respective one of the plurality of specific hospital acquired. This issue occurs 1 additional time in claim 1. Claim 1 recites assessing, by the by one or more computers of the machine learning system, supervised, which should recite assessing, by the [by] one or more computers of the machine learning system, supervised. This issue occurs 6 additional times in claim 1. Appropriate correction is required. Claim Rejections - 35 USC § 112(a) The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1-6, 10-13, and 21-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims not listed below are rejected based on dependency. Amended claim 1 recites the non-original limitation training, by one or more computers of a machine learning system, a predictive model with evidence-based case literature, wherein the trained predictive model is configured to generate resilience fuzzy membership levels. The identified limitation is part of the amendments filed 23 October 2025 and Applicant’s associated remarks do not identify support for the identified limitation. Thus applicant has not pointed out where the new limitation is supported. Further, there does not appear to be written description support for the identified limitation in the application as originally filed. See MPEP 2163(I)(A). Therefore the claim is rejected based on the written description requirement. Amended claim 1 recites the non-original limitation generate first fuzzy set membership rules that can specify a resilience level for each of the hospital plurality of acquired infection impact reduction strategies based on regional, environmental, and demographic factors associated with the potential hospital employing a respective one of plurality of specific hospital acquired infection impact reduction strategies. The identified limitation is part of the amendments filed 23 October 2025 and Applicant’s associated remarks do not identify support for the identified limitation. Thus applicant has not pointed out where the new limitation is supported. Further, there does not appear to be written description support for the identified limitation in the application as originally filed. See MPEP 2163(I)(A). Therefore the claim is rejected based on the written description requirement. The claim is similarly rejected based on the limitation generate first fuzzy set membership rules that can specify a resilience level for each of the hospital plurality of acquired infection impact reduction strategies based on regional, environmental, and demographic factors associated with the potential hospital employing a respective one of plurality of specific hospital acquired infection impact reduction strategies. Amended claim 1 recites the non-original limitation inferring, by the one or more computers of the machine learning system, a resilience performance of the specific hospital in hospital infection risk factor prevention of each of the plurality of hospital acquired infection impact reduction strategies at the specific hospital using a fuzzy inference system employing the first fuzzy membership set rules. The identified limitation is part of the amendments filed 23 October 2025 and Applicant’s associated remarks do not identify support for the identified limitation. Thus applicant has not pointed out where the new limitation is supported. Further, there does not appear to be written description support for the identified limitation in the application as originally filed. See MPEP 2163(I)(A). Therefore the claim is rejected based on the written description requirement. The claim is similarly rejected based on the limitation inferring, by the one or more computers of the machine learning system, a risk prevention performance of the specific hospital in hospital infection risk factor prevention of each of the plurality of hospital acquired infection impact reduction strategies at the specific hospital using a fuzzy inference system employing the second fuzzy membership set rules. Amended claim 1 recites the non-original limitation outputting, by the by one or more computers of the machine learning system, a ranking of performance safety outcomes for the plurality of hospital acquired infection impact reduction strategies employed by the specific hospital based on their inferred resilience performances and inferred risk prevention performances. The identified limitation is part of the amendments filed 23 October 2025 and Applicant’s associated remarks do not identify support for the identified limitation. Thus applicant has not pointed out where the new limitation is supported. Further, there does not appear to be written description support for the identified limitation in the application as originally filed. See MPEP 2163(I)(A). Therefore the claim is rejected based on the written description requirement. New claim 21 recites the non-original limitation altering a hospital strategy for managing a risk of hospital acquired infections based on the inferred resilience performances and inferred risk prevention performances. The identified limitation is part of the amendments filed 23 October 2025 and Applicant’s associated remarks do not identify support for the identified limitation. Thus applicant has not pointed out where the new limitation is supported. Further, there does not appear to be written description support for the identified limitation in the application as originally filed. See MPEP 2163(I)(A). Therefore the claim is rejected based on the written description requirement. Claims 22 is similarly rejected for the limitation modifying the first fuzzy set membership rules in dependence on inferred performance of the resilience performance. Claim 23 is similarly rejected for the limitation modifying the second fuzzy set membership rules in dependence on inferred performance of the risk prevention performance of the specific hospital in hospital infection risk factor prevention. 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. Claim(s) 1-6, 10-13, and 21-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites: a method for assessing hospital acquired infection reduction strategies to prevent risks of hospital acquired infections, comprising: a predictive model with evidence-based case literature, wherein the using, using, for each of the plurality of hospital acquired infection impact reduction strategies, assessing, strategies based on a first set of variables, wherein the first set of variables comprise regional, environmental, and demographic factors associated with the specific hospital; for each of the plurality of hospital acquired infection reduction strategies, assessing, inferring, inferring, outputting, The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of using fuzzy analyses to evaluate a hospital acquired infection performance. This concept describes a mental process that a healthcare data analyst should follow to evaluate hospital acquired infection performance similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such the claims are determined to set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea. MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application. Claim 1 recites the additional element of one or more computers of a machine learning system. This additional element is recited at an extremely high level of generality, and is interpreted as a generic computing device used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not integrate the abstract idea into a practical application. Claim 1 further recites the additional element of training a model. This additional element amounts to instructions to implement the abstract idea with a computing device. As such, this additional element does not integrate the abstract idea into a practical application. There are no further additional elements. When considered as a combination, the additional elements only amount to instructions to implement the abstract idea with a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea. At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea. As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea or instructions to implement the abstract idea with a generic computing device. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements does not amount to significantly more. Further, as the combination of additional elements only amount to instructions to implement the abstract idea with a computing device, the combination of additional elements similarly does not amount to significantly more. There are no further additional elements. Therefore, when considered individually and as a combination, the additional elements of the independent claim do not amount to significantly more than the abstract idea. Thus the independent claim is not patent eligible. Dependent claims 2-6, 10-13, and 21-23 further describe and narrow the method of organizing human activity, but these claims continue to set forth a method of organizing human activity and recite an abstract idea. Dependent claims 2-6, 10-13, and 21-23 recite no further additional elements. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for the same reasons as indicated above. As such, these claims continue to be directed to an abstract idea. Further, the previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for the same reasons as indicated above. Thus as the dependent claims remain directed to an abstract idea, and as the additional elements of the dependent claims do not amount to significantly more, the dependent claims are not patent eligible. Response to Arguments Applicant’s Argument Regarding 112(a) Rejections of claims 1-13: Applicant has amended the claims to remove the objected language. As such, Applicant respectfully submits that the recited claim language is supported by Applicant’s disclosure and requests withdrawal of the rejection. Examiner’s Response: Applicant's arguments filed 23 October 2025 have been fully considered but they are moot in view of the extensive amendments filed 23 October 2025. The rejection has been updated in view of the amendments. Applicant’s Argument Regarding 101 Rejections of claims 1-6 and 10-13: Systems and methods of the present disclosure address the problem about “understanding how the different components of the system interact with one another to achieve specific goals.” … To address this problem, the specification presents the technical solution of “evaluating system performance safety” with “Supervised Learning techniques such as OLS [which] offer greater specificity to the weighting of different system risk and resilience fuzzy membership levels. … ” See page 11, Lines 16-23. As stated on page 52 of the present application, [0213 and 0214 of pre-grant publication] Therefore, Applicant submits that the present specification shows a teaching on how the present invention improves computer modeling technology and that there is a nexus between the present claim language and the improvement to technology. For example, claim 1 presently recites: [entirety of claim body]. Applicant respectfully submits that the foregoing clearly describes a concrete embodiment of an improved solution that trains and initiates a predictive computer model and infers, using the trained predictive model executed on a computing device, resilience performance and risk prevention performances of a specific hospital for one or more hospital acquired infections, among other features. The foregoing is similar to the rationale expressed by the Director of the USPTO in its Decision on Request for Rehearing for the Ex Parte Desjardins matter. Examiner’s Response: Applicant's arguments filed 23 October 2025 have been fully considered but they are not persuasive. First, examiner notes that the claim does not appear to address a problem of “understanding how the different components of the system interact with one another to achieve specific goals.” Second, examiner notes that per MPEP 2106.05(a), “a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” Here, the disclosure does not appear to provide a technical explanation for implementing the asserted solution. Instead, the disclosure appears to describe the generalized application of existing techniques, without describing either the existing techniques or their application in a detailed manner that would suggest the presence of a technical improvement. Applicant’s argument by paragraph is not clear to the examiner. The identified disclosures do not appear to indicate that the claims constitute an improvement to computer modeling technology nor do they appear to indicate that the claims reflect such an improvement. Examiner notes that the claim rejection is based on the entirety of the claim, and that therefore Applicant’s mere repetition of the same claim language itself does not convey the eligibility of the claim. Examiner notes that the claims do not “us[e] the trained predictive model”, as the current claim does not reference the “predictive model” subsequent to its training. The Ex Parte Desjardins decision notes: “Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. … We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.” Here, there is no such analogous improvement to the training of the machine learning model itself. As such, the rationale articulated in Ex Parte Desjardins is neither similar to the argument advanced by applicant nor applicable to the instant claims. Additional Considerations The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited. Chen (US 2003/0225651 A1) is noted for describing a fuzzy logic inference system similar to the techniques articulated by applicant. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST. 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, Kambiz Abdi can be reached at (571) 272-6702. 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. /Bion A Shelden/Primary Examiner, Art Unit 3685 2026-01-31
Read full office action

Prosecution Timeline

Aug 10, 2022
Application Filed
Aug 10, 2024
Non-Final Rejection — §101, §112
Nov 15, 2024
Response Filed
Nov 25, 2024
Final Rejection — §101, §112
Jan 28, 2025
Response after Non-Final Action
Feb 24, 2025
Request for Continued Examination
Feb 28, 2025
Response after Non-Final Action
Apr 05, 2025
Non-Final Rejection — §101, §112
Jul 10, 2025
Response Filed
Jul 22, 2025
Final Rejection — §101, §112
Sep 15, 2025
Interview Requested
Sep 23, 2025
Response after Non-Final Action
Oct 07, 2025
Applicant Interview (Telephonic)
Oct 07, 2025
Examiner Interview Summary
Oct 23, 2025
Request for Continued Examination
Oct 31, 2025
Response after Non-Final Action
Jan 31, 2026
Non-Final Rejection — §101, §112 (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

5-6
Expected OA Rounds
22%
Grant Probability
42%
With Interview (+19.7%)
4y 2m
Median Time to Grant
High
PTA Risk
Based on 311 resolved cases by this examiner. Grant probability derived from career allow rate.

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