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 .
Claim Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP20192459.4, filed on 08/24/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/28/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings submitted on 2/17/2023 are accepted.
Specification
The specification submitted on 2/17/2023 is accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim
limitation(s) uses a generic placeholder that is coupled with functional language without
reciting sufficient structure to perform the recited function and the generic placeholder is
not preceded by a structural modifier. Such claim limitation(s) is/are:
“estimation module” in claims 1 and 14
“super learner module” in claims 1 and 14
“selector” in claims 1 and 14
“extension component” in claim 10
“preselector component” in claim 11
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
In cases involving a special purpose computer-implemented means-plus-function
limitation, the Federal Circuit has consistently required that the structure be more than
simply a general-purpose computer or microprocessor and that the specification must
disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc.
V. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239.
Regarding the “estimation module” in claims 1 and 14, the applicant recites in the first paragraph of page 6, "The super learner module is defined by a combination of the plurality of estimation modules. Each estimation module may be a machine of the super learner module. The machines of the super learner module may be initially determined based on an estimation performance of the individual machines with regard to data of a suitable database, such as the previously described BACC database. For example, a set of best performing or ranked machines may be selected for the super learner module. Accordingly, the plurality of estimation modules constituting the super learner module corresponds to the selected best performing or ranked machines. As an example, the plurality of estimation modules can be combined by an optimal convex combination of weights of the plurality of estimation modules. The at least one processor may input the set of vital parameters into the plurality of estimation modules, such that the set of vital parameters is provided to and input into each estimation module of the plurality of estimation modules of the super learner module.” Accordingly, the Examiner interprets the “estimation module” as comprising a single predictive model (or a machine according to the applicant) that receives patient data and outputs an estimated probability of myocardial infarction.
Regarding the “super learner module” in claims 1 and 14, see the aforementioned in the first paragraph of page 6. Accordingly, the Examiner interprets the “super learner module” as a combination of these learners/machines that are selected based on performance rankings to be used in the subsequent step of deriving a more accurate single output probability.
Regarding the “selector” recited in claims 1 and 14, although the specification recites features such as “the selector may select the troponin assay in response to a manual, pre-set or an automated selection of the troponin assay….” in the second paragraph of page 5, it does not provide a sufficient written description of the particular structure of the device. As such, the Examiner interprets “selector” as any device capable of selecting and providing the set of troponin assays.
Regarding the “extension component” in claim 10, although the specification recites features including that “the extension component may be triggered manually via the user interface. Additionally or as an alternative, the extension component may be triggered remotely by a central instance of a medical facility, or automatically upon detection that a new troponin assay is used. The extension component may request information on the further troponin assay, for example, via the user interface. The information may include one or more of a name of the further troponin assay, a name of the manufacturer of the further troponin assay, a troponin marker the further troponin assay may measure, and/or an LOD of the further troponin assay, in any combination” on page 15-16, as to the function of the component, it does not provide a sufficient written description of the particular structure of the device. Therefore, the Examiner interprets the “component” as any device configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository.
Similarly, regarding the “preselector component” in claim 11, although the specification recites as to the function of the component, it does not provide a sufficient written description of the particular structure of the device. Therefore, the Examiner interprets the “component” as any device configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
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.
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 10-11 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. This is a written description rejection.
