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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/04/2026 has been entered.
Status of Claims
This action is in reply to the present claims filed on 08/04/2026.
Claims 1, 6, 10, and 12 have been amended.
Claims 1-7, 9-12, and 14-21 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 1-7, 9-12, and 14-21 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 Analysis:
Independent Claims 1, 10, and 12 are within one of the four statutory categories. Claims 1, 10, and 12 are directed to a system, a method, and a non-transitory computer-readable storage medium (i.e. machine), respectively. Dependent Claims 1-7 and 9 recite a system, 11 and 14-17 recite a method, and 18-21 recite a non-transitory computer-readable medium, and therefore also fall within one of the four statutory categories.
Step 2A Analysis – Prong One:
The substantially similar independent claims, taking Claim 1 as exemplary, recite the following:
A system for predicting a patient treatment option, the system comprising: a processor configured to:
receive input data including biodynamical measurements comprising coronary vessel motion data in respect of a patient;
and process the biodynamical measurements to obtain output data including (i) an indication for a treatment option for the patient, (ii) one or more treatment parameters associated with the treatment option, and (iii) one or more predicted treatment outcomes resulting from performing the treatment option under the one or more treatment parameters,
wherein the biodynamical measurements are processed based on a trained machine learning model previously trained on patient data from a cohort of patient,
and wherein the machine learning model is implemented based on a clustering algorithm.
The series of steps as shown underlined above, given the broadest reasonable interpretation, recite the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions- in this case, receiving input data and processing the data to determine an indication for a treatment plan) e.g., see MPEP 2106.04(a)(2). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-7 and 14-21 recite other limitations directed toward the abstract idea. For example, Claims 2, 14, and 18 recite the biodynamical measurements is a time series, Claims 3, 15, and 19 recite the different treatment options correspond to different clusters, and the indication includes an indication of the different clusters, Claims 4, 16, and 20 recite a visualization of the indication, Claims 5, 17, and 21 recite a visualization of different clusters, Claim 6 recites details of what data types are included in the biodynamical measurements, Claim 7 recites the biodynamical measurements include image data. The dependent claims only serve to narrow the abstract idea set forth in the independent claims, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, see MPEP 2106.04. Hence, the dependent claims are further directed toward certain methods of organizing human activity.
Step 2A Analysis – Prong Two:
Claims 1, 10, and 12 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, machine learning model, and clustering algorithm of Claim 1, the clustering algorithm of Claim 10, and the non-transitory computer-readable storage medium, computer program, processor, machine learning model, and clustering algorithm of Claim 12) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply the exceptions using generic computer components. For example, Applicant’s specification explains that the computing device PU on which the system is implemented is powered by one or more processors capable of parallel processing. For example, processors of multi-core design may be used to parallelize the computing performed by the predictor module PM (see Applicant’s specification, p. 7, lines 35-36). [M]achine learning ("ML") approaches are used to train an ML model on large amounts of patient data held in database(s), the predictor module using such a trained model to predict, that is compute, the treatment option (p. 4, lines 11-13). For example, in the said clustering type algorithms, after training, the predictor module PM provides an indication, such as centroid or other descriptor, for one or more of the given pre-defined cluster(s) C1 into which the patient is likely to fall into (p. 12, lines 7-9). A computer program may be stored and/or distributed on a suitable medium (in particular, but not necessarily, a non-transitory medium), such as an optical storage medium or a solid- state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems (p. 21, lines 29-32). Furthermore, Examiner notes that the step of training a machine learning model recited in Claims 1, 10, and 12 amounts to merely generally linking to a technological field of use. MPEP 2106.04(d)(1) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the idea. Therefore, Claims 1, 10, and 12 are directed to an abstract idea without practical application.
Dependent Claims 4-5, 9, 11, 16-18, and 20-21 also recite additional elements. Claims 4, 16, and 20 recite the previously recited processor and new additional elements of a graphics display and display device and specify the processor generates a graphics display for display on a display device. Claims 5, 17, and 21 recite the previously recited graphics display and specify the graphics display includes a visualization of the clusters and a graphical indicator (new additional element). Claim 9 recites the previously recited machine learning model and a new element of a training system and specifies a training system for training the machine learning model. Claim 11 recites the biodynamical measurements are processed based on a trained machine learning model (new element) previously trained on patient data from a cohort of patients, and wherein the machine learning model is implemented based on the clustering algorithm (previously recited). However, these additional elements are described only at a high level of generality and are being used in their expected fashion, so these additional elements do not integrate the abstract idea into a practice application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or in combination, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A prong two, the additional elements of the processor, machine learning model, and clustering algorithm of Claim 1, the clustering algorithm of Claim 10, and the non-transitory computer-readable storage medium, computer program, processor, machine learning mode, and clustering algorithm of Claim 12 amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”) in step 2B. MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). For these reasons, independent Claims 1, 10, and 12 are not patent eligible.
