Response to Amendment
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Applicant’s response to the last Office Action dated 11/10/2025, as well as amendment to claims, filed on 02/10/2026 have been entered and made of record.
In light of Applicant’s amendment of the independent claims with the limitation that has been previously considered to integrate the identified abstract idea into a practical application, the rejection of claims under 35 U.S.C. 101 based on abstract idea has been withdrawn.
Status of Claims
Claims 21-22, 24-31, 33-38, and 40-43 are pending, Claims 41-43 are newly presented. Claims 1-20, 23, 32, and 39 are canceled.
Response to Arguments
Examiner has carefully reviewed Applicant’s arguments with respect to the amended claims filed with the Office on February 10, 2026. Applicant has presented new Claims 41-43, which recites “whether the one or more images satisfy simulation-specific quality thresholds required to execute the simulation”. However, Applicant does not explicitly refer to the paragraph in Applicant’s original specification or to the drawings, in which the recited limitation is supported. Review of Applicant’s specification reveals that the only mention of threshold is in Paragraph [00141], and not related to the recited limitation. Therefore, the new claims include “new matter”, which are subject to rejection under the first paragraph of 35 U.S.C. 112.
Applicant arguments presented in Pages 12-13 of its Reply with respect to the rejections of record under 35 U.S.C. 103 have been fully considered. Applicant’s arguments are merely directed to the amended portion of the claims, and the new analyses presented below render these arguments moot. Applicant’s amendment of independent Claims 21, 30, and 37 has altered the scope of the claims, and therefore, has initiated the following new ground(s) of rejection. THIS ACTION IS MADE FINAL.
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.
Claims 41-43 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 claims contain 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. Specifically, the specification does not have support for the limitation “whether the one or more images satisfy simulation-specific quality thresholds required to execute the simulation”.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-22, 24-25, 29, 30-31, 33, 36-38, and 40 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yan et al. (US 2009/0116713) in view of Mansi et al. (US 2013/0197881), and in further view of Claus et al. (US 2014/0153690).
Consider Claim 21, Yan discloses “A computer-implemented method for assessing a quality of a medical image of at least a portion of a patient's anatomy” (Yan, Paragraph [0031]), “comprising: receiving, via at least one processor, one or more images of at least a portion of the patient's anatomy” (Yan, Paragraph [0031] and Fig. 2:202; and also Paragraph [0060] disclose: “Medical image sequences, such as x-ray image sequences, can be taken of a particular part of the human body to sequentially record the physiological response during a certain period of time”. Finally, Fig. 11:1104 and Paragraph [0063] disclose: “medical image quality assessment may be implemented on a computer using well-known computer processors, memory units, storage devices, computer software, and other components”); “determining, via the at least one processor, an identification of one or more image characteristics of the one or more received images, where the one or more image characteristics are associated with one or more non-anatomical features or metadata of the one or more images” (Yan, Paragraph [0057] discloses:
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The recited sharpness and noise level quality indices are interpreted as image characteristics associated with non-anatomical features of the medical image); “the one or more image characteristics being associated with acquisition, reconstruction, or image quality attributes ” (Yan, Paragraph [0057] discloses quality index which is objective quantitative measures of the quality of input image); “and generating, via the at least one processor, an assessment of suitability of the received one or more images for (Yan, Paragraph [0057] discloses “the noise level quality index and the sharpness quality index can be combined, for example using machine learning of annotated sample images by human experts, to determine an overall quality index for medical image quality assessment.” Also, Yan discloses that the assessment of image quality is important to recognize if the captured image is suitable for further analyses and task, for example, see Paragraph [0003], wherein it is disclosed “When transmitting an image progressively, an objective image quality metric can be used to determine if the transmitted image information is good enough, or if more information needs to be transmitted”), “”. Although Yan’s method assesses image characteristics (quality) for medical analysis purposes, it does not explicitly disclose “patient-specific medical simulation”. However, in an analogous field of endeavor, Mansi discloses “a patient-specific computational heart model is generated based on the medical images. At step 120, CRT is simulated using the patient-specific computational heart model” (Mansi, Paragraph [0027] and Fig. 1, steps 100 and 110). In addition, Mansi discloses “wherein the patient- specific medical simulation includes a simulation of one or more of coronary blood flow, velocity, pressure, plaque and wall stress, or fractional flow reserve (FFR)” (Mansi, Paragraph [0070], where coronary blood flow is modeled and Paragraph [0046], the reference to endocardial pressure).
Accordingly, at the time of the invention, it would have been obvious to one of ordinary skill in the art to combine Yan with the teachings of Mansi to obtain the already assessed image for quality and use it for patient-specific medical simulation and the patient-specific medical simulation includes a simulation of one or more of coronary blood flow, velocity, pressure, plaque and wall stress, or fractional flow reserve (FFR) (emphasis added). One of ordinary skill in the art would be motivated to combine Yan and Mansi to make certain that the provided input image is of high enough quality in order to provide correct diagnostic results or simulation tools and avoid false positive and negative results due to noise in the poor quality image, and the better quality images result in more accurate simulation of coronary blood flow. Accordingly, the combination of Yan and Mansi discloses the above-described limitations of the invention of Claim 21.
The combination of Yan and Mansi does not explicitly disclose “image quality attributes that affect one or more of computation time or accuracy of a patient-specific medical simulation”. However, in an analogous field of endeavor, Claus discloses that the relationship between quality in a medical image and the computation times: “Further, the C-arm system may reconstruct the acquired projection data into two-dimensional (2D) and/or three-dimensional (3D) images using, for example, a filtered back-projection (FBP) or an algebraic reconstruction (ART) technique. The FBP approach, although fast, may introduce streaking artifacts that negatively impact image quality, and in turn, affect medical diagnosis and decision-making In contrast, iterative methods based on ART may reduce image artifacts by using image priors but may suffer from longer computation times” (Claus, Paragraph [0005]).
Accordingly, at the time of the invention, it would have been obvious to one of ordinary skill in the art to combine the combination of Yan and Mansi with the teachings of Claus to document the effect of image quality in a medical imaging environment with the computation time. One of ordinary skill in the art would be motivated to combine Yan, Mansi, and Claus to take into consideration the relationship between the computation times and the image quality in a medical imaging diagnostics environment. Accordingly, the combination of Yan, Mansi, and Claus discloses the invention of Claim 21.
Consider Claim 22, the combination of Yan, Mansi, and Claus discloses “The computer-implemented method of claim 21, wherein the one or more non-anatomical features are selected from a group consisting of a presence of an image artifact, an energy-tissue interaction characteristic of an acquisition of the one or more images, relative motion or position between different images in the one or more images, a reconstruction algorithm used to generate the one or more images, data indicative of a hardware failure during image acquisition, a sensitivity of a detector used for image acquisition, a local contrast level, a local noise level, a presence of a mis-registration or misalignment, a local motion anomaly, a local blurring anomaly, a partial volume effect, or a blooming effect” (emphasis added) (Yan, Paragraph [0021], where sharpness and noise level in the image are assessed and Paragraph [0039] discloses the degree of blurring).
Consider Claim 24, the combination of Yan, Mansi, and Claus discloses “The computer-implemented method of claim 21, further comprising: determining one or more image properties of the received one or more images, wherein the assessment of suitability of the received one or more images is further based on the determined one or more image properties” (Yan, Paragraph [0057], where sharpness quality index and noise level quality index, i.e., image properties, are assessed to determine suitability of the medical image for further analysis).
Consider Claim 25, the combination of Yan, Mansi, and Claus discloses “The computer-implemented method of claim 24, wherein the one or more image properties include one or more of: a spatial or temporal resolution of the one or more images, a medical anatomy slice thickness, a number of scanner slices, a medication or contrast agent parameter, or a patient characteristic” (emphasis added) (Mansi, Paragraph [0028] discloses “a patient-specific computational heart model is generated based on the personalized anatomic model 212 calculated from the images 202 and the clinical data 204. In particular, the patient-specific computational heart model is generated using an inverse problem to adjust parameters of the computational heart model such that simulated parameters 222”). The proposed combination as well as the motivation for combining the Yan, Mansi, and Claus references presented in the rejection of Claim 21, apply to Claim 25 and are incorporated herein by reference. Thus, the method recited in Claim 25 is met by Yan, Mansi, and Claus.
Consider Claim 29, the combination of Yan, Mansi, and Claus discloses “The computer-implemented method of claim 21, further comprising: selecting one of a plurality of different simulation techniques based on the assessment of suitability of the received one or more images for patient-specific medical simulation” (Mansi, Paragraph [0028] discloses “a patient-specific computational heart model is generated based on the personalized anatomic model 212 calculated from the images 202 and the clinical data 204. In particular, the patient-specific computational heart model is generated using an inverse problem to adjust parameters of the computational heart model such that simulated parameters 222, such as heart motion, ejection fraction, etc., output by the patient-specific computational heart model match the clinical data 204 observed for the patient. At the end of this stage, the model is completely generative and the heart function of the patient is accurately reproduced in the computer system performing the method”). The proposed combination as well as the motivation for combining the Yan, Mansi, and Claus references presented in the rejection of Claim 21, apply to Claim 29 and are incorporated herein by reference. Thus, the method recited in Claim 29 is met by Yan, Mansi, and Claus.
Claims 30-31, 33, and 36 recite systems with elements corresponding to the steps of the methods recited in Claims 21-22, 25, and 29, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps of methods of Claims 21-23, 25, and 29 respectively. Additionally, the rationale and motivation to combine the Yan, Mansi, and Claus references, presented in rejection of Claim 21, apply to these claims. Finally, the combination of Yan, Mansi, and Claus discloses a memory and a processor (Yan, Fig. 11:1110 and 1104).
Claims 37-38 and 40 recite computer-readable media storing programs with instructions corresponding to the steps of the methods recited in Claims 21-22 and 25, respectively. Therefore, the recited instructions of these claims are mapped to the proposed combination in the same manner as the corresponding steps of method Claims 21-23 and 25, respectively. Additionally, the rationale and motivation to combine the Yan, Mansi, and Claus references, presented in rejection of Claim 21, apply to these claims. Finally, the combination of Yan, Mansi, and Claus discloses memory and storage for the programs (Yan, Fig. 11: 1110 and 1112).
Claims 26 and 34 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yan et al. (US 2009/0116713) in view of Mansi et al. (US 2013/0197881), and in further view of Claus et al. (US 2014/0153690), and still in further view of Wilkes et al. (US 2013/0131992).
Consider Claim 26, the combination of Yan, Mansi, and Claus does not explicitly disclose “The computer-implemented method of claim 21, wherein the assessment of suitability of the received one or more images for patient-specific medical simulation includes a determination of an effect of the image quality of the received one or more images on one or more of computation time or accuracy of one or more of modeling or simulation results relative to a baseline.” However, in an analogous field of endeavor, Wilkes discloses “The accuracy and utility of predictive models strongly depend on at least several factors related to the quality of the MRS scans” (Wilkes, Paragraph [0008]).
Accordingly, at the time of the invention, it would have been obvious to one of ordinary skill in the art to combine the combination of Yan, Mansi, and Claus with the teachings of Wilkes to conclude that the quality of images affects the simulation/modeling. One of ordinary skill in the art would be motivated to combine Yan, Mansi, Claus, and Wilkes to assess the quality of images in order to achieve a better medical simulation result. Accordingly, the combination of Yan, Mansi, Claus, and Wilkes discloses the invention of Claim 26.
Claim 34 recites a system with elements corresponding to the steps of the method recited in Claim 26. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps of method of Claim 26. Additionally, the rationale and motivation to combine the Yan, Mansi, Claus, and Wilkes references, presented in rejection of Claim 26, apply to this claim. Finally, the combination of Yan, Mansi, Claus, and Wilkes discloses a memory and a processor (Yan, Fig. 11:1110 and 1104).
Claims 27-28 and 35 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yan et al. (US 2009/0116713) in view of Mansi et al. (US 2013/0197881), and in further view of Claus et al. (US 2014/0153690), and still in further view of Wang et al. (US 2010/0086189).
The combination of Yan, Mansi, and Claus does not explicitly disclose “The computer-implemented method of claim 21, further comprising: in response to determining that the assessment of suitability of the received one or more images for patient-specific medical simulation is below a predetermined threshold, obtaining one or more supplemental images of the patient's anatomy.” However, in an analogous field of endeavor, Wang discloses measurement of the quality of the acquired image, and if the quality is less that acceptable, provides the technician of an opportunity to obtain additional images (Wang, Paragraph [0038]).
Accordingly, at the time of the invention, it would have been obvious to one of ordinary skill in the art to combine the combination of Yan, Mansi, and Claus with the teachings of Wang to retake additional medical images if the quality of the original one is less than an acceptable threshold. One of ordinary skill in the art would be motivated to combine Yan, Mansi, Claus, and Wang to make certain that the provided input final image is of high enough quality in order to provide correct diagnostic results or simulation tools and avoid false positive and negative results due to noise in the poor quality image. Accordingly, the combination of Yan, Mansi, Claus, and Wang discloses the invention of Claim 27.
Consider Claim 28, the combination of Yan, Mansi, Claus, and Wang discloses “The computer-implemented method of claim 27, wherein the one or more supplemental images are sourced from one or more of a prior acquisition or of a different modality of acquisition, relative to the received one or more images” (Wang, Paragraph [0038] discloses granting the technician the opportunity to alter acquisition variables to retake additional images). The proposed combination as well as the motivation for combining the Yan, Mansi, Claus, and Wang references presented in the rejection of Claim 27, apply to Claim 28 and are incorporated herein by reference. Thus, the method recited in Claim 28 is met by Yan, Mansi, Claus, and Wang.
Claim 35 recites a system with elements corresponding to the steps of the method recited in Claim 28. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps of method of Claim 28. Additionally, the rationale and motivation to combine the Yan, Mansi, Claus, and Wang references, presented in rejection of Claim 27, apply to this claim. Finally, the combination of Yan, Mansi, Claus, and Wang discloses a memory and a processor (Yan, Fig. 11:1110 and 1104).
Claims 41-43 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Yan et al. (US 2009/0116713) in view of Mansi et al. (US 2013/0197881), and in further view of Claus et al. (US 2014/0153690), and still in further view of Ronald C. Reitan (US 5,600,574).
Consider Claim 41, although as presented in analysis of Claim 21, the combination of Yan, Mansi, and Reitan measures the quality of the medical image and perform simulation of an anatomical tissue of a patient, it does not explicitly disclose “wherein the assessment indicates whether the one or more images satisfy simulation-specific quality thresholds required to execute the simulation.” However, in an analogous field of endeavor, Reitan discloses measurement of image quality will be a deciding factor for a simple go/ no go instructions to a system operator (Reitan, Column 2, lines 66 to Column 3, line 6).
Accordingly, at the time of the invention, it would have been obvious to one of ordinary skill in the art to combine the combination of Yan, Mansi, and Claus with the teachings of Reitan to establish a quality measurement and threshold to decide to carry on simulation or not. One of ordinary skill in the art would be motivated to combine Yan, Mansi, Claus, and Reitan to perform medical simulation in response to the measured quality being in an acceptable range to satisfy accuracy of the result of the simulation. Accordingly, the combination of Yan, Mansi, Claus, and Reitan discloses the invention of Claim 41.
Claim 42 recites a system with elements corresponding to the steps of the method recited in Claim 41. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps of method of Claim 41. Additionally, the rationale and motivation to combine the Yan, Mansi, Claus, and Reitan references, presented in rejection of Claim 41, apply to this claim. Finally, the combination of Yan, Mansi, Claus, and Reitan discloses a memory and a processor (Yan, Fig. 11:1110 and 1104).
Claim 43 recites a non-transitory computer-readable medium with instructions corresponding to the steps of the method recited in Claim 41. Therefore, the recited instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps of method of Claim 41. Additionally, the rationale and motivation to combine the Yan, Mansi, Claus, and Reitan references, presented in rejection of Claim 41, apply to this claim. Finally, the combination of Yan, Mansi, Claus, and Reitan discloses a memory and a processor (Yan, Fig. 11:1110 and 1104).
Conclusion and Contact Information
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Siamak HARANDI whose telephone number is (571)270-1832. The examiner can normally be reached Monday - Friday 9:30 - 6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Siamak Harandi/Primary Examiner, Art Unit 2662
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