Regarding claim 10, the claim limitation of “the extension component” lacks sufficient wrriten description. Although the specification provides a description of what each component is configured to do throughout, it does not provide a sufficient written description of the structure for each of the
corresponding components. The specification mentions broadly, for example, “the extension component may be triggered manually via the user interface. Additionally or as an alternative, the extension component may be triggered remotely by a central instance of a medical facility, or automatically upon detection that a new troponin assay is used. The extension component may request information on the further troponin assay, for example, via the user interface. The information may include one or more of a name of the further troponin assay, a name of the manufacturer of the further troponin assay, a troponin marker the further troponin assay may measure, and/or an LOD of the further troponin assay, in any combination. In addition, the computing device further comprising a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data. The preselector component may be triggered when a set of vital parameters is received by the receiver. The preselector component may scan the troponin data of the set of vital parameters. The scanning may include estimating at least one troponin assay from the set of troponin assays that was most likely used for measuring the troponin measurements included in the troponin data of the set of vital parameters. A most likely troponin assay may be a troponin assay with a matching manufacturer. Additionally or as an alternative, the most likely troponin assay may be able to measure the same troponin marker as for the troponin data. After estimating the most likely troponin assay, the selector may select or preselect the most likely used troponin assay. The preselector component may further estimate a matching score to indicate correctness of preselection. The preselection and/or the matching score may be provided via the user interface to the user for confirmation. Subsequently, the selector may either directly or responsive to user input select trigger the repository to provide the plurality of estimation modules associated with the preselected troponin assay. Thus, the preselector component enables an automatic selection of a troponin assay based on the received set of vital parameters. According to a preferred embodiment, the computing device further comprises a user interface, wherein the user interface is configured to receive input of a user and provide at least the estimated probability to the user”. Although Figure 2 shows the components on an interface, there is no mention of whether the module is comprised of a generic computer processor or linked to any particular structure. The lack of sufficient structure description applies to: “extension component” and “preselector component.” Appropriate correction is required.
Of note, when the claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under section 112(a). (See MPEP § 2163.03, subsection VI).
Claims 10-11 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “extension component” and “preselector component” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is unclear whether these modules correspond to particular structures configured to perform the recited functions or merely represent generic computer- implemented functional elements. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 18-20 are non-statutory as they recite "a computer readable medium". The claims as instantly recited read on carrier waves, which are transitory propagating signals and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of
invention (see In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory
computer readable medium" would overcome the rejection with respect to claim 18 reading on signals.
However, the amendment to only "non-transitory computer readable medium" would not overcome
the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without
significantly more (see below).
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claim 1, 14, and 18 recites using the plurality of estimation modules to estimate the probability of myocardial infarction of the subject using the set of vital parameters including the troponin data of the subject.
Claim 3 recites the computing device according to claim 1, wherein the at least one processor is further configured to estimate a prediction interval for the probability of myocardial infarction.
Claim 4 recites the computing device according to claim 3, wherein the prediction interval is estimated by: setting a coverage level; drawing equal-sized folds of datasets in at least a part of a database for cross- validation; forming cross-validated observations with prediction variability for each equal- sized fold; determining a mean and standard deviation of the cross-validated observations; and estimating the prediction interval using the coverage level, and the mean and standard deviation of the created observations.
Claim 5 recites the computing device according to claim 4, wherein forming cross-validated observation comprises, for each equal-sized fold: training each of the plurality of estimation modules using the equal-sized folds without the respective equal-sized fold; making predictions for all data in the respective equal-sized fold; estimating residuals for all data in the respective equal-sized fold using the predictions; and randomly drawing a number of data without replacement from the respective equal- sized fold, to form the cross-validated observations and a prediction error, using the fold-specific predicted observation and the respective residual of the randomly drawn data.
Claim 7 recites the computing device of claim 1, wherein the super learner module corresponds to a 1 measurement model if the troponin data includes one troponin measurement, and wherein the super learner module corresponds to a 2 measurement model or to a 1 measurement model if the troponin data includes at least two troponin measurements.
Claim 8 recites the computing device of claim 7, wherein the 1 measurement model comprises one or more of a gradient boosting machine, a generalized additive machine and a generalized boosted regression machine, and wherein the 2 measurement model comprises one or more of a generalized additive machine, a generalized boosted regression machine and a random forest machine.
Claim 9 recites the computing device of claim 7, wherein one or more super learner modules are fitted by a server, using a set of equal weights.
Claim 19 recites the computer-readable medium of claim 18, further comprising estimating a prediction interval for the probability of myocardial infarction.