Dependent Claims 2-3, 6-7, 14-15, and 18-19 do not recite any additional elements and further narrow the abstract idea. Claims 2, 14, and 18 recite the biodynamical measurements is a time series. Claims 3, 15, and 19 recite the different treatment options correspond to different clusters, and the indication includes an indication of the different clusters. Claim 6 recites details of what data types are included in the biodynamical measurements. Claim 7 recites the biodynamical measurements include image data.
Dependent Claims 4, 16, and 20 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claims 4, 16, and 20 recite the previously recited processor and new additional elements of a graphics display and display device and specify the processor generates a graphics display for display on a display device.
Dependent Claims 5, 9, 11, 17, and 21 recite new additional elements. Claims 5, 17, and 21 recite the previously recited graphics display and specify the graphics display includes a visualization of the clusters and a graphical indicator (new additional element). Claim 9 recites the previously recited machine learning model and a new element of a training system and specifies a training system for training the machine learning model. Claim 11 recites the biodynamical measurements are processed based on a trained machine learning model (new element) previously trained on patient data from a cohort of patients, and wherein the machine learning model is implemented based on the clustering algorithm (previously recited).
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 does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of 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 1-7, 9-12, and 14-21 are rejected under 35 U.S.C. § 101 as being directed to a 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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claims 1-7, 9, 11-12, and 14-21 are rejected under 35 USC § 103 as being unpatentable over Johnson et al. (US 20220183571 A1) in view of Bydlon et al. (US 20240383133 A1), Lobsiger et al. (US 20230268079 A1, and Isgum et al. (US 20200394795 A1).
Regarding Claim 1, Johnson discloses the following:
A system for predicting a patient treatment option, the system comprising: a processor configured to: (Johnson discloses systems and methods are provided for generating, training, and applying models for predicting an objective based on features associated with a patient [0004].)
receive input data including biodynamical measurements…in respect of a patient; (Johnson discloses the machine learning engine includes one or more cardiac objective models and trained using a training electrocardiogram signal data set and a training observational patient feature data set, to predict a cardiac objective state; and predicting, in the one or more processors, a probability of the cardiac objective state using the trained machine learning model [0005].)
and process the biodynamical measurements to obtain output data including (i) an indication for a treatment option for the patient, (Johnson discloses a convolutional neural network (CNN) may receive each lead of an ECG at a one dimensional convolutional layer and each branch may be received…for generating prediction results,…[0100]. The Examiner interprets the ECG data as the measurements. The electronic report may include a recommendation to a physician to treat the patient using a treatment that correlates with a magnitude of a determined degree of risk, a recommendation to a physician to de-escalate when the patient is low risk to reduce adverse events,…or a recommendation to a physician to elect a treatment which provides adjustments to the typical monitoring such as scanning, imaging, blood testing…the electronic report may include a recommendation for accelerated screening of the patient, a recommendation for consideration of additional monitoring [0053].)
and (iii) one or more predicted treatment outcomes resulting from performing the treatment option… (Johnson discloses the method further includes: in response to predicting the probability of the cardiac objective state, predicting, in the one or more processors, a target cardiac outcome; and automatically generating an electronic report including the predictions of probability of the target cardiac outcome [0012].)
wherein the biodynamical measurements are processed based on a trained machine learning model previously trained… (Johnson discloses information acquired from patients' electronic medical records (EMR), unstructured text, genetic sequencing, imaging, and various other information can be converted into features that are used for training a plurality of machine-learning models [0074].)
and wherein the machine learning model is implemented based on a clustering algorithm (Johnson discloses MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression,…nearest neighbor clustering;…[0108].)
Johnson does not disclose the training data coming from a cohort of patients which is met by Bydlon:
…a trained machine learning model previously trained on patient data from a cohort of patients, (Bydlon teaches using any one of age, BMI, anatomical target, or procedure type may improve the robustness and accuracy of predicted categories by filtering existing training data set S′ and using information only from patients with similar patient characteristics when predicting the category. The training data set S′ can thus be stratified according to patient characteristic [0266].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the model being trained using patient data from a cohort of patient as taught by Bydlon. This modification would create a system and method capable of improving performance of the algorithm (see Bydlon, ¶ 0031).
Johnson and Bydlon do not teach the machine learning model outputting treatment parameters which is met by Lobsiger:
(ii) one or more treatment parameters associated with the treatment option,… under the one or more treatment parameters (Lobsiger teaches treatment parameters may be provided as an output by an ML model trained to estimate/predict optimal treatment parameters given paired treatment goal and patient data inputs [0041].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the machine learning model outputting treatment parameters as taught by Lobsiger. This modification would create a system and method which makes treatment parameters more efficiently tuned/updated, thereby improving the patient treatment outcome (see Lobsiger, ¶ 0034).
Johnson, Bydlon, and Lobsiger do not teach the input to the machine learning model being coronary vessel motion data which is met by Isgum:
…input data including biodynamical measurements comprising coronary vessel motion data in respect of a patient; (Isgum teaches using the axial trajectory of the coronary vessel of interest to sample image data that corresponds to the coronary vessel of interest; using the image data as input to the first machine learning-based model,…[0024-25]. The Examiner interprets an axial trajectory of a coronary vessel as coronary vessel motion.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the input to the machine learning model being coronary vessel motion data as taught by Isgum. This modification would provide a functional assessment of blood vessels (see Isgum, ¶ 0004).
Regarding Claim 12, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Johnson further discloses:
A non-transitory computer-readable storage medium having stored a computer program comprising instructions, which, when executed by a processor, cause the processor to: (The computer system 1200 includes a processing device 1202, a main memory 1204 (such as read-only memory (ROM), flash memory, dynamic random access memory (DRAM)…and a data storage device…[0186].)
Regarding Claim 2, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 1 above. Johnson further discloses:
wherein the biodynamical measurements is a time series. (Johnson discloses an ECG may include resting 12-lead electrocardiograms (ECGs) such as 1250 signal values short leads (e.g., Leads I, V2, V3, V4, V6) or 5000 signal values per long, rhythm ECG lead (e.g., Leads II, V1, V5) having voltages associated with each lead over a period of time. In some examples, for a cardiac event prediction model trained on ECG only, a resulting receiver operating characteristic (ROC) area under curve (AUC) may be approximately 0.52 [0165-166]. The Examiner interprets the ECG data as a time series as it includes a time period.)
Regarding Claims 14 and 18, this claim recites limitations that are substantially similar to those recited in claim 2 above; thus, the same rejection applies.
Regarding Claim 3, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 1 above. Johnson further discloses
wherein different treatment options…the indication (Johnson discloses a the electronic report may include a recommendation to a physician to treat the patient using a treatment that correlates with a magnitude of a determined degree of risk, a recommendation to a physician to de-escalate when the patient is low risk to reduce adverse events,…or a recommendation to a physician to elect a treatment which provides adjustments to the typical monitoring such as scanning, imaging, blood testing…the electronic report may include a recommendation for accelerated screening of the patient, a recommendation for consideration of additional monitoring [0053].)
Johnson does not disclose the output treatment options corresponding to different clusters which is met by Bydlon:
wherein [the output] correspond to different clusters, and wherein the indication includes an indication of one or more of the different clusters. (Bydlon teaches the categories predicted or output are hence presented by clusters in the training data. Training may be done independent from imagery such as X-ray imagery, but alternatively such imagery may still be used, in particular when generating synthetic trainings data by the training data generator system,…[0020].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the output treatment options corresponding to different clusters as taught by Bydlon. This modification would create a system and method capable of improving performance of the algorithm (see Bydlon, ¶ 0031).
Regarding Claims 15 and 19, this claim recites limitations that are substantially similar to those recited in claim 3 above; thus, the same rejection applies.
Regarding Claim 4, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 3 above. Johnson further discloses:
The processor is further configured to generate a graphics display for display on a display device, the graphics display provides a visualization of the indication. (Johnson discloses the electronic report may include a recommendation to a physician to treat the patient using a treatment that correlates with a magnitude of a determined degree of risk, a recommendation to a physician to de-escalate when the patient is low risk to reduce adverse events,…or a recommendation to a physician to elect a treatment which provides adjustments to the typical monitoring such as scanning, imaging, blood testing…the electronic report may include a recommendation for accelerated screening of the patient,…[0053]. The webform may receive the predictions and display them to the user through the user interface of the webform 900, as seen in FIG. 9A [0157].)
Regarding Claims 16 and 20, this claim recites limitations that are substantially similar to those recited in claim 4 above; thus, the same rejection applies.
Regarding Claim 5, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 4 above. Johnson further discloses:
and a graphical indicator in respect of the patient indicative of proximity or similarity to the different clusters. (Johnson discloses MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression,…nearest neighbor clustering;…[0108].)
Johnson does not disclose the following limitations met by Bydlon:
wherein the graphics display includes a visualization of the different clusters (Bydlon teaches the current shape measurement may also be rendered in a graphical view on the same or a different graphics display in a different view pane. The user may be able to switch between different such panes. Specifically, as a visual check aid, the current shape measurement may be visualized versus a graphical rendition of the training data clusters, similar to FIG. 5. The current shape measurement may be displayed in association with its predicted cluster, with other one or more clusters also displayed in other parts of the graphic display [0115].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate displaying different clusters as taught by Bydlon. This modification would create a system and method which can allow a user to check visually very quickly whether the predicted cluster is sound (see Bydlon, ¶ 0031).
Regarding Claims 17 and 21, this claim recites limitations that are substantially similar to those recited in claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 1 above. Johnson further discloses:
the biodynamical measurements further incude one or more of: perfusion data, electrocardiogram data, electroencephalogram data, and oxygenation data. (Johnson discloses the machine learning engine includes one or more cardiac objective models and trained using a training electrocardiogram signal data set and a training observational patient feature data set, to predict a cardiac objective state; and predicting, in the one or more processors, a probability of the cardiac objective state using the trained machine learning model [0005].)
Regarding Claim 7, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 1 above. Johnson further discloses:
wherein the biodynamical measurements include image data. (Johnson discloses an imaging module, such as, e.g., the imaging module 117, may comprise a feature collection associated with information derived from imaging records of a patient. Imaging records may include electrocardiograms, fractional flow reserve, H&E slides, IHC slides, radiology images, and other medical imaging information,… [0088].)
Regarding Claim 9, Johnson, Bydlon, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 1 above. Johnson further discloses:
A training system for training, based on the training data, the machine learning model of the system of claim 1. (Johnson discloses systems and methods are provided for generating, training, and applying models for predicting an objective based on features associated with a patient [0004]. [T]he machine learning engine includes one or more cardiac objective models and trained using a training electrocardiogram signal data set and a training observational patient feature data set, to predict a cardiac objective state; and predicting, in the one or more processors, a probability of the cardiac objective state using the trained machine learning model [0005].)
Regarding Claim 11, Johnson, Lobsiger, and Isgum teach the limitations as seen in the rejection of Claim 10. Johnson further discloses:
wherein the biodynamical measurements are processed based on a trained machine learning model previously trained… Johnson discloses information acquired from patients' electronic medical records (EMR), unstructured text, genetic sequencing, imaging, and various other information can be converted into features that are used for training a plurality of machine-learning models [0074].)
and wherein the machine learning model is implemented based on the clustering algorithm (Johnson discloses MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression,…nearest neighbor clustering;…[0108].)
Johnson does not disclose the training data coming from a cohort of patients which is met by Bydlon:
…a trained machine learning model previously trained on patient data from a cohort of patients, (Bydlon teaches using any one of age, BMI, anatomical target, or procedure type may improve the robustness and accuracy of predicted categories by filtering existing training data set S′ and using information only from patients with similar patient characteristics when predicting the category. The training data set S′ can thus be stratified according to patient characteristic [0266].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the model being trained using patient data from a cohort of patient as taught by Bydlon. This modification would create a system and method capable of improving performance of the algorithm (see Bydlon, ¶ 0031).
Claim 10 is rejected under 35 USC § 103 as being unpatentable over Johnson et al. (US 20220183571 A1) in view of Lobsiger et al. (US 20230268079 A1) and Isgum et al. (US 20200394795 A1).
