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 .
The Applicant’s Remarks filed 11/11/2025 have been received and considered.
The 112(b) rejections cited in the non-final office action mailed 08/11/2025 are hereby withdrawn.
Claims 1, 5, 7, 15, and 19 have been amended.
Claims 2 has been canceled.
Claims 1 and 3 – 20, all of the remaining claims in this application, have been rejected.
Claim Objections
Claim 7 is objected to because of the following informalities:
Claim 7 recites “wherein the diagnostic medical images are an optical coherence tomography images”. It should read as “wherein the diagnostic medical images are [[an]] optical coherence tomography images”.
Appropriate correction is required.
Response to Applicant’s Remarks
Applicant’s remarks were filed 11/11/2025 regarding amendments to independent claims 1, 15, and 19. Applicant’s remarks on Page 7 argue that the combination of both cited prior arts of Lee and Rothberg fail to teach a system in which image quality is identified based at least in part on an extent of artifacts within the lumen causing lumen view obstruction. Applicant further states that the cited portion of Lee in which it is stated that image frames were “reviewed” and those of poor quality were excluded. It’s noted that Applicant’s main point is that the image quality is to be determined by that of a system/apparatus and not done manually (i.e. done by cardiologists in this reference).
The Examiner disagrees with the remarks made by the Applicant. Examiner notes section 2144.04(III) of the MPEP:
III. AUTOMATING A MANUAL ACTIVITY
In reVenner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) (Appellant argued that claims to a permanent mold casting apparatus for molding trunk pistons were allowable over the prior art because the claimed invention combined “old permanent-mold structures together with a timer and solenoid which automatically actuates the known pressure valve system to release the inner core after a predetermined time has elapsed.” The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.).
In this case, a system for automating an image quality and the determination of image quality manually by medical personal as disclosed in Lee, get to the same result which under the Broadest Reasonable Interpretation (BRI) is to determine if the image is usable or of a poor quality based on the presence of artifacts within the lumen. Therefore, Examiner maintains that the combined prior art referenced in the non-final mailed 08/11/2025 do indeed teach the features of the claims, as detailed below.
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, 3 – 7, 9 – 10, 12 – 13, and 15 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature “Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features” to Lee et al. (hereinafter Lee) in view of Rothberg et al. (US Publication No. 20190130554 A1) (hereinafter Rothberg).
Claim 1
Lee teaches a method of identifying quality diagnostic medical images, the method comprising: receiving, by one more processors, diagnostic medical images of a lumen captured as a series of images during a patient imaging procedure ("Third, we use a large number of IVOCT images and corresponding labels, including a wide variety of lesions (i.e., over 6,500 images, 49 patients, and 111 volumes of interest (VOIs))", Introduction);
analyzing by one or more processors, in real time or near real time, with a trained machine learning model, the diagnostic medical images, wherein the trained machine learning model is trained on a set of annotated diagnostic medical images with respect to lumen view obstruction within the medical images (Figure 1; "First, A-line classification avoids the issue of the indeterminate back border of lipidous plaque found in pixel classification methods. That is, because there is rapid absorption of light in lipidous tissues, one cannot determine the location of the back boarder. Second, we create a powerful, hybrid classification which combines deep learning A-line features and hand-crafted lumen morphological features. The latter exploits the observation that non-circular lumens are often observed in the presence of calcifications28. Essentially, we are combining two methods previously reported by us: A-line CNN classification27 and lumen morphological features which were identified as important among 1000 s of hand-crafted features previously reported28.", Introduction);
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Identifying by one or more processors, based on the analyzing, image quality for the diagnostic medical images, wherein the image quality is based, at least in part, on an extent of artifacts within the lumen causing lumen view obstruction (Figure 2; "IVOCT images were acquired with a frequency-domain OCT system (ILUMIEN OPTIS; St. Jude Medical Inc.). During image acquisition, the optical probe was automatically pulled back from distal to proximal at a speed of 36 mm/s, frame rate of 180 frames/sec, and axial resolution of 20 μm. All IVOCT image frames were initially reviewed, and frames having poor quality due to luminal blood, unclear lumen, artifact, or reverberation were excluded", Experimental Methods - Images and their annotation). This clearly indicates that (a) one of ordinary skill in the art would want to review all images (“All IVOCT image frames”) to identify blood blockages; (b) the distinction between acceptable and unacceptable images can be done at scale; and (c) poor quality images due to blood obstruction would render the images unusable for their intended purpose.
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Lee does not teach outputting for display on a user interface, in real time or near real time, an indication of the identified image quality.
However, Rothberg teaches outputting for display on a user interface, in real time or near real time, an indication of the identified image quality ("FIG. 6 illustrates an example of a graphical user interface (GUI) 600 that may be displayed by a display screen of a computing device during imaging with an ultrasound device in communication with the computing device, in accordance with certain embodiments described herein. The GUI 600 displays an ultrasound image 602, a live quality indicator 604, a record option 614, and an instruction 616. The live quality indicator 604 includes a bar 606 having a first end 608 and a second end 610, a slider 618, an acceptability indicator 620, and text 612.", Paragraph [0085]; “The live quality may be calculated in real-time during imaging, as the images are collected. The process 200 begins with act 202”, Paragraph [0054]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lee, to incorporate outputting, in real time or near real time, an indicator of image quality, as disclosed by Rothberg. The suggestion/motivation for doing so would have been to determine which images to keep or discard before performing further analysis and thereby improve the efficiency of image collection.
