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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/09/2026 has been entered.
Status of Claims
This action is in response to the RCE filed 04/09/2026.
Claims 1, 9, 17 were amended on 04/09/2026.
Claims 1-20 are currently pending and have been examined.
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 1-20 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.
Claims 1-20 are drawn to a method, a system and a non-transitory computer readable medium which are statutory categories of invention (Step 1: YES).
Independent claims 1, 9, and 17 recite: processing electronic images to quantify coronary microvascular disease, receiving, imaging data of one or more captured electronic images of subject vasculature, wherein a first set of the imaging data was captured prior to an administration of one or more pharmacological agents to an imaged subject, and wherein a second set of the imaging data was captured subsequent to the administration of the one or more pharmacological agents to the imaged subject; providing, the imaging data including the first set of the imaging data and the second set of the imaging data, and a set of patient data, using one or more gathered and/or simulated sets of imaging data and patient data that corresponds to latent variables, to identify coronary microvascular disease (CMD) features within the captured imaging data and the set of patient data; outputting, one or more CMD measures and/or a predicted CMD endotype based on the identified CMD features within the captured imaging data and the set of patient data; generating, including the output one or more CMD measures and/or the predicted CMD endotype; and transmitting, in electronic communication.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification between physicians, third party providers and patients, which states that “In addition to server systems 140, the environment of FIG. 1 further includes a plurality of physicians 120 and third party providers 130, any of which may be connected to an electronic network 110, such as the Internet, through one or more computers, servers, and/or handheld mobile devices. In FIG. 1, physicians 120 and third party providers 130 may each represent a computer system, as well as an organization that uses such a system. For example, a physician 120 may be a hospital or a computer system of a hospital.” (see: specification paragraph 33). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “Physicians 120 and/or third party providers 130 may create or otherwise obtain medical images, such as images of the cardiac, vascular, and/or organ systems, of one or more patients. Physicians 120 and/or third party providers 130 may also obtain any combination of patient-specific information, such as age, medical history, blood pressure, blood viscosity, genetic risk factors, and other types of patient-specific information. Physicians 120 and/or third party providers 130 may transmit the patientspecific information to server systems 140 over the electronic network 110.” (see: specification paragraph 33). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “computer-implemented”, “one or more processors/processor”, “machine-learning model, wherein the machine-learning model has been trained to learn associations between imaging data and patient data”, “trained machine-learning model”, “graphical user interface”, “display”, “user device”, “image processing system”, and “data storage device”, “user device” are recited at a high level of generality (e.g., that the identifying and transmitting is performed using generic computer components and a generic machine learning model with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2, Figure 8 and
Paragraph 36, where “In some embodiments, server systems 140 may comprise and/or utilize a cloud computing platform with scalable resources for computations and/or data storage, and may run an application for performing methods described in this disclosure on the cloud computing platform. In such embodiments, any outputs may be transmitted to another computer system, such as a personal computer, for display and/or storage.”
Paragraph 37, where “Other examples of computer systems for performing methods of this disclosure include desktop computers, laptop computers, and mobile computing devices such as tablets and smartphones.”
Paragraph 38, where “A computer system, such as server systems 140, may include one or more computing devices. If the one or more processors of the computer system is implemented as a plurality of processors, the plurality of processors may be included in a single computing device or distribute among a plurality of computing devices. If a computer system comprises a plurality of computing devices, the memory of the computer system may include the respective memory of each computing device of the plurality of computing devices.”
Paragraph 31, where “The execution of the machine-learning model may include deployment of one or more machine-learning techniques, such as linear regression, logistic regression, random forest, gradient boosted machine (GBM), deep learning, and/or a deep neural network. Supervised and/or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth. Unsupervised approaches may include clustering, classification or the like. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. Any suitable type of training may be used, e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batchbased, etc.”
Paragraph 44, where “As depicted in FIG. 2, image processing system(s) 202 may include capturing module 204. In various embodiments, capturing module 204 is configured to receive captured imaging data of one or more electronic images. In examples, the electronic images may captured using medical imaging techniques, such as by computed tomography angiography (CTA), coronary CT angiography (CCTA), and the like”
Paragraph 40, where “The user device(s) 212 may be configured to enable a user to access and/or interact with other systems in the environment 200. For example, the user device(s) 212 may each be a computer system such as, for example, a desktop computer, a mobile device, a tablet, etc. In some embodiments, the user device(s) 212 may include one or more electronic application(s), e.g., a program, plugin, browser extension, etc., installed on a memory of the user device(s) 212. In some embodiments, the electronic application(s) may be associated with one or more of the other components in the environment 200. For example, the electronic application(s) may include one or more of system control software, system monitoring software, software development tools, etc.”
Paragraph 61, where “At step 320, the one or more CMD measures and the predicted CMD endotype may be transmitted to a user interface, such as to user device 212 via transmission module 208, as depicted in FIG. 2.”
Paragraph 83, “FIG. 8 depicts a flow diagram for training a machine-learning model. The trained machine learning model of FIG. 8 may be used in any of methods 300-700. As shown in flow diagram 800 of FIG. 8, training data 812 may include one or more of stage inputs 814 and known outcomes 818 related to a machine-learning model to be trained. The stage inputs 814 (e.g., medical imaging data such as CTA or CCTA images, on-imaging data, patient characteristics such as genetic factors, or ECG data, or the like) may be from any applicable source including a component or set shown in the figures provided herein. The known outcomes 818 (e.g., known CMD measures and the predicted CMD endotypes) may be included for machine-learning models generated based on supervised or semi-supervised training. An unsupervised machine-learning model might not be trained using known outcomes 818. Known outcomes 818 may include known or desired outputs for future inputs similar to or in the same category as stage inputs 814 that do not have corresponding known outputs.”
Paragraph 93, where “The hardware elements, operating systems, and programming languages of such equipment are conventional in nature, and it is presumed that those skilled in the art are adequately familiar therewith. Device 900 may also include input and output ports 950 to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various server functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the servers may be implemented by appropriate programming of one computer hardware platform.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-8, 10-16 and 18-20 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2-8, 10-16 and 18-20 recite determining, identifying and generating data from received image data on the generically recited computing systems as shown in the parent claims above.
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
The arguments filed 04/09/2026 have been fully considered.
Regarding the arguments pertaining to the 103 rejection, these arguments are persuasive. The amendments overcome the prior art of Figureoa-Alvarez (US 2022/0366571 A1), (“Physiological Stratification of Patients with Angina due to Coronary Microvascular Dysfunction”) (2020, Rahman et. al), Kwon (US 2024/0374196 A1), and Taylor (US 2021/0196391 A1) do not teach the claim amendments that the machine-learning model has been trained to learn associations between imaging data and patient data in combination with the predicted CMD endotype based on the identified CMD features within the captured imaging data and the set of patient is not taught by the prior art. A new search was conducted and found the prior art of Hartung (WO 2019068535 A1) that teaches imaging data sets being fed into a model but does not teach that it accounts for CMD endotypes in the training dataset. The 103 rejection has been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these argument are not persuasive. Applicant argues that the claimed invention improves the functioning of the imaging processing system. Examiner respectfully disagrees. The claimed invention does not provide an improvement to technology as the trained machine learning model and the computing components are recited generically in the specification as shown in the rejection above. Reciting the claim limitations without any substantiation as to how the system is being improved is not persuasive. The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
Further, quantifying coronary microvascular disease is inherently part of the abstract idea as it is directed to the interactions of patients with their healthcare providers. Quantifying a disease for improved diagnostics is improving the medical field by improving the interactions between a provider and a patient as evidenced in the specification and show that the claims are directed to organizing human activity. The “medical field” is not necessarily a “technical field”, nor is a treatment effected. Classen is an example of adding a meaningful limitation to the claims that create a practical application, however Classen integrated the results of the analysis into a specific and tangible method that resulted in the method “moving from abstract scientific principle to specific application” (Classen Immunotherapies Inc. v. Biogen IDEC). The current claimed limitations fail to provide this practical application and the 101 rejection is maintained.
The dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hartung (WO 2019068535 A1) teaches imaging data sets being fed into a model but does not teach that it accounts for CMD endotypes in the training dataset.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686
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