DETAILED ACTION
A 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 Amendment
Title of the Invention
Applicant’s amendment, see Applicant’s Remarks [Title Objections; pg. 7], filed 4/15/2026 with respect to the objection to the title has been fully considered and is persuasive. The objection to the title has been withdrawn. The examiner acknowledges the new title is sufficiently descriptive.
Rejections under 35 U.S.C. § 112(b)
Applicant’s amendment, see Applicant’s Remarks [Title Objections; pg. 7], filed 4/15/2026 with respect to the rejection(s) of claim(s) 1-6 under 35 U.S.C. § 112(b) to the title has been fully considered and is persuasive. The rejections under 35 U.S.C. § 112(b) have been withdrawn. The examiner acknowledges the amendments now clearly distinguish a first medical image in which an indirect finding has not occurred, and a second, synthesized medical image in which an indirect finding is generated at a determined position of the first medical image.
Response to Arguments
Applicant’s arguments, see Applicant's Remarks, pgs. 8-12, filed 4/15/2026, with respect to the rejection(s) of claim(s) 1-6 under 35 U.S.C. § 102 and 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. The examiner agrees that applicant’s argument that the original rejection under 35 U.S.C. § 102 in view of Osawa; Akira does not teach the currently amended claims, and that Osawa is directed towards detection, classification and labeling of existing findings, in contrast with the simulated manifestation associated with the occurrence of a lesion of the invention currently claimed. The examiner notes that the proposed amendments change the scope of the claims.
Upon further consideration, a new ground(s) of rejection is made in view of Nickisch et al (EP 3982324 A1), Zangooei et al (“Multiscale computational modeling of cancer growth using features derived from microCT images”, Scientific Reports, 2021), and Liu et al (US 2021/0327054 A1) – see claim rejections under 35 U.S.C. § 103 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.
Claim(s) 1, 5 & 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nickisch et al (EP 3982324 A1), hereinafter referred to as “Nickisch”, in view of Zangooei et al (“Multiscale computational modeling of cancer growth using features derived from microCT images”, Scientific Reports, 2021), hereinafter referred to as “Zangooei”.
Regarding claim 1, Nickisch disclose a system for generating synthetic images of blood vessel legions to be used as training material for lesion analysis. More particularly, Nickisch teach An image processing apparatus (processing system 60 [¶0138; Fig. 6]) comprising:
at least one processor (one or more processors 61 [¶0138; Fig. 6]),
wherein the processor
acquires a first medical image in which an indirect finding has not occurred (Obtaining an image 190 of blood vessels in a region of interest - the examiner notes these images have no lesions or indirect findings associated with them [¶0046; step 110 of Fig. 1]),
acquires input information including a position of a lesion, (a location for each lesion is defined based on a determined location of the blood vessel(s) in the image so a synthetic lesion can be accurately positioned within the blood vessel [¶0055; step 131 of Fig. 1]).
While Nickisch discloses generating a second medical image using the input information, they do not teach determining a position of an indirect finding, nor does it generate a second medical image of said indirect finding. Zangooei, however is analogous art pertinent to the field of endeavor and disclose a computational model for simulating angiogenesis associated with a blood vessel lesion. Zangooei teach determines a position of the indirect finding associated with occurrence of the lesion based on the input information including the position of the lesion (Zangooei: microvascular segments (shown in green), are simulated to grow over time based on spatial input data from in vivo contrast-enhanced microCT images of a tumor and its vascular network in its early stages – the examiner notes that here, microvascular segments are treated as an indirect finding with their aberrant growth simulating early stage (time = 24 & 144 hrs), prior to the lesion being easily perceivable via microCT, and late stage (time = 576 & 720 hrs), when the lesion has growth significantly and would be more visible via microCT [Sec Results - 02-4; Fig. 1]), and
generates a second medical image in which the indirect finding is generated at the determined position in the first medical image (Zangooei: simulated microvascular segments are generated based on the defined position of the tumor and vascular network from the microCT images [Sec Results - 02-4; Figs. 1]).
Zangooei additionally discloses the need for a computational model to simulation angiogenesis associated with the occurrence of a lesion to inform the overall evolution of tumor phenotype and drug delivery [03-04]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the computational simulation of the microvasculature around a tumor as it develops proposed by Zangooei with the synthetic lesion generation system of Nickisch to arrive at the invention of the instant application.
With respect to claim 5, Nickisch teach An image processing method (Nickisch: the method 100 outlined in Fig. 1 [¶0045]) executed by a processor (Nickisch: one or more processors 61 [¶0138; Fig. 6]) of an image processing apparatus (Nickisch: processing system 60 [¶0138; Fig. 6]), the method comprising:
acquiring a first medical image in which an indirect finding has not occurred (Nickisch: btaining an image 190 of blood vessels in a region of interest - the examiner notes these images have no lesions or indirect findings associated with them [¶0046; step 110 of Fig. 1]),
acquiring input information including a position of a lesion, (Nickisch: a location for each lesion is defined based on a determined location of the blood vessel(s) in the image so a synthetic lesion can be accurately positioned within the blood vessel [¶0055; step 131 of Fig. 1]).
Again, Nickish discloses generating a second medical image using the input information, but they do not teach determining a position of an indirect finding, nor does it generate a second medical image of said indirect finding. Zangooei, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose a computational model for simulating angiogenesis associated with a blood vessel lesion. Zangooei teach determining a position of the indirect finding associated with occurrence of the lesion based on the input information including the position of the lesion (Zangooei: microvascular segments (shown in green), are simulated to grow over time based on spatial input data from in vivo contrast-enhanced microCT images of a tumor and its vascular network in its early stages – the examiner notes that here, microvascular segments are treated as an indirect finding with their aberrant growth simulating early stage (time = 24 & 144 hrs), prior to the lesion being easily perceivable via microCT, and late stage (time = 576 & 720 hrs), when the lesion has growth significantly and would be more visible via microCT [Sec Results - 02-4; Fig. 1]); and
generating a second medical image in which the indirect finding is generated at the determined position in the first medical image (Zangooei: simulated microvascular segments are generated based on the defined position of the tumor and vascular network from the microCT images [Sec Results - 02-4; Figs. 1]).
Zangooei additionally discloses the need for a computational model to simulation angiogenesis associated with the occurrence of a lesion to inform the overall evolution of tumor phenotype and drug delivery [03-04]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the computational simulation of the microvasculature around a tumor as it develops proposed by Zangooei with the synthetic lesion generation system of Nickisch to arrive at the invention of the instant application.
Considering claim 6, Nickisch A non-transitory computer-readable storage medium (Nickisch: memory 62, which can include ROM, capable of storing application 66 [¶0139-0144; Fig. 6]) storing an image processing program (Nickisch: the method 100 outlined in Fig. 1 [¶0045]) for causing a processor (Nickisch: one or more processors 61 [¶0138; Fig. 6]) of an image processing apparatus (Nickisch: processing system 60 [¶0138; Fig. 6]) to execute:
acquiring a first medical image in which an indirect finding has not occurred (Nickisch: btaining an image 190 of blood vessels in a region of interest - the examiner notes these images have no lesions or indirect findings associated with them [¶0046; step 110 of Fig. 1]),
acquiring input information including a position of a lesion, (Nickisch: a location for each lesion is defined based on a determined location of the blood vessel(s) in the image so a synthetic lesion can be accurately positioned within the blood vessel [¶0055; step 131 of Fig. 1]).
Again, Nickish discloses generating a second medical image using the input information, but they do not teach determining a position of an indirect finding, nor does it generate a second medical image of said indirect finding. Zangooei, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose a computational model for simulating angiogenesis associated with a blood vessel lesion. Zangooei teach determining a position of the indirect finding associated with occurrence of the lesion based on the input information including the position of the lesion (Zangooei: microvascular segments (shown in green), are simulated to grow over time based on spatial input data from in vivo contrast-enhanced microCT images of a tumor and its vascular network in its early stages – the examiner notes that here, microvascular segments are treated as an indirect finding with their aberrant growth simulating early stage (time = 24 & 144 hrs), prior to the lesion being easily perceivable via microCT, and late stage (time = 576 & 720 hrs), when the lesion has growth significantly and would be more visible via microCT [Sec Results - 02-4; Fig. 1]); and
generating a second medical image in which the indirect finding is generated at the determined position in the first medical image (Zangooei: simulated microvascular segments are generated based on the defined position of the tumor and vascular network from the microCT images [Sec Results - 02-4; Figs. 1]).
Zangooei additionally discloses the need for a computational model to simulation angiogenesis associated with the occurrence of a lesion to inform the overall evolution of tumor phenotype and drug delivery [03-04]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the computational simulation of the microvasculature around a tumor as it develops proposed by Zangooei with the synthetic lesion generation system of Nickisch to arrive at the invention of the instant application.
Claim(s) 2-4 & 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nickisch et al (EP 3982324 A1), hereinafter referred to as “Nickisch”, in view of Zangooei et al (“Multiscale computational modeling of cancer growth using features derived from microCT images”, Scientific Reports, 2021), hereinafter referred to as “Zangooei”, further in view of Liu et al (US 2021/0327054 A1), hereinafter referred to as “Liu”.
As for claim 2, Nickisch in view of Zangooei teach The image processing apparatus according to claim 1 (described above), but do not explicitly teach input information containing the position of a known lesion in an abnormal image.
Liu, contrastingly, disclose a system and method for generating synthetic training images of abnormality patterns associated with COVID-19. Liu teach wherein the input information including the position of the lesion is a position of the lesion in an abnormal medical image in which the lesion has occurred (Liu: training images 802, containing known positions of abnormality patterns (lesions) associated with COVID-19 are fed into framework 800, with known positions of lesions masked in manually annotated masks 804 to provide masked training images 806 [¶0052-54; Fig. 8]).
Liu further disclose that their framework for training a machine learning based generator network architecture fills a need for providing training data for machine learning based systems for correlating the severity and progression of Covid-19 [0005]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the medical image synthesis framework disclosed by Liu, and incorporate it with lesion and microvascular image synthesis system proposed by Nickisch in view of Zangooei to arrive at the invention of the instant application.
Concerning claim 3, Nicksich in view of Zangooei teach The image processing apparatus according to claim 1 (as described previously), but do not teach input information in the form of a probability of occurrence.
Liu, on the other hand, teach wherein the input information including the position of the lesion is a probability map in which a probability of occurrence of the lesion is defined for each position in the first medical image (Liu: spatial probability maps 400 illustrate increasing likelihood of lesion appearance with darker shading [¶0039; Fig. 4]).
Liu further disclose that their framework for training a machine learning based generator network architecture fills a need for providing training data for machine learning based systems for correlating the severity and progression of Covid-19 [0005]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the medical image synthesis framework disclosed by Liu, and incorporate it with lesion and microvascular image synthesis system proposed by Nickisch in view of Zangoie to arrive at the invention of the instant application.
With respect to claim 4, Nickisch in view of Zangooei teach The image processing apparatus according to claim 1 (as described previously), but does not teach input information including a probability map for the occurrence of a lesion.
Liu, however, teach wherein the input information including the position of the lesion is data in which a value indicating existence of the lesion is defined in a region in which the lesion exists in the first medical image (Liu: Liu: training images 802, containing known positions of abnormality patterns (lesions) associated with COVID-19 are fed into framework 800, with known positions of lesions masked in manually annotated masks 804 to provide masked training images 806 – the examiner notes that masking of an image inherently assigns a binary value for areas under the mask associated with the existence of a lesion [¶0052-54; Fig. 8]).
Liu further disclose that their framework for training a machine learning based generator network architecture fills a need for providing training data for machine learning based systems for correlating the severity and progression of Covid-19 [0005]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the medical image synthesis framework disclosed by Liu, and incorporate it with lesion and microvascular image synthesis system proposed by Nickisch in view of Zangoie to arrive at the invention of the instant application.
As for claim 7, Nickisch in view of Zangooei teach The image processing apparatus according to claim 1 (as described previously), however, they fail to teach a generator and discriminator architecture.
Liu, contrastingly, teach wherein the processor further inputs the first medical image to a trained model, wherein the trained model includes a generator and a discriminator (Liu: framework 800 for a training a machine learning based generator network for generating synthesized medical images comprising a generator network 808 and discriminator network 812 [0052-55; Fig. 8]), the generator generates the second medical image and the discriminator discriminates whether the generated second medical image is a real medical image or a fake medical image (Liu: the generator 808 is trained to generate synthesized training images 810 from provided masked training images 806, which are input into the discriminator 812 alongside training images 802 to classify images as real or fake [0054; Fig. 8]).
Liu further disclose that their framework for training a machine learning based generator network architecture fills a need for providing training data for machine learning based systems for correlating the severity and progression of Covid-19 [0005]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the medical image synthesis framework disclosed by Liu, and incorporate it with lesion and microvascular image synthesis system proposed by Nickisch in view of Zangooei to arrive at the invention of the instant application.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael M. Sofroniou whose telephone number is (571)272-0287. The examiner can normally be reached M-F: 8:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M. Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL M SOFRONIOU/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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