DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to a request for continued examination (“RCE”) filed 31 July 2026, on an application filed 20 March 2024, that is a continuation-in-part of an application filed on 18 July 2022 and issued as U.S. Patent 11,942,215.
Claims 1, 9, 10, 20 and 21 have been amended.
Claims 1-21 are currently pending and have been examined.
The prior art rejection has previously been removed as the claims have been determined to be novel.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 31 July 2026 has been entered.
TERMINAL DISCLAIMER
The Applicant submitted a terminal disclaimer to obviate a double patient rejection over U.S. Patent No. 11,942,215. This terminal disclaimer has been approved, accordingly the double patenting rejection has been removed.
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 1-21 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.
Step 1
Claims 1-21 are within the four statutory categories. Claims 1-9 and 21 are drawn to an intelligent secure networked system for identifying and correcting a defect in a health care service, which is within the four statutory categories (i.e. machine). Claims 10-19 are drawn to a method for identifying and correcting a defect in a healthcare service, which is within the four statutory categories (i.e. process). Claim 20 is drawn to a non-transitory computer-readable storage medium having embodied thereon instructions, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: An intelligent secure networked system for identifying and correcting a defect in a health care service, the system comprising:
a computer processor for processing data;
a storage medium communicatively coupled to the computer processor, the storage medium storing data;
a secure intelligent network communicatively coupled to the computer processor and the storage medium, the secure intelligent network having a deep neural network trained by an evidence engine with evidentiary support including one or more of medical journals, health studies, clinical guidelines, or standards bodies, the deep neural network configured to:
receive a set of data comprising physician-directed health care service data for a previous stress test as coded and unstructured narrative text, and further comprising health care service data as a health care service is being delivered;
adjust for one or more factors having a presence or lacking in claims data, the one or more factors including: undocumented comorbidities, hedging in diagnostic uncertainty, strength of clinical support, ulterior motives and defensive medicine, the presence for each factor of the one or more factors equating to incremental statistical variability that is calculated to a sum, added to a statistical range of better practice, and results in an adjusted range of better practice;
receive a set of metrics associated with appropriateness of a stress test;
apply a weight, bias and threshold, the weight, the bias and the threshold directing an analysis by the deep neural network on the physician-directed health care service data for a stress test;
generate a first output comprising an appropriateness measure for the stress test and a range of better practice, the range of better practice comprising limits of the appropriateness measure, where an appropriateness measures score exceeds an upper limit in a case of overuse of a service that results from operation of the deep neural network on the set of data, or is below a lower limit in a case of underuse of the service;
generate a second output that comprises a rate of inappropriateness of the stress test, the inappropriateness having a numerator representing a number of stress tests with nuclear imaging that occurred within 30 days of an evaluation and management visit to a cardiologist and having a denominator representing stress testing that occurred within 30 days of an evaluation and management visit to a cardiologist, excluding cases with inpatients, outpatients with symptoms of acute coronary syndrome or patients who had a cardiac-related emergency department visit within a thirty-day period;
apply a dynamic feedback communicatively coupling the appropriateness measure, the range of better practice, and the rate of inappropriateness of the stress test for the specific health care service for continuous learning of the deep neural network, the continuous learning comprising adjusting at least the weight of the deep neural network by backpropagation from an output node of the deep neural network toward an input node of the deep neural network;
update the set of metrics according to the dynamic feedback as received by the deep neural network;
retrain the deep neural network by an iterative algorithm with an updated training set that includes false positives generated when the deep neural network classifies data, whereby the positives are minimized;
generate an appropriateness measures score for cardiovascular stress testing; and
generate a cumulative appropriateness practice score to reflect a physician's performance across multiple measures or practice areas.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of “mathematical concepts” (the mathematical indications e.g. numerator and denominator limitations) and/or the abstract idea of a mental process because they recite a process that could be practically performed in the human mind (i.e. in this case the steps directed to generating an appropriateness score of a physician’s use of cardiovascular stress testing based on historical data) or using a pen and paper, but for the recitation of generic computer components (i.e. the structural components of the computer, the training of the deep neural network), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 10 and 20 are identical as the abstract idea for claims 1, because the only difference between claims 1, 10 and 20 is that claim 1 recites a system method, whereas claim 10 recites a method and claim 20 recites a non-transitory computer-readable media.
Dependent claims 2-8, 11-19 and 21 include other limitations, for example claims 2-13, 12-19 and 21 further indicates what data is used and where data comes from, and claims 8, 9, 18 and 19 indicates data outputs, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 2-8, 11-19 and 21 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-8, 11-19 and 21 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 10 and 20.
Prong 2 of Step 2A
Claims 1-21 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the deep neural network, training thereof, and the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 37 and 109 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to medical treatment data, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-8, 11-19 and 21 include other limitations, but these limitations also amount to no more than mere generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-8, 11-19 and 21), and/or do not include any additional elements beyond those already recited in independent claims 1, 10 and 20, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1-21 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, neural network and the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs 37 and 109 of the Specification discloses that the additional elements (i.e. the structural components of the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data ) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Dependent claims 2-8, 11-19 and 21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 10 and 20, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere generally link the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-8, 11-19 and 21), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments filed 31 July 2026 concerning the rejection of all claims under 35 U.S.C. 112 have been fully considered and they are persuasive in view of the amendments to the claims. Accordingly, these rejections have been removed.
Applicant’s arguments filed 31 July 2026 concerning the rejection of all claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 15-22 that the claims comprise statutory material because:
A. “Step 2A, Prong One: The Identified Limitations Do Not Recite an Abstract Idea Within the Enumerated Groupings” as claim one recites no mathematical formula, they are not directed to certain methods of organizing human behavior and the training of the deep neural network cannot be described as a mental process.
B. “Step 2A, Prong Two: Claim 1 as a Whole Integrates Any Recited Exception Into a Practical Application” as the claim reflects an improvement in the functioning of a computer in the recitation of the retraining of the deep neural network.
C. “Step 2B: The Ordered Combination Supplies Significantly More” because the claims recite backpropagation and iterative training.
Regarding A., the Office notes that the claims are not shown as being directed to certain methods of organizing human activity, they are directed to a mathematical process and a mental process. There is math as recited in the present claims, note the summation, the numerator and denominator limitations which are mathematical formulas. Regarding the argument against the mental process abstract idea grouping, the Applicant has listed various additional elements that do not amount to significantly more, as shown above.
Regarding B., MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer. Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. There is no clear nexus between the claims as stated and Applicants stated improvement. Because there is no improvement to the function of the computer, a practical application is not present.
Regarding C., please see the rejection above where the claims are shown to be directed to an abstract idea without significantly more.
Accordingly, the rejection is upheld.
Conclusion
Unused but cited relevant prior art includes:
Stangel (U.S. PG-Pub 2017/0068787 A1), which discloses a medical artificial intelligence system that that guides healthcare toward better health outcomes.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
5 August 2026
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