The limitations for setting a coverage level; drawing equal-sized folds of datasets in at least a part of a database for cross- validation; forming cross-validated observations with prediction variability for each equal- sized fold can be practically performed in the human mind, randomly drawing a number of data without replacement from the respective equal- sized fold, to form the cross-validated observations and a prediction error, using the fold-specific predicted observation and the respective residual of the randomly drawn data, the super learner module corresponds to a 1 measurement model if the troponin data includes one troponin measurement, and wherein the super learner module corresponds to a 2 measurement model or to a 1 measurement model if the troponin data includes at least two troponin measurements, and therefore falls under the “mental processes” grouping of ideas. Evaluation of coverage, grouping, comparison, selecting and making a decision on what model to use based on measurements can be done both in the human mind and with a pen and paper. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016).
The limitations for training each of the plurality of estimation modules using the equal-sized folds without the respective equal-sized fold; making predictions for all data in the respective equal-sized fold; estimating residuals for all data in the respective equal-sized fold using the predictions; wherein one or more super learner modules are fitted by a server, using a set of equal weights; are verbal equivalents of a mathematical calculation and therefore fall under the “mathematical process” grouping of ideas.
The limitations wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement; the 1 measurement model comprises one or more of a gradient boosting machine, a generalized additive machine and a generalized boosted regression machine, and wherein the 2 measurement model comprises one or more of a generalized additive machine, a generalized boosted regression machine and a random forest machine; merely serve to further limit the judicial exception.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claims 1, 14, and 18 recite a computing device for estimating the probability of myocardial infarction, the computing device comprising at least one processor.
Claim 1 recites a receiver configured to receive a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay; a selector configured to select a troponin assay from a set of troponin assays; a repository configured to provide a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules constitute a super learner module.
Claim 2 recites the computing device according to claim 1, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement.
Claim 6 recites the computing device of claim 5, where residuals are stored in one or more pre-computed tables in the repository.
Claim 10 recites the computing device of claim 1, further comprising an extension component configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository.
Claim 11 recites the computing device of claim 1, further comprising a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject.
Claim 12 recites the computing device of claim 1, wherein the computing device further comprises a user interface, wherein the user interface is configured to receive input of a user and provide at least the estimated probability to the user.
Claim 13 recites the computing device of claim 1, wherein the computing device is a portable computing device, including one of a mobile communication device, a smart device, a smart watch, or a personal digital assistant.
Claim 14 recites a system, comprising: a server; and at least one computing device.
Claim 15 recites the system of claim 14, further comprising: a network, wherein the at least one computing device is connected to the server via the network, wherein the server is configured to provide the plurality of estimation modules constituting the super learner module to the repository of the at least one computing device.
Claim 16 recites the system of claim 14, further comprising a database configured to store datasets related to vital parameters including troponin data.
Claim 17 recites the system of claim 14, wherein the server is further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner modules.
Claim 18 recites a computer-readable memory medium storing instructions that, when executed on a computing device configure the computing device, perform a method for estimating a probability of myocardial infarction.
Claim 20 recites the computer-readable medium of claim 18, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement.
The limitations for a receiver configured to receive a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay; a selector configured to select a troponin assay from a set of troponin assays; a repository configured to provide a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules constitute a super learner module amounts to insignificant extra-solution activity, namely, a data gathering, processing, analyzing, and/or display step. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)). Furthermore, these limitations constitute a field-of-use and/or technological environment limitation and the courts have ruled in Parker V. Flook, 437 U.S. 584, 198 USPQ 193 (1978) that limiting an abstract idea to a field of use or adding post solution components does not make the concept patentable. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The limitations of a computing device for estimating the probability of myocardial infarction, the computing device comprising at least one processor, residuals stored in one or more pre-computed tables in the repository; an extension component configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository; a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject; computing device is a portable computing device, including one of a mobile communication device, a smart device, a smart watch, or a personal digital assistant; a network, wherein the at least one computing device is connected to the server via the network, wherein the server is configured to provide the plurality of estimation modules constituting the super learner module to the repository of the at least one computing device; a database configured to store datasets related to vital parameters including troponin data; a server is further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner modules; and a computer-readable memory medium storing instructions that, when executed on a computing device configure the computing device, perform a method for estimating a probability of myocardial infarction amounts to mere instructions to apply an exception and thus merely invokes computers or other machinery as a tool to perform an existing process (see MPEP 2106.05(f)). The courts have ruled that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit)).
