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 .
Response to Arguments/Amendments
The amendment filed 07/07/2026 in response to the Non-Final Office Action mailed on 04/15/2026 has been entered.
Claims 1 and 3-13 are currently pending in U.S. Patent Application No. 18/426,070.
Applicants’ remarks filed 07/07/2026 have been fully considered and responded to below.
The claim objections with respect to claim 1 have been removed in view of the claim amendments.
The claim rejections under 35 U.S.C. 112(b) for claims 1 and 3-13 have been removed in view of the claim amendments.
Regarding the 35 U.S.C. 103 rejections, the Applicant’s remarks have been fully considered but are moot because the new grounds of rejection regarding the amended limitation no longer relies on the combination of references presented in the Non-Final Rejection. A change in scope necessitated by the Applicant’s amendments has led to an updated search revealing new art. However, the Examiner specifically notes the remarks made by the Applicant on pages 10-11 with regards to Claim 1. The Applicant notes that Yamada is silent on the “identifying multiple partial image data sets” limitation noted in claim 1 and specifically points to [0020] from the Applicant’s specification for support of the amended limitations. The Examiner notes that [0020] from the Applicant’s specification states that the identification can be based on acquisition times and may include “selection and/or annotation and/or provision”. Yamada in [0045] and [0055-0057] discloses identifying the arterial layer and the venous layer based on timing, which is similar to the Applicant’s specification (as noted in the 35 U.S.C. 103 rejection presented below).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 9-10, and 13 are rejected as being unpatentable over Yamada (US 2005/0283066; hereinafter “Yamada”) in view of Su et al. (“autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients”, DOI: 10.1109/TMI.2021.3077113, Publication Year: 2021; hereinafter “Su” ).
Regarding Claim 1, Yamada discloses a method for providing a result data set, the method comprising (see Fig. 3, Yamada):
capturing a first medical image data set that maps an object under examination within a first temporal phase ([0049], Yamada discloses capturing mask images.);
capturing a second medical image data set that maps a flow of contrast agent in the object under examination within a second temporal phase in a time-resolved manner ([0052-0053], Yamada discloses obtaining images after a contrast medium is injected (i.e., contrast images).);
providing the result data set comprising multiple subtraction image data sets ([0061-0062], Yamada discloses obtaining result images by performing subtraction between the mask images and the arterial and venous layer images.),
wherein each subtraction image data set of the multiple subtraction image data sets is determined based on a difference between the first medical image data set and an individual partial image data set of the multiple partial image data sets in the second medical image data set ([0061-0062], Yamada discloses obtaining result images by performing subtraction between the mask images and each of the arterial and venous layer images.).
Yamada does not explicitly disclose identifying multiple partial image data sets in the second medical image data set, wherein each partial image data set of the multiple partial image data sets maps one of multiple physiological subphases within the second temporal phase, and wherein identifying each partial image data set of the multiple partial image data sets comprises selecting, annotating, and/or providing the partial image data set in the second medical image data set (The Examiner does note [0045], [0055-0057], wherein Yamada discloses identifying the arterial layer and the venous layer based on timing (i.e., the arterial layer is based on an estimate on how long it will take the contrast media to reach the arteries and the venous layer is based on an estimate on how long it will take for the contrast media to reach the veins from the arteries.). The Examiner further notes how this is similar to the Applicant’s specification in [0020], wherein acquisition timing is used as a way to identify partial datasets. The Examiner does not necessarily concede or acquiesce that Yamada does not disclose the aforementioned limitation, but for the clarity of the record, the Examiner will present the Su reference below.).
Su discloses identifying multiple partial image data sets in the second medical image data set, wherein each partial image data set of the multiple partial image data sets maps one of multiple physiological subphases within the second temporal phase, and wherein identifying each partial image data set of the multiple partial image data sets comprises selecting, annotating, and/or providing the partial image data set in the second medical image data set (Figs. 1-2, A. Phase Classification, Su discloses using a trained model to annotate images into different phases.).
Yamada and Su are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Yamada such that it incorporated Su’s explicit definitions of different DSA phases in order to annotate images based on contrast flow. The motivation for this being the ability to automate the classification process of contrast images.
Claim 13 is the device claim corresponding to claim 1 and is similarly rejected (see Figs 1-2, Yamada).
Regarding Claim 4, Yamada in view of Su teaches the method of claim 1, wherein the physiological subphases comprise an arterial phase, a parenchymal phase, a venous phase, or a combination thereof (Figs. 1-2, A. Phase Classification, Su discloses using a trained model to annotate images into different phases. Also see [0045], [0052-0057], wherein Yamada discloses obtaining contrast images during an arterial phase and a venous phase).
Regarding Claim 5, Yamada in view of Su teaches the method of claim 1, wherein multiple comparison partial image data sets are identified in a training image data set, wherein each comparison partial image data set maps one of multiple physiological subphases, wherein the multiple comparison partial image data sets are identified based on: respective acquisition times of each respective comparison partial image data set, based on differences between the respectively mapped flow of the contrast agent, by annotation in the medical training image data set, or combinations thereof (Section III. Data and Annotation, B. Data Annotation, Su discloses performing manual phase labelling to generate ground truth data (i.e., comparison partial image data), which maps each DSA image to a phase (non-contrast, arterial, parenchymal, and venous) based on flow of the contrast agent),
wherein the identifying of the multiple partial image data sets comprises applying a trained function to input data (A. Phase Classification, Su discloses a CNN (i.e., a trained function) applied to DSA images (i.e., input data) for classification into different phases (arterial, parenchymal, and venous).),
wherein the input data is based on the second medical image data set (See Fig. 2, Su discloses an input of a DSA sequence.),
wherein at least one parameter of the trained function is adjusted based on a comparison of training partial image data sets with the multiple comparison partial image data sets (A. Phase Classification, Section III. Data and Annotation, Su discloses utilizing a phase classification dataset to train a modified ResNet18 model. The Examiner notes that the process of training a CNN model (such as ResNet18) involves adjusting the weights/parameters of the model based on a loss function which compares the result image from a model to a ground truth image.), and
wherein the multiple partial image data sets are provided as output data of the trained function (See Fig. 1, the output from phase classification includes arterial phase and parenchymal phase images used for further processing.).
Regrading Claim 9, Yamada in view of Su teaches the method of claim 1, wherein the multiple partial image data sets are identified in the second medical image data set based on respective acquisition times of the multiple partial image data sets ([0052], [0056], Yamada discloses obtaining images from the arterial phase or from the venous phase based on the start timing obtained by the timing-retaining unit. Also see 1) Phase Definition, wherein Su discloses specific contrast phase definitions based on time.).
Regarding Claim 10, Yamada in view of Su teaches the method of claim 1, wherein each partial image data set of the multiple partial image data sets is identified based on differences between the mapped flow of the contrast agent in the second medical image data set (B. Data Annotation, Su discloses the identifying the phases by manually annotating images, wherein the phases (non-contrast, arterial, parenchymal, and venous) are based on contrast flow (as defined in Section II-A.1).).
Claim 3 is rejected as being unpatentable over Yamada in view of Su in view of Kump et al. (“Digital subtraction peripheral angiography using image stacking: initial clinical results”, DOI: 10.1118/1.1350676, Publication Year: 2001; hereinafter “Kump”).
Regarding Claim 3, Yamada in view of Su teaches the method of claim 1, (Figs. 1-2, A. Phase Classification, Su discloses using a trained model to annotate images into different phases. Also see [0045], [0052-0057], wherein Yamada discloses capturing images of arterial and venous subphases (i.e., physiological subphase).), and wherein each subtraction image data set of the multiple subtraction image data sets is determined as a difference between the first medical image data set and the ([0061-0062], Yamada discloses obtaining result images by performing subtraction between the mask images and the arterial and venous layer images.).
Yamada in view of Su does not explicitly teach wherein the providing of the result data set comprises determining a maximum opacity image for each partial image data set of the multiple partial image data sets, the determining comprising a determination, image point by image point within each partial image data set, of a maximum opacity along a temporal dimension within a physiological subphase (italicized for context).
Kump discloses wherein the providing of the result data set comprises determining a maximum opacity image for each partial image data set of the multiple partial image data sets, the determining comprising a determination, image point by image point within each partial image data set, of a maximum opacity along a temporal dimension within a physiological subphase (italicized for context) (II. Image Processing Algorithms – 1. Maximum Opacification, Kump discloses generating a maximum opacification image
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based on the minimum pixel value (i.e., the minimum pixel value gives the maximum opacity) over all input images.).
Yamada, Su, and Kump are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Yamada in view of Su such that the series of partial image datasets representing different physiological subphases (as disclosed by Yamada in view of Su), are processed by utilizing the maximum opacification method disclosed by Kump to generate a maximum opacification image per physiological subphase, prior to the image subtraction process disclosed by Yamada in view of Su. The motivation for this combination being the ability to visually enhance the images by maximizing opacity, which can improve downstream processing.
Claims 6-8 are rejected as being unpatentable over Yamada in view of Su in view of Tache et al. (“Preliminary Results for Automatic Detection of Arterio-Venous Malformations from Medical Images”, DOI: 10.1109/CSCS.2013.82, Publication Year: 2013; hereinafter “Tache”).
Regarding Claim 6, Yamada in view of Su teaches the method of claim 1.
Yamada in view of Su does not explicitly teach wherein the providing of the result data set comprises displaying a graphical representation of the result data set by a display unit.
Tache discloses wherein the providing of the result data set comprises displaying a graphical representation of the result data set by a display unit (Fig. 4, D. Technical Limitations, Tache discloses transferring medical images to a PC where they can be visualized. The Examiner notes the usage of a PC along with specific visualization software (AngioProcess) involves a display unit.).
Yamada, Su, and Tache are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Yamada in view of Su such that it incorporated Tache’s usage of a PC and display. The motivation for this combination being the ability to visualize the result data set.
Regarding Claim 7, Yamada in view of Su in view of Tache teaches the method of claim 6, wherein the graphical representation of the result data set comprises a color-coded representation, a superimposed representation, a coordinated representation, a sequential representation, or a combination thereof of the subtraction image data sets (see Fig. 4, where Tache discloses the visualization of a linear subtraction image, which includes pixel coordinates as well as an “color image” option.).
Regarding Claim 8, Yamada in view of Su teaches the claim of method 1.
Yamada in view of Su does not explicitly teach wherein the providing of the result data set comprises a registration of each partial image data set to be subtracted and of the first medical image data set.
Tache discloses wherein the providing of the result data set comprises a registration of each partial image data set to be subtracted and of the first medical image data set (B. Digital Subtraction Angiography, Tache discloses performing spatial shifting (i.e., registering) of the mask image relative to the live image for improved registration of common features between the two images.).
Yamada, Su, and Tache are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Yamada in view of Su such that it incorporated Tache’s implementation of image registration between mask images and live images. The motivation for this combination being the ability remove artifacts caused by subject motion during imaging.
Claim 11 is rejected as being unpatentable over Hashimoto et al. (US 2023/0309941; hereinafter “Hashimoto”) in view of Yamada in view of Su.
Regarding Claim 11, Hashimoto discloses a method for providing a comparison data set, the method comprising:
providing a first result data set by, at a first time ([0059], [0061], Hashimoto discloses obtaining a DSA image prior to a procedure.):
providing a second result data set by, at a second time ([0059], [0061], Hashimoto discloses obtaining a DSA image after a procedure.):
wherein a change in the object under examination has taken place between the first time and the second time ([0053], [0059-0061], Hashimoto discloses obtaining DSA images before and after a procedure.); and
providing the comparison data set comprising a difference between a subtraction image data set of the first result data set and of the second result data set that map a same physiological subphase ([0065], Hashimoto discloses obtaining a difference image by subtracting X-ray images obtained before and after a procedure.).
Hashimoto does not disclose the explicit steps involved in obtaining a first result data set (“capturing a first medical image data set that maps an object under examination within a first temporal phase; capturing a second medical image data set that maps a flow of contrast agent in the object under examination within a second temporal phase in a time-resolved manner; and identifying multiple partial image data sets in the second medical image data set, wherein each partial image data set of the multiple partial image data sets maps one of multiple physiological subphases within the second temporal phase, wherein identifying each partial image data set of the multiple partial image data sets comprises selecting, annotating, and/or providing the partial image data set in the second medical image data set, wherein the first result data set comprises multiple subtraction image data sets, and wherein each subtraction image data set of the multiple subtraction image data sets is determined based on a difference between the first medical image data set and an individual partial image data set of the multiple partial image data sets in the second medical image data set;”) and a second result data set (“capturing an additional first medical image data set that maps the object under examination within the first temporal phase; capturing an additional second medical image data set that maps the flow of the contrast agent in the object under examination within the second temporal phase in the time-resolved manner; and identifying additional multiple partial image data sets in the additional second medical image data set, wherein each partial image data set of the additional multiple partial image data sets maps one physiological subphase of multiple physiological subphases within the second temporal phase, wherein the second result data set comprises additional multiple subtraction image data sets, wherein each subtraction image data set of the additional multiple subtraction image data sets is determined based on the additional first medical image data set and a respective partial image data set of the additional multiple partial image data sets in the additional second medical image data set,”) (The Examiner asserts that the steps to obtain the “first result data set” and the “second result data set” are the same steps being performed at two different times/under two different conditions.).
Yamada discloses capturing a first medical image data set that maps an object under examination within a first temporal phase ([0049], Yamada discloses capturing mask images.);
capturing a second medical image data set that maps a flow of contrast agent in the object under examination within a second temporal phase in a time-resolved manner ([0052-0053], Yamada discloses obtaining images after a contrast medium is injected (i.e., contrast images).); and
wherein the first result data set comprises multiple subtraction image data sets, and wherein each subtraction image data set of the multiple subtraction image data sets is determined based on a difference between first medical image data set and an individual partial image data set of the multiple partial image data sets in the second medical image data set ([0061-0062], Yamada discloses obtaining result images by performing subtraction between the mask images and the arterial and venous layer images.);
providing a second result data set by, at a second time (italicized for context) ([0035], [0044], [0046], [0071], The Examiner notes that Yamada’s disclosure is not limited to being performed only once or only for one patient, and that the method disclosed in Fig. 3 and Fig. 5 can be performed a plurality of times.)
capturing an additional first medical image data set that maps the object under examination within the first temporal phase ([0049], Yamada discloses capturing mask images.);
capturing an additional second medical image data set that maps the flow of the contrast agent in the object under examination within the second temporal phase in the time-resolved manner ([0052-0053], Yamada discloses obtaining images after a contrast medium is injected (i.e., contrast images).); and
wherein the second result data set comprises additional multiple subtraction image data sets, wherein each subtraction image data set of the additional multiple subtraction image data sets is determined based on the additional first medical image data set and a respective partial image data set of the additional multiple partial image data sets in the additional second medical image data set ([0061-0062], Yamada discloses obtaining result images by performing subtraction between the mask images and the arterial and venous layer images.),
Hashimoto and Yamada are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Hashimoto such that the DSA images obtained before and after a procedure are obtained using the method disclosed by Yamada (which includes specific distinction in different contrast phases), and consequently the difference image calculated by Hashimoto is based on the phase-specific DSA images disclosed by Yamada. The motivation for this combination being the ability to specifically monitor how contrast flow is affected by a procedure.
Hashimoto in view of Yamada does not explicitly teach identifying multiple partial image data sets in the second medical image data set, wherein each partial image data set of the multiple partial image data sets maps one of multiple physiological subphases within the second temporal phase, wherein identifying each partial image data set of the multiple partial image data sets comprises selecting, annotating, and/or providing the partial image data set in the second medical image data set (The Examiner notes that the comments made in claim 1 regarding Yamato’s disclosure and this limitation also applies here.).
Su discloses identifying multiple partial image data sets in the second medical image data set, wherein each partial image data set of the multiple partial image data sets maps one of multiple physiological subphases within the second temporal phase, wherein identifying each partial image data set of the multiple partial image data sets comprises selecting, annotating, and/or providing the partial image data set in the second medical image data set (Figs. 1-2, A. Phase Classification, Su discloses using a trained model to annotate images into different phases.).
Hashimoto, Yamada, and Su are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to angiography images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Hashimoto in view of Yamada such that it incorporated Su’s explicit definitions of different DSA phases in order to annotate images based on contrast flow, and consequently apply this annotation/classification to the first and second data sets taught by Hashimoto in view of Yamada. The motivation for this being the ability to automate the classification process of contrast images.
Claim 12 is rejected as being unpatentable over Suehling et al. (US20110002520; hereinafter “Suehling”) in view of Su.
Regarding Claim 12, Suehling discloses a computer-implemented method for providing a trained function, the method comprising (see [0025-0032]):
capturing a medical training image data set that maps a flow of contrast agent in an object under examination in a time-resolved manner such that the medical training image data set comprises a filling image ([0005-0006], [0017], [0025-0032], Suehling discloses obtaining contrast images (i.e., filling images).);
identifying multiple comparison partial image data sets in the medical training image data set prior to digital subtraction, wherein each comparison partial image data set maps one of multiple physiological subphases, wherein the multiple comparison partial image data sets are identified based on: respective acquisition times of each respective comparison partial image data set, differences between the respectively mapped flow of the contrast agent, by annotation in the medical training image data set, or combinations thereof ([0032], Suehling discloses manually annotating contrast images with phase information using a clinical expert.);
Suehling does not explicitly disclose identifying multiple training partial image data sets by applying the trained function to input data, wherein the input data is based on the training image data set, and wherein the multiple training partial image data sets are provided as output data of the trained function; adjusting at least one parameter of the trained function based on a comparison of the multiple training partial image data sets with the comparison partial image data sets; and providing the trained function.
Su discloses identifying multiple training partial image data sets by applying the trained function to input data, wherein the input data is based on the training image data set, and wherein the multiple training partial image data sets are provided as output data of the trained function (A. Phase Classification, Su discloses a phase classification CNN model (i.e., a trained function) which takes input of training data and outputs phase-classified data. Also see [0005-0006], [0017], [0025-0032], wherein Suehling discloses applying a trained classifier to training image data to determine a contrast phase classification.););
adjusting at least one parameter of the trained function based on a comparison of the multiple training partial image data sets with the comparison partial image data sets; and providing the trained function (Fig. 2, A. Phase Classification, Su discloses utilizing a phase classification dataset to train a modified ResNet18 model (representing the phase classification CNN model). The Examiner notes that the process of training a CNN model (such as ResNet18) involves adjusting the weights/parameters of the model based on a loss function which compares the result image from a model to a ground truth image. Furthermore, the trained CNN model is one network in the greater DL pipeline shown in Fig. 1.).
Suehling and Su are considered to be analogous to the claimed invention as they are in the same field of applying image processing methods to contrast images. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Suehling such that the training data based on filling images disclosed by Suehling are used as input training data to train the phase classification models disclosed by Su. The motivation for this combination being the ability to utilize a well-established CNN model to determine contrast phase classification.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/PROMOTTO TAJRIAN ISLAM/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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