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 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 1-21 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites language that is stricken, although this is an unamended claimset. Accordingly, it is unknown whether the included language is part of the claimset and therefore the metes and bounds of the claim are unclear.
Claims 1, 11 and 22 recite material related to deep neural network configured to: have a weight, bias and threshold…. It is unclear what this limitation refers to, the entire network has a weight, bias and threshold. The metes and bounds of the claim are unknown.
Claim 1 refers to the neural network in the generate limitation. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination this limitation is interpreted to read the deep neural network.
Claim 10 refers to the neural network in the training and generating limitations. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination these limitations are interpreted to read the deep neural network.
To the extent that other claims rely on claims that are rejected under 35 USC 112 and fail to correct the deficiencies of the claims they rely on, those other claims are rejected for the same reasons as the claims they rely on. Appropriate correction is required.
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,
the deep neural network trained by an evidence engine with evidentiary support including 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 health care service data as a health care service is being delivered, and configured to adjust for a factor lacking in claims data including undocumented comorbidities, hedging in diagnostic uncertainty, strength of clinical support, ulterior motives, defensive medicine, a presence for each factor 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;
have a weight, bias and threshold directing an analysis by the deep neural network on physician-directed health care service data for a stress test;
to generate a first output comprising a knowledge narrative representing a plain-text description of an appropriateness measure for the stress test and a 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, or is below a lower limit in a case of underuse of a service that results from operation of the neural network on the input elements;
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;
have a dynamic feedback communicatively coupling the knowledge narrative and range of better practice node and the rate of inappropriateness of the stress test for the specific health care service for continuous learning of the deep neural network;
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” and/or the abstract idea of a mental process and/or a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the steps directed to generating an appropriateness score of a physician’s use of cardiovascular stress testing based on historical data), 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 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-21 are rejected under 35 U.S.C. 103 as being obvious over Lieberman (U.S. PG-Pub 2020/0075164 A1), hereinafter Lieberman, in view of Siqin et al. (“Can Physicians Identify Inappropriate Nuclear Stress Tests?”, Circulation: Cardiovascular Quality and Outcomes Volume 8, Issue 1, January 2015; Pages 23-29), hereinafter Circulation, further in view of Blanchard et al. (U.S. PG-Pub 2019/0371472 A1), hereinafter Blanchard.
As per claims 1, 11 and 22, Lieberman discloses a method and a non-transitory computer-readable storage medium having embodied thereon instructions and An intelligent secure networked system for identifying and correcting a defect in a health care service, the system comprising (Lieberman, Figs. 2-3 and 10.):
a computer processor for processing data (Lieberman, Figs. 2-3 and 10.);
a storage medium communicatively coupled to the computer processor, the storage medium storing data (Lieberman, Figs. 2-3, 6-8 and 10.);
a secure intelligent network communicatively coupled to the computer processor and the storage medium, the secure intelligent network having a deep neural network (Lieberman, Figs. 2-3, 6-8 and 10.),
the deep neural network trained by an evidence engine with evidentiary support including medical journals, health studies, clinical guidelines, or standards bodies (See the training of the neural networks using subject matter expert panels, at least, at Lieberman, Fig. 3A.), 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 health care service data as a health care service is being delivered (Various data sources are provided as an input to the system, including unstructured text Office notes #117 and “any source of medical data now known or later developed”, which would include physician-directed health care service data in unstructured narrative text, see paragraphs 24 and 26, and Fig. 2. Note paragraph 25 indicates that data sources include stress tests.), …
receive a set of metrics associated with appropriateness of a stress test (Various data sources are provided as an input to the system, including unstructured text Office notes #117 and “any source of medical data now known or later developed”, which would include physician-directed health care service data in unstructured narrative text, see paragraphs 24 and 26, and Fig. 2. Note paragraph 25 indicates that data sources include stress tests.);
have a weight … directing an analysis by the deep neural network on physician-directed health care service data for a stress test (Lieberman discloses input of stress test data, paragraph 25, and a neural network that weights intermediary nodes, see paragraphs 46 and 49-51. See also intermediary nodes of Figs. 6 and 7.);
to generate a first output comprising a knowledge narrative representing a plain-text description of an appropriateness measure for the stress test (Lieberman, Fig. 2 #250 provides a series of outputs that indicate the appropriateness of a health care measure, including “patient satisfaction level (expressed numerically on a suitable scale), health complications (expressed as categorical data), cost per episode (in monetary units), functional status (e.g., the extent to which the patient has recovered to full functionality), and the need for ongoing additional treatments”, see paragraph 32. Lieberman discloses a second output that determines whether a particular medical intervention is indicated given the expected outcomes, see paragraph 33. Lieberman discloses a plurality of outputs, including a recommended physician or practice for a give treatment, see paragraphs 60-61 and 67. It is old and well known in the medical arts to provide an indication of a particular physician in plain text.);
and a range of better practice corresponding to the appropriateness measure … (Lieberman discloses outputting a classification indicating the probability of whether a medical intervention is appropriate, but does not output limits, see paragraphs 23, 32, 51 and 52.),
… have a dynamic feedback communicatively coupling the knowledge narrative and range of better practice node and the rate of inappropriateness of the stress test for the specific health care service for continuous learning of the deep neural network (Output outcomes #250 or fed back to the system, see paragraph 33.);
generate an appropriateness measures score for a medical intervention including cardiovascular stress testing (Lieberman, Fig. 2 #250 provides a series of outputs that indicate the appropriateness of a health care measure, including “patient satisfaction level (expressed numerically on a suitable scale), health complications (expressed as categorical data), cost per episode (in monetary units), functional status (e.g., the extent to which the patient has recovered to full functionality), and the need for ongoing additional treatments”, see paragraph 32. Lieberman discloses a second output that determines whether a particular medical intervention is indicated given the expected outcomes, see paragraph 33. Lieberman discloses a plurality of outputs, including a recommended physician or practice for a give treatment, see paragraphs 60-61 and 67. Interventions include cardiac stress tests, see paragraph 25.).
Lieberman fails to explicitly disclose:
configured to adjust for a factor lacking in data, a presence for each factor 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;
upper and lower limits of appropriateness corresponding to correct utilization,
considering bias and threshold of data,
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,
However, Circulation teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention:
to adjust for a factor lacking in data, a presence for each factor 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 (See Circulation, page 26.);
consider upper and lower limits of appropriateness corresponding to correct utilization (Circulation, Table 1.),
consider bias and threshold of data (Circulation, note inter-rate reliability of using Cohen K statistics with confidence interval of Table 1.),
to provide calculation of stress test inappropriateness in comparison to all stress tests for a given entity within better practice limits and the consideration of inclusion and exclusion parameters (See Circulation, pages 25-26), and
generate a cumulative appropriateness practice score to reflect a physician's performance across multiple measures or practice areas (Circulation, Tables 1 and 3.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for assessing medical interventions of Lieberman to consider thresholding and bias of data, consider upper and lower limits of appropriateness corresponding to correct utilization, calculation of stress test inappropriateness in comparison to all stress tests for a given within better practice limits and the consideration of inclusion and exclusion parameters and generation of a cumulative appropriateness practice score, as disclosed by Circulation, in order to arrive at a method for assessing medical interventions that considers presents an appropriateness score of a physician or entity.
Lieberman/Circulation fail to disclose adjusting for a lack of claims data including undocumented comorbidities, hedging in diagnostic uncertainty, strength of clinical support, ulterior motives, defensive medicine.
Blanchard teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to adjust for a lack of claims data including undocumented comorbidities, hedging in diagnostic uncertainty, strength of clinical support, ulterior motives, defensive medicine in order to provide a “financial incentive related to the overall health outcome of the patient” (Blanchard, paragraph 3.)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for assessing medical interventions of Lieberman/Circulation to consider adjusting for lacking claims data, as disclosed by Blanchard, in order to arrive at a method for assessing medical interventions that provides a “financial incentive related to the overall health outcome of the patient” (Blanchard, paragraph 3.)
Lieberman, Circulation and Blanchard are all directed to the processing of health care data and specifically to an examination of medical practice information. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 2-5, 9, 11-15, 19 and 21, Lieberman/Circulation/Blanchard disclose claims 1 and 11, discussed above. Lieberman/Circulation/Blanchard also discloses:
2,11. the physician-directed healthcare service data that includes data received from a physician-submitted insurance claim (Lieberman discloses input of “any source of medical data now known or later developed”, see paragraph 26.);
3,12. the physician-directed healthcare service data that includes a diagnosis or a treatment plan (Lieberman discloses input of “any source of medical data now known or later developed”, see paragraph 26.);
4,14. a set of metrics including data regarding a medical standard of care (Lieberman discloses using metrics regarding medical information, see Figs. 3A-3B and paragraphs 36-37. The appropriateness considerations of Circulation would also comprise standards of care.);
5,15. the set of metrics including data regarding a cost of a medical service (Lieberman discloses using metrics regarding medical information, see Figs. 3A-3B and paragraphs 36-37, as well as processing using costs per episode and training use “cost functions”, see paragraphs 32 and 50.);
9,19. the second output node generating an output that comprises a plain text recommended action for a specific patient (Lieberman presents a ranked list of appropriateness and costs for similar medical services for a given diagnosis which would inherently comprise a patient-specific recommended action, see paragraphs 20, 43 and 56. Lieberman also discloses outputting a particular surgeon or facility to have a procedure performed at, see paragraph 61.);
13. input for the set of metrics that is received from published guidelines, medical journals, standards organizations, or expert opinion (Lieberman, Figs. 3A-3B.); and
21. the evidence engine configured with: textual action recommendations from practice guidelines; unstructured data in narrative text; textual terms and concepts linked with Boolean operators; semi-structured data; organized text; structured concepts coded for computer interpretation; and coded information, machine executable, interpretable by clinical decision support systems (Lieberman, Fig. 3A discloses training of neural networks using a data configured evidence engine.).
Claims 6-8 and 17-19 are rejected under 35 U.S.C. 103 as being obvious over Lieberman/Circulation/Blanchard, further in view of Bayyana et al. (U.S. PG-Pub 2021/0265051 A1), hereinafter Bayyana.
As per claims 2-5, 9, 10, 12-16, 20 and 21, Lieberman/Circulation/Blanchard disclose claims 1 and 11, discussed above. Lieberman/Circulation/Blanchard also discloses receiving metrics, as shown above. Lieberman fails to explicitly disclose:
6-8, 17-19. data received from a commercial/customer/Medicare claim.
Bayyana teaches that it was old and well known in the art of healthcare communications at the time of the invention/filing to provide data received from a commercial/customer/Medicare claim (Bayyana discloses developing metrics based on historical claims data, see paragraphs 29, 73, 74 and 77. It is the Office’s position that the particular type of claim data is received from amounts to mere design choice as the invention operates the same regardless of the description of the data.) in order to use historical data to make present predictions for patient care.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications at the time of the invention/filing to modify the method for assessing medical interventions of Lieberman/Circulation/Blanchard to include data received from a commercial/customer/Medicare claim, as disclosed by Bayyana, in order to arrive at a method for assessing medical interventions that analyzes historical data to make present predictions for patient care. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Conclusion
The following uncited references are also relevant to the present claimset:
Mantz (U.S. PG-Pub 2014/0100863 A1) discloses a medical benefits determination system.
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
11 December 2025