Of note, there are also no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems (e.g. a server, computing device, system, processor, etc.). As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. In addition, the courts have also ruled in Parker V. Flook, 437 U.S. 584, 198 USPQ 193 (1978) that limiting an abstract idea to a field of use or adding post solution components does not make the concept patentable. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The limitations of the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement and a server further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner module merely serve to further limit the judicial exception and does not integrate the judicial exception into a practical application. As such, claims 1-20 do not integrate the judicial exception into a practical application.
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claims 1, 14, and 18 recite a computing device for estimating the probability of myocardial infarction, the computing device comprising at least one processor.
Claim 1 recites a receiver configured to receive a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay; a selector configured to select a troponin assay from a set of troponin assays; a repository configured to provide a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules constitute a super learner module.
Claim 2 recites the computing device according to claim 1, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement.
Claim 6 recites the computing device of claim 5, where residuals are stored in one or more pre-computed tables in the repository.
Claim 10 recites the computing device of claim 1, further comprising an extension component configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository.
Claim 11 recites the computing device of claim 1, further comprising a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject.
Claim 12 recites the computing device of claim 1, wherein the computing device further comprises a user interface, wherein the user interface is configured to receive input of a user and provide at least the estimated probability to the user.
Claim 13 recites the computing device of claim 1, wherein the computing device is a portable computing device, including one of a mobile communication device, a smart device, a smart watch, or a personal digital assistant.
Claim 14 recites a system, comprising: a server; and at least one computing device.
Claim 15 recites the system of claim 14, further comprising: a network, wherein the at least one computing device is connected to the server via the network, wherein the server is configured to provide the plurality of estimation modules constituting the super learner module to the repository of the at least one computing device.
Claim 16 recites the system of claim 14, further comprising a database configured to store datasets related to vital parameters including troponin data.
Claim 17 recites the system of claim 14, wherein the server is further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner modules.
Claim 18 recites a computer-readable memory medium storing instructions that, when executed on a computing device configure the computing device, perform a method for estimating a probability of myocardial infarction.
Claim 20 recites the computer-readable medium of claim 18, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement.
The limitations for a receiver configured to receive a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay; a selector configured to select a troponin assay from a set of troponin assays; a repository configured to provide a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules; a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject; and a computing device further comprises a user interface, wherein the user interface is configured to receive input of a user and provide at least the estimated probability to the user amount to data gathering or selection which are conventional activities. In addition, the limitations of residuals stored in one or more pre-computed tables in the repository; an extension component configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository; a network, wherein the at least one computing device is connected to the server via the network, wherein the server is configured to provide the plurality of estimation modules constituting the super learner module to the repository of the at least one computing device; a database configured to store datasets related to vital parameters including troponin data; server is further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner modules constitutes data storage and transmission over a network. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in 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); and Versata Dev. Group,
Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Furthermore, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems (e.g. a server, computing device, system, processor, etc.). As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
There are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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, 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 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.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claims 1-2, 7-9, 11-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Beshiri (WO2017173353) as filed in the IDS on 7/13/2023 in view of Polley et al. (Super learner. Stat Appl Genet Mol Biol. 2007;6 :Article 25. doi: 10.2202/1544-6115.1309).