Claim 3
Regarding Claim 3, dependent on claim 1, Lee, in view of Rothberg, teaches the invention as claimed in claim 1.
Lee further teaches wherein the series of diagnostic medical images is obtained through an optical coherence tomography pullback (Rejected as applied to claim 1).
Claim 4
Regarding Claim 4, dependent on claim 1, Lee, in view of Rothberg, teaches the invention as claimed in claim 1.
Lee further teaches classifying the diagnostic medical images as a first classification or a second classification ("If an A-line included ≥3 pixels of either lipidous or calcified plaques, we determined which of these two classes was in the majority, and then labeled the A-line accordingly as fibrolipidic or fibrocalcific.", Experimental Methods - Images and their annotations).
Claim 5
Regarding Claim 5, dependent on claim 4, Lee, in view of Rothberg, teaches the invention as claimed in claim 4.
Lee further teaches providing an alert or notification when the diagnostic medical images are classified in the second classification (Figure 6; "Hybrid classification followed by CRF gave good A-line classification results. The proposed method gave better classification results for the fibrolipidic class (F1 score ≈ 0.887) as compared to those in fibrocalcific class (F1 score ≈ 0.677).", Results), wherein the different colors in Figure 6 directly below notify the user of the classification since the classification is color dependent.
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Claim 6
Regarding Claim 6, dependent on claim 1, Lee, in view of Rothberg, teaches the invention as claimed in claim 1.
Lee further teaches wherein the set of annotated diagnostic medical images comprises annotations including clear, blood, or guide catheter ("In addition, we also measured clinical plaque attributes, such as arc angle and length, to provide a more clinically meaningful analysis (Fig. 4). The arc angle was measured from the center of the catheter to the boundaries of plaque A-lines.", Experimental Methods - Performance evaluation).
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Claim 7
Regarding Claim 7, dependent on claim 1, Lee, in view of Rothberg, teaches the invention as claimed in claim 1.
Lee further teaches wherein the diagnostic medical images are an optical coherence tomography images (Rejected as applied to claim 1).
Claim 9
Regarding Claim 9, dependent on claim 1, Lee, in view of Rothberg, teaches the invention as claimed in claim 1.
Lee does not teach computing a probability indicative of whether the diagnostic medical images are acceptable or not acceptable.
However, Rothberg teaches computing a probability indicative of whether the diagnostic medical images are acceptable or not acceptable (“In some embodiments, the method further includes generating for display, during the imaging, an indicator of an acceptability of the sequence of images, where the indicator of the acceptability of the sequence of images includes an indicator of a threshold quality such that a distance from the first end to the indicator of the threshold quality relative to the length of the bar is proportional to the threshold quality.”, Paragraph [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee, in view of Rothberg, to incorporate computing a probability indicative of the diagnostic images being acceptable or not, as disclosed by Rothberg. The suggestion/motivation for doing so would have been to determine, from the images taken, which images would be most viable for further analysis and removing those images that are prone to error or could potentially lead to misdiagnosis.
Claim 10
Regarding Claim 10, dependent on claim 9, Lee, in view of Rothberg, teaches the invention as claimed in claim 9.
Lee does not teach using a threshold method to convert the computed probability to a classification of the diagnostic medical images.
However, Rothberg teaches using a threshold method to convert the computed probability to a classification of the diagnostic medical images (“If the quality is above a threshold quality (e.g., 50% on a scale of 0% to 100%), automatic measurement based on the sequence of images may proceed. If the quality is below the threshold quality, an error may be displayed indicating that automatic measurement will not proceed.”, Paragraph [0044]).
Claim 12
Regarding Claim 12, dependent on claim 9, Lee, in view of Rothberg, teaches the invention as claimed in claim 9.
Lee further teaches using morphological classification to convert the computed probability to a classification of the diagnostic medical images (Figure 8; “Since A-line classification does not fully consider spatial similarities between nearby A-lines in θ and z, we employed classification noise cleaning. In particular, we implemented a fully connected CRF that defines the pairwise edge potentials of each probabilistic classification (θ, z) by a linear combination of Gaussian kernels39.”, Image Analysis Methods – Classification noise reduction using fully connected conditional random field (CRF); “Adding lumen morphological features gave statistically significant improvement (p < 0.05), as compared to classification with convolutional features alone.”, Abstract).
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Claim 13
Regarding Claim 13, dependent on claim 9, Lee, in view of Rothberg, teaches the invention as claimed in claim 9.
Lee does not teach wherein acceptable means that the diagnostic medical images are above a predefined threshold quality which allows for evaluation of characteristics of human tissue above a threshold level of accuracy or confidence.
However, Rothberg teaches wherein acceptable means that the diagnostic medical images are above a predefined threshold quality which allows for evaluation of characteristics of human tissue above a threshold level of accuracy or confidence ('If the quality is above a threshold quality (e.g., 50% on a scale of 0% to 100%), automatic measurement based on the sequence of images may proceed. If the quality is below the threshold quality, an error may be displayed indicating that automatic measurement will not proceed.", Paragraph [0044]).
Claim 17
Regarding Claim 17, dependent on claim 16, Lee, in view of Rothberg, teaches the invention as claimed in claim 16.
Lee further teaches wherein the instructions are configured to display a plurality of OCT images along with an indicator associated with a classification of each image of the plurality of OCT images (Figure 6), where specific colors in the image "indicate" the classification type.
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Claim 15, an independent system claim, is rejected for the same reasons as applied to claim 1.
Claim 19, an independent non-transitory computer readable medium claim, is rejected for the same reason as applied to claim 1.
Claims 16, 18, and 20 are rejected for the same reasons as applied to the above claims.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature “Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features” to Lee et al. (hereinafter Lee) in view of Rothberg et al. (US Publication No. 20190130554 A1) (hereinafter Rothberg) and in further view of WANG et al. (US Publication No. 20240005501 A1) (hereinafter WANG).
Claim 8
Regarding Claim 8, dependent on claim 9, Lee, in view of Rothberg, teaches the invention as claimed in claim 9.
Neither Lee, or Rothberg, or the combination teach classifying the diagnostic medical images as a clear medical image or a blood medical image.
However, WANG teaches classifying the diagnostic medical images as a clear medical image or a blood medical image ("The inventors therefore considered the significance of tissue pathology of wound as well as the frequency of morphological appearances in typical skin OCT images, to identify a plurality of classes that could be associated with improved accuracy. They decided to define seven distinctive image sub-types within the OCT image of a skin wound, namely neoepidermis, clot, granular tissue, collagen, intact tissue, blood (liquid) and outside (also referred to as “void” or “background”).", Paragraph [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee, in view of Rothberg, to incorporate classifying the diagnostic medical images as a clear medical image or a blood medical image, as disclosed by WANG. The suggestion/motivation for doing so would have been to determine if an image contained residual blood so as to discard the image to reduce error during patient diagnosis and medical image analysis.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature “Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features” to Lee et al. (hereinafter Lee) in view of Rothberg et al. (US Publication No. 20190130554 A1) (hereinafter Rothberg) and in further view of Wang et al. (US Publication No. 20160213253 A1) (hereinafter Wang).
Claim 11
Regarding Claim 11, dependent on claim 9, Lee, in view of Rothberg, teaches the invention as claimed in claim 9.
Neither Lee, or Rothberg, or the combination teach using graph cuts to convert the computed probability to a classification of the diagnostic medical images.
However, Wang teaches using graph cuts to convert the computed probability to a classification of the diagnostic medical images ("A final probability estimate of strut presence can then be determined based on the interpolated strut depths and the high confidence struts that have been identified. In some examples, the depth calculator 52 can use a graph cut method to localize depths of stent struts based on the OCT image data 38.", Paragraph [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee, in view of Rothberg, to incorporate using graph cuts to covert the computed probability to a classification of the diagnostic medical images, as disclosed by Wang. The suggestion/motivation for doing so would have been to allow for a more precise classification and reduce error and misdiagnosis of the captured medical images.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature “Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features” to Lee et al. (hereinafter Lee) in view of Rothberg et al. (US Publication No. 20190130554 A1) (hereinafter Rothberg) and in further view of Fridley et al. (US Publication No. 20220260988 A1) (hereinafter Fridley).
Claim 14
Regarding Claim 14, dependent on claim 13, Lee, in view of Rothberg, teaches the invention as claimed in claim 13.
Neither Lee, or Rothberg, or the combination teach wherein a value for the predefined threshold quality is determined by optimizing a machine learning model.
However, Fridley teaches wherein a value for the predefined threshold quality is determined by optimizing a machine learning model ("For example, the inference subsystem 130 can implement machine learning models that are configured to (i) optimize the threshold at which anomalies are predicted or detected.", Paragraph [0062]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee, in view of Rothberg, to incorporate determining a value for a predefined threshold quality by optimizing a machine learning model, as disclosed by Fridley. The suggestion/motivation for doing so would have been to train the machine learning model with various batches of training data, optimizing the threshold until a desired success rate was achieved so as to reduce error in medical image classifications.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shiju Joseph, Asif Adnan, David Adlam, "Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method," J. Med. Imag. 3(4) 044001 (29 November 2016) https://doi.org/10.1117/1.JMI.3.4.044001, where Joseph also discloses annotated OCT images of a lumen.
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Huaizhong Zhang et al., “Automatic vessel lumen segmentation in optical coherence tomography (OCT) images”. Applied Soft Computing Journal (2020). https://doi.org/10.1016/j.asoc.2019.106042, where Zhang also discloses annotated OCT images of a lumen.
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Grigorios-Aris Cheimariotis et al., “ARC-OCT: Automatic detection of lumen border in intravascular OCT images”. Computer Methods and Programs in Biomedicine (2017). https://doi.org/10.1016/j.cmpb.2017.08.007
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00.
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/RONDE LEE MILLER/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698