Regarding Claim 10, Johnson discloses the following:
A computer-implemented method for predicting a patient treatment option, the method comprising: (Johnson discloses systems and methods are provided for generating, training, and applying models for predicting an objective based on features associated with a patient [0004].)
receiving input data including biodynamical measurements…in respect of a patient; (Johnson discloses the machine learning engine includes one or more cardiac objective models and trained using a training electrocardiogram signal data set and a training observational patient feature data set, to predict a cardiac objective state; and predicting, in the one or more processors, a probability of the cardiac objective state using the trained machine learning model [0005].)
and processing the biodynamical measurements to obtain output data including an indication for a treatment option for the patient, (Johnson discloses a convolutional neural network (CNN) may receive each lead of an ECG at a one dimensional convolutional layer and each branch may be received…for generating prediction results,…[0100]. The Examiner interprets the ECG data as the measurements. The electronic report may include a recommendation to a physician to treat the patient using a treatment that correlates with a magnitude of a determined degree of risk, a recommendation to a physician to de-escalate when the patient is low risk to reduce adverse events,…or a recommendation to a physician to elect a treatment which provides adjustments to the typical monitoring such as scanning, imaging, blood testing…the electronic report may include a recommendation for accelerated screening of the patient, a recommendation for consideration of additional monitoring [0053].)
wherein the processing is based on a clustering algorithm. (Johnson discloses MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression,…nearest neighbor clustering;… [0108].)
Johnson does not disclose the machine learning model outputting treatment parameters which is met by Lobsiger:
(ii) one or more treatment parameters associated with the treatment option,… under the one or more treatment parameters (Lobsiger teaches treatment parameters may be provided as an output by an ML model trained to estimate/predict optimal treatment parameters given paired treatment goal and patient data inputs [0041].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the machine learning model outputting treatment parameters as taught by Lobsiger. This modification would create a system and method which makes treatment parameters more efficiently tuned/updated, thereby improving the patient treatment outcome (see Lobsiger, ¶ 0034).
Johnson and Lobsiger do not teach the input to the machine learning model being coronary vessel motion data which is met by Isgum:
…input data including biodynamical measurements comprising coronary vessel motion data in respect of a patient; (Isgum teaches using the axial trajectory of the coronary vessel of interest to sample image data that corresponds to the coronary vessel of interest; using the image data as input to the first machine learning-based model,…[0024-25]. The Examiner interprets an axial trajectory of a coronary vessel as coronary vessel motion.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for receiving biodynamical measurements, processing the measurements with a machine learning model, and outputting an indication for a treatment option as disclosed by Johnson to incorporate the input to the machine learning model being coronary vessel motion data as taught by Isgum. This modification would provide a functional assessment of blood vessels (see Isgum, ¶ 0004).
Relevant Art Made of Record Not Currently Being Applied
The following references are considered pertinent to the Applicant’s disclosure but are not currently being relied upon:
Sharma et al. (US 20180233233 A1) teaches a system for automated disease progression modeling and therapy optimization for an individual patient which includes using a trajectory clustering algorithm.
Fleming et al. (US 20200320692 A1) teaches a system for determining a pathologic condition from a medical image of a portion of a subject which includes acquiring a plurality of lesion locations in a medical image and applying a clustering algorithm to the plurality of lesion locations to identify at least one lesion cluster and corresponding lesion cluster data.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-7, 9-12, and 14-21, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues that the amended claims are not directed merely to organizing human activity nor do they recite managing personal behavior or relationships or interactions between people. Claim 1 recites that "the biodynamical measurements are processed based on a trained machine learning model previously trained on patient data from a cohort of patients" and that "the machine learning model is implemented based on a clustering algorithm." These claim limitations do not recite organizing human activity, managing personal behavior, or managing relationships or interactions between people. Rather, the claims recite processing "biodynamical measurements comprising coronary vessel motion data" using the recited clustering-based machine-learning processing to obtain the expressly recited output data. Applicant's Specification explains that "coronary vessel motion data have been found to be a particularly robust (bio-) marker/indicator for treatment options" and that such coronary vessel motion data "may be represented by motion fields, in 4D (3D+t) or 2D+1, such as based on segmented cardiac imagery" (see Applicant's Specification, para [0018]). Thus, the claims as a whole are not directed to the alleged abstract idea (see Applicant’s Remarks, p. 6-7).
Regarding (a), Examiner respectfully disagrees. The additional elements are not considered when evaluating whether there is a judicial exception present. Instead, the additional elements are omitted, and the limitations that remain are evaluated for the presence of a judicial exception, and in this case, the limitations (i.e., receiving input data and processing the data to obtain an output data of an indication of a treatment option, treatment parameters, and predicted treatment outcomes) are steps that could be reasonably carried out by a person following a set of rules or instructions. It is important to note that the text within the parentheses stating social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that the October 2019 Update: Subject Matter Eligibility on p. 5 stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Applicant argues Claim 1 does not merely recite receiving information and displaying a result; claim 1 recites "receive input data including biodynamical measurements comprising coronary vessel motion data in respect of a patient" and "process the biodynamical measurements to obtain output data including (i) an indication for a treatment option for the patient, (ii) one or more treatment parameters associated with the treatment option, and (iii) one or more predicted treatment outcomes resulting from performing the treatment option under the one or more treatment parameters," wherein "the biodynamical measurements are processed based on a trained machine learning model previously trained on patient data from a cohort of patients" and "the machine learning model is implemented based on a clustering algorithm." Applicant's Specification explains, but does not limit, that "the clustering algorithm is based on a similarity measure," that the similarity measure may be used "to divide the training data based on a patient cohort into distinct clusters," and that, after training, "the treatment option prediction for a given patient is computed based on the similarity measure or a related measure" (see Applicant's Specification, para [0014]). "[t]he proposed system or method allow practicing personalized medicine by taking, if required, a large number of characteristics of the current patient into account" and that "[t]he system affords a technical solution to aid medical decision making" (see Applicant's Specification, para [0031]). Accordingly, the claims apply the recited processing to a concrete medical-data context involving "biodynamical measurements comprising coronary vessel motion data" and integrate any alleged abstract idea into a practical application (p. 7-8).
Regarding (b), Examiner respectfully disagrees. Examiner notes that predicting treatments and treatment outcomes is not a technical problem. That is because the concept of medical decision making has existed since long before the advent of computer technology, and thus cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Assuming, arguendo, that the problem was technical in nature, there are no details in the claims that indicate how the clustering algorithm works such that it solves a problem in an unconventional manner. Examiner notes that merely training a machine learning model with data and the model outputting a determination amounts to no more than generally linking the additional elements of the machine learning model and clustering algorithm to the technological field of use or technological environment of machine learning because there is nothing in the claims or the specification that provide an explanation as to how such training is unconventional or different than the standard manner upon which machine learning models are commonly trained. The section of the specification ([0014] and [0031]) highlighted by Applicant above merely specify the specific information that the data contains which is a modification to the abstract idea and therefore cannot provide a practical application.
Applicant argues Claim 1 requires the ordered combination of "receive input data including biodynamical measurements comprising coronary vessel motion data in respect of a patient," "process the biodynamical measurements to obtain output data including (i) an indication for a treatment option for the patient, (ii) one or more treatment parameters associated with the treatment option, and (iii) one or more predicted treatment outcomes resulting from performing the treatment option under the one or more treatment parameters," processing the biodynamical measurements "based on a trained machine learning model previously trained on patient data from a cohort of patients," and implementing the machine learning model "based on a clustering algorithm." Applicant's Specification explains that "the patterns may be clustered into descriptive clusters," that "[a] correlation between motion pattern and disease progression, optimal treatment decisions, optimal follow-up regime, risk factors, etc., can be determined," and that "[t]he clustering is performed by using a suitable similarity measure to compare motion descriptive vectors" (see Applicant's Specification, [0034]). The claims therefore do not merely instruct a generic computer to apply an abstract idea, but instead recite a specific processing of "biodynamical measurements comprising coronary vessel motion data" to obtain the recited treatment option, associated treatment parameters, and predicted treatment outcomes (p. 8).
Regarding (c), Examiner respectfully disagrees. Examiner notes that the limitations argued of receiving data input, processing measurements to obtain a treatment option, treatment parameters, and predicted outcomes are abstract and abstract cannot provide practical application or significantly more. An improvement to these abstract ideas does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology). For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to the type of data that is utilized. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Examiner notes that the machine learning model, training the model, and clustering algorithm are not a part of the abstract idea and are additional elements; however, these elements are recited at a high level of generality such that the claims seem more analogous to "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Regarding rejections under 35 USC 103 to Claims 1-7, 9-12, and 14-21, Applicant’s arguments have been considered and are persuasive. Therefore, the rejection has been withdrawn. However, in light of the amendments, a new rejection has been made rejecting independent claim 1 over Johnson in view of Bydlon, Lobsiger, and Isgum.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