Regarding claim 1, Beshiri teaches:
A computing device for estimating the probability of myocardial infarction (laboratory information system is used to generate an estimated risk of acute coronary system; see Fig. 2 on page 11; spreadsheet on page 31 used to determine predictive value related to the diagnosis of type 1 myocardial infarction), the computing device comprising:
a receiver configured to receive a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay (subject values obtained include subject gender values, ECG, hematology parameter, subject age, troponin I concentration, etc. on page 2 lines 1-11; see Fig. 3);
at least one processor configured to use the plurality of estimation modules to estimate the probability of myocardial infarction of the subject using the set of vital parameters including the troponin data of the subject (combined values are processed to estimate risk of myocardial infarction; see page 3 lines 12-22).
Beshiri does not teach a selector configured to select a troponin assay from a set of troponin assays and a repository configured to provide a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules constitutes a super learner module. Polley teaches the selection of an estimator (see “Introduction”; page 23; paragraphs 1-2) and feeding the estimator into the machine learning algorithm of combining many estimators resulting in an improved estimator via a super learner (Super learner algorithm described on page 24-25 where an algorithm takes observed data and outputs a predicted value which is consequently ensembled for further analysis). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Polley’s super learner algorithm into Beshiri’s myocardial infarction estimation pipeline in order to provide an improved estimator (see page 2-3). The combination would have been accomplished with reasonable expectation of success as Beshiri already makes use of machine learning algorithms (i.e. decision tree-based systems) and implements statistical modeling in his estimation pipeline.
Regarding claim 2, Beshiri teaches:
The computing device according to claim 1, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement (see page 8, lines 5-20; specific parameters of troponin data disclosed explicitly).
Regarding claim 7,
Beshiri as modified teaches the claimed invention substantially as stated above.
He does not teach the computing device of claim 1, wherein the super learner module corresponds to a 1 measurement model if the troponin data includes one troponin measurement, and wherein the super learner module corresponds to a 2 measurement model or to a 1 measurement model if the troponin data includes at least two troponin measurements. Polley teaches that estimator selection is not limited to using a single estimator (not limited to a single estimator as seen in page 23 paragraph 2; explicit citation operating on the learning dataset Xi with specific examples throughout pages 26-30). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Beshiri’s myocardial infarction prediction pipeline with Polley’s super learner algorithm in order to account for multiple estimators to provide an improved estimator value. The modification could be accomplished with reasonable expectation of success as Beshiri’s pipeline already includes decision tree machine learning architecture in his pipeline.
Regarding claim 8, Beshiri as modified teaches:
The computing device of claim 7, wherein the 1 measurement model comprises one or more of a gradient boosting machine, a generalized additive machine and a generalized boosted regression machine, and wherein the 2 measurement model comprises one or more of a generalized additive machine, a generalized boosted regression machine and a random forest machine (see “1. Decision Tree Algorithm” wherein the determination of myocardial infarction risk is determined with an additive tree model which includes a boosted decision tree model; page 15 lines 16-28). The Examiner notes that during patent examination, claims are given their broadest reasonable interpretation consistent with the specification. See MPEP 2111. Therefore, the limitation of "A or B" is interpreted to encompass embodiments comprising A, B, or either alternative, and the prior art need only disclose one of the recited alternatives to satisfy the limitation.
Regarding claim 9, Beshiri as modified teaches:
The computing device of claim 7, wherein one or more super learner modules are fitted by a server, using a set of equal weights (see Fig. 2 for fitting; algorithm already explicitly uses weighted estimations in fitting).
Regarding claim 11, Beshiri as modified teaches:
The computing device of claim 1, further comprising a preselector component configured to preselect a troponin assay corresponding to the initial troponin assay based on at least the troponin data of the subject (see page 30 lines 30-35; wherein cardiac troponin concentrations are measured and diagnosis was made with the evidence of a rise and/or fall of a cardiac troponin concentration). The Examiner notes that the fall and rise measurements are based on the same troponin assay.
Regarding claim 12, Beshiri as modified teaches:
The computing device of claim 1, wherein the computing device further comprises a user interface, wherein the user interface is configured to receive input of a user and provide at least the estimated probability to the user (see page 8 lines 24-36 and page 9 lines 1-4; processing system comprises a display component such as a computer monitor, tablet computer screen, etc.).
Regarding claim 13, Beshiri as modified teaches:
The computing device of claim 1, wherein the computing device is a portable computing device, including one of a mobile communication device, a smart device, a smart watch, or a personal digital assistant (see page 8 lines 24-36 and page 9 lines 1-4; processing system comprises a display component such as a computer monitor, tablet computer screen, etc.).
Regarding claim 14, Beshiri as modified teaches the claimed invention substantially as stated above (see rejection on Claim 1 and LIS (laboratory information system) disclosed in Fig. 3 workflow).
Regarding claim 16, Beshiri as modified teaches:
The system of claim 14, further comprising a database configured to store datasets related to vital parameters including troponin data (see page 4; lines 16-36 wherein the processing system with databases are described related to a variety of values such as the cTnT concentration among others). The Examiner notes that although the datasets related to a decision tree architecture, nevertheless, the database is still related to the vital parameters (i.e. troponin data).
Regarding claim 17, Beshiri as modified teaches:
The system of claim 14, wherein the server is further configured to store estimated residuals in one or more pre-computed tables for one or more machines and/or super learner modules (see page 4; lines 16-36 for database with related tables and LIS (laboratory information system) disclosed in Fig. 3 workflow). The Examiner notes that laboratory information systems are designed to store data and analyze patterns and relationships among them as evidenced by Pantanowitz et al. (Large integrated databases (repositories and warehouses) can be analyzed (data mining) to identify patterns or relationships [20] explicitly stated in “Databases”). Accordingly, servers necessarily involve databases and storage. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual
operation, would necessarily perform the method claimed, then the method claimed will be
considered to be anticipated by the prior art device. Additionally, where the Patent Office has
reason to believe that a functional limitation asserted to be critical for establishing novelty in the
claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses
the authority to require the applicant to prove that the subject matter shown to be in the prior art
does not possess the characteristic relied on (see MPEP § 2112).
Regarding claim 18, Beshiri as modified teaches:
A computer-readable memory medium storing instructions that, when executed on a computing device configure the computing device, perform a method for estimating a probability of myocardial infarction comprising: receiving a set of vital parameters of a subject, the set of vital parameters including troponin data of the subject, the troponin data of the subject being measured using an initial troponin assay; selecting a troponin assay from a set of troponin assays; providing a plurality of estimation modules corresponding to the selected troponin assay, wherein the plurality of estimation modules constitute a super learner module; and using the plurality of estimation modules, estimating the probability of myocardial infarction of the subject using the set of vital parameters including the troponin data of the subject (see rejection on claim 1). The Examiner notes that Beshiri explicitly teaches all the limitations above in addition to the non-transitory computer readable medium component (method explicitly involves a computer processor, and ii) non- transitory computer memory (e.g., single memory component or multiple, distributed memory components; comprising one or more computer programs and a database (see page 2 lines 24-26)).
Regarding claim 20, Beshiri as modified teaches:
The computer-readable memory medium of claim 18, wherein the troponin data includes at least one troponin measurement of a troponin marker, the troponin measurement including a measurement value of the troponin marker and a timestamp of the troponin measurement (see page 8, lines 5-20; specific parameters of troponin data disclosed explicitly).
Claim(s) 3-4 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beshiri (WO2017173353) as filed in the IDS on 7/13/2023, as applied in claim 1, in view of Polley et al. (Super learner. Stat Appl Genet Mol Biol. 2007;6 :Article 25. doi: 10.2202/1544-6115.1309) further in view of Cai et al. (Predicting future responses based on possibly mis-specified working models, Biometrika, Volume 95, Issue 1, March 2008, Pages 75–92).
Regarding claim 3,
Beshiri and Polley teach the claimed invention substantially as claimed above.
Beshiri as modified does not teach the computing device claim 1 wherein the at least one processor is further configured to estimate a prediction interval for the probability of myocardial infarction. Cai teaches that prediction interval procedures that are used for predicting future responses repeatedly for various distinct sets of covariates is common in the art and have been applied through various disclosures (prediction intervals and regions known and implemented in disclosure, pages 75-76). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Beshiri’s myocardial infarction prediction pipeline with Polley’s super learner algorithm and Cai’s stastitical analysis in order to predict future responses based on vectors of observable covariates. This combination would have yielded a reasonable expectation of success as Cai is a continuation of prediction intervals as a component of predictive modeling in the same field of endeavor.
Regarding claim 4,
Beshiri as modified teaches the claimed invention substantially as claimed above.
Beshiri as modified does not teach the computing device claim 3, wherein the prediction interval is estimated by: setting a coverage level; drawing equal-sized folds of datasets in at least a part of a database for cross- validation; forming cross-validated observations with prediction variability for each equal- sized fold; determining a mean and standard deviation of the cross-validated observations; and estimating the prediction interval using the coverage level, and the mean and standard deviation of the created observations. Polley teaches drawing equal-sized folds of datasets (V-fold cross-validation random partitioning found on page 4 paragraph 1) in at least a part of a database for cross-validation and forming cross-validated observations with prediction variability for each equal- sized fold (see equation 1.5 on page 3 for evaluation of predictions on held out observations; also see description of validation-fold predictions on page 4 paragraph 1). Polley does not teach setting a coverage level, determining a mean and standard deviation of the cross-validated observations; and estimating the prediction interval using the coverage level, and the mean and standard deviation of the created observations. Cai teaches setting a coverage level (known in the art as described on page 75-76 in “Summary” and “Introduction”), determining a mean and standard deviation of the cross-validated observations (page 83 in paragraph 1; explicitly recited that cross-validation is a valid way to reduce bias to obtain better prediction sets and is a common approach to apply onto coverage data; mean and standard deviation computed for distributions) and estimating the prediction interval using the coverage level (recited in the entire computational pipeline on page 83), and the mean and standard deviation of the created observations (subsequent computation of mean and standard deviation of observations found on page 83). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Beshiri’s myocardial infarction prediction pipeline with Polley’s super learner algorithm and Cai’s stastitical analysis in order to reduce bias to obtain better prediction sets as recited in Cai (page 83 in paragraph 1) and address shortcomings in currently understood cross-validation methods as recited in Polley (page 3 in paragraph 1-2). This combination would have yielded a reasonable expectation of success as all disclosures operate in the field of statistical analysis with covariate data.
Regarding claim 19, Cai teaches:
The computer-readable memory medium of claim 18, further comprising estimating a prediction interval for the probability of myocardial infarction (prediction intervals and regions known and implemented in disclosure, pages 75-76).
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Beshiri (WO2017173353) as filed in the IDS on 7/13/2023, as applied to claim 14, in view of Polley et al. (Super learner. Stat Appl Genet Mol Biol. 2007;6 :Article 25. doi: 10.2202/1544-6115.1309) further in view of Pantanowitz et al. (Medical Laboratory Informatics, Clinics in Laboratory Medicine, Volume 27, Issue 4, 2007, Pages 823-843)
Regarding claim 15, Beshiri as modified teaches the claimed invention substantially as stated above (see rejection on Claim 1 and LIS (laboratory information system) disclosed in Fig. 3 workflow). Furthermore, it is known in the art that information is transmitted across networks in the architecture of Laboratory Information Systems as evidenced by Pantanowitz et al. (see “Networks” and “Electronic Medical Record” in attached document).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beshiri (WO2017173353) filed in the IDS on 7/13/2023, as applied in claim 1, in view of Polley et al. (Super learner. Stat Appl Genet Mol Biol. 2007;6 :Article 25. doi: 10.2202/1544-6115.1309), further in view of Mazzara et al. (CombiROC: an interactive web tool for selecting accurate marker combinations of omics data. Sci Rep 7, 45477 (2017)).
Regarding claim 10,
Beshiri and Polley teaches the claimed invention substantially as claimed above.
Beshiri as modified does not teach the computing device of claim 1, further comprising an extension component configured to add a further troponin assay to the set of troponin assays, wherein a plurality of further estimation modules corresponding to the further troponin assay is stored in the repository. Mazzara discloses CombiROC states that existing literature provides various statistical modeling strategies to combine biomarkers among them including threshold-based, logistic regression, and tree-based methods. (see ”Introduction”; troponin data is a biomarker dataset which is shown to be used for modeling pipelines; page 1 paragraph 1). Therefore, it would have been obvious to one of ordinary skill in the art to integrate Mazzara’s laboratory informatics tool into Beshiri as modified’s myocardial infarction prediction pipeline in order to improve diagnostic accuracy considerably by including multiple markers (see “Abstract”; page 1). This could be accomplished with reasonable expectation of success as both inventions operate in the same field of endeavor with statistical analysis performed with software disclosed throughout.
Claim(s) 5-6 are free from prior art.
Regarding claim 5,
Beshiri as modified teaches the claimed invention substantially as claimed above.
Polley teaches the computing device according to claim 4, wherein forming cross-validated observation comprises, for each equal-sized fold: training each of the plurality of estimation modules using the equal-sized folds without the respective equal-sized fold (see page 4 in “1.1.2 V-fold cross validation” wherein for each v split, the validation split is removed and the estimator is fit on the remaining data); making predictions for all data in the respective equal-sized fold (performance is assessed on validation data using the fit of the estimator which necessarily requires predictions on the held-out fold on page 4 in “1.1.2 V-fold cross validation”); estimating residuals for all data in the respective equal-sized fold using the predictions (see pages 2-3; equations 1.1-1.3 for the regression function which contains the residual term with the squared residual being the loss function, therefore the residuals for all data is required to be known in the disclosure). The Examiner notes that under the broadest reasonable interpretation, a cross-validated observation consists of a prediction and its associated residual.
However, both Polley and Beshiri are silent as to the features of randomly drawing a number of data without replacement from the respective equal- sized fold, the random drawing data step to form the cross-validated observations and a prediction error, using the fold-specific predicted observation and the respective residual of the randomly drawn data. As such, the limitations of claim 5 are free from prior art.
Regarding claim 6, Polley teaches:
The computing device of claim 5, where residuals are stored in one or more pre-computed tables in the repository. (see page 4; lines 16-36 for database with related tables and LIS (laboratory information system) disclosed in Fig. 3 workflow). The Examiner notes that laboratory information systems are designed to store data and analyze patterns and relationships among them as evidenced by Pantanowitz et al. (Large integrated databases (repositories and warehouses) can be analyzed (data mining) to identify patterns or relationships [20] explicitly stated in “Databases”). Accordingly, servers necessarily involve databases and storage. It is elementary that the mere recitation of a newly discovered function or property, inherently possessed by things in the prior art, does not cause a claim drawn to distinguish of the prior art. Under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be
considered to be anticipated by the prior art device. Additionally, where the Patent Office has
reason to believe that a functional limitation asserted to be critical for establishing novelty in the
claimed subject matter may, in fact, be an inherent characteristic of the prior art, it possesses
the authority to require the applicant to prove that the subject matter shown to be in the prior art
does not possess the characteristic relied on (see MPEP § 2112).
However, as claim 6 is dependent on claim 5, and the limitations of claim 5 are free from prior art, claim 6 is also free from prior art (see notes on claim 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Linden et al. (Combining High-Sensitivity Cardiac Troponin I and Cardiac Troponin T in the Early Diagnosis of Acute Myocardial Infarction. Circulation, 138(10), 989–999) discloses cardiac troponin I and cardiac troponin T for early diagnosis of acute myocardial infarction.
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights