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 .
Priority
Acknowledgement is made of Applicant’s claim of priority from Foreign Application No. TW113105065, filed February 7, 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system, method, and non-transitory computer-readable medium for . Consider method claim [3]:
Step 1:
With regard to Step 1, the instant claim is directed to a method or a process; and therefore, the claim is directed to one of the statutory categories of invention.
Step 2A, Prong One:
With regard to 2A, Prong One, the limitations “selecting a sequence of chest CT images from the original CT images, where the chest CT images are those of the original CT images that include a chest portion of the patient and that are in sequential order”, “for each of the chest CT images, inputting the chest CT image into a part detection model that is generated using deep learning, so as to generate a detection result that is related to the chest CT image, where the detection result indicates whether the chest CT image represents an ascending aorta”, “for each of the chest CT images, inputting the chest CT image into a status analysis model that is generated using deep learning, so as to generate an analysis result that is related to the chest CT image, where the analysis result indicates whether the chest CT image shows aortic dissection”, “making a first determination on whether among consecutive M chest CT images of the plurality of chest CT images, there is at least N chest CT image(s) the analysis result of each of which shows aortic dissection, where M and N are positive integers, and N is not greater than M”, “in response to the first determination being affirmative, making a second determination on whether the detection result that is related to at least one of the at least N chest CT image(s) represents an ascending aorta; in response to the second determination being affirmative, generating a first classification result that indicates type A aortic dissection” and “in response to the second determination being negative, generating a second classification result that indicates type B aortic dissection” as drafted, recite an abstract idea, such as a process that, under its broadest reasonable interpretation, covers performance of the limitations manually and in the mind of a person. That is, a user or person skilled in the art may select a sequence of CT images of a chest area, detect whether the chest CT image represents an ascending aorta, detect whether the chest CT image shows aortic dissection, diagnose with aortic dissection when a certain number of the images shows the aortic dissection, and determine the type of the dissection as A when the image shows the ascending aorta and B when it does not. This is the concept that falls under the grouping of abstract ideas mental processes, i.e., a concept performed in the human mind, evaluation, judgement, and/or opinion of the user.
Step 2A, Prong Two:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional step or a combination of additional steps in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional step of “receiving a plurality of original CT images that are related to a patient and that are in sequential order” is considered to be extra-solution activity of gathering information. In addition, with respect to the system and computer-readable medium claims of claims 5-12, the mere recitation of a generic processor, memory, or storage medium to perform/store programming instructions of the recited/identified abstract idea does not integrate the identified abstract idea into a practical application. Accordingly, the above-mentioned additional elements/limitations do not integrate the abstract idea into a practical application; and therefore, the independent claims recite an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B. The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/limitations to perform the recited steps, amount to no more than insignificant extra-solution activity. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Therefore, independent claims 1 and 9 are not patent eligible. In addition, claims 5-8 and 10-12 of the instant application provide limitations that both individually or in combination do not integrate the identified abstract idea into a practical application or provide significantly more than the identified abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang et al. (“Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography”).
Regarding claim 1, Huang teaches a method for analyzing aortic computed tomography (CT) images, the method being implemented by a processor (p. 2279-2280, the model was trained with a central processing unit) and comprising:
receiving a plurality of original CT images that are related to a patient and that are in sequential order (p. 2278, Non-ECG-gated aortic CTA was performed by two scanners (SOMATOM Definition Flash and SOMATOM Definition AS, Siemens Healthineers). Arterial phase images were acquired by bolus tracking with a threshold of 100 Hounsfield units (HU) at the ascending thoracic aorta and a fixed trigger delay of 12 and 16 s for AS and Flash, respectively. The scan range was from the thoracic inlet to the groins. All axial images were 5.0 mm and sized 512 × 512 pixels. The window width and level for visual diagnosis were set at 600 HU and 150 HU, respectively);
selecting a sequence of chest CT images from the original CT images, where the chest CT images are those of the original CT images that include a chest portion of the patient and that are in sequential order (p. 2281, an arterial phase series of CTA of the aorta was inputted);
for each of the chest CT images, inputting the chest CT image into a part detection model that is generated using deep learning (p. 2278, a 2-step hierarchical model to evaluate the practical usefulness of deep learning networks for the detection and classification of aortic dissection (AD)), so as to generate a detection result that is related to the chest CT image, where the detection result indicates whether the chest CT image represents an ascending aorta (p. 2281; Fig. 2, the second step model for classification of aortic dissection. P. 2278, Stanford type A AD involves the ascending aorta with or without extension to the descending aorta. Stanford type B AD involves the descending thoracic aorta distal to the left subclavian artery);
for each of the chest CT images, inputting the chest CT image into a status analysis model that is generated using deep learning (p. 2278, a 2-step hierarchical model to evaluate the practical usefulness of deep learning networks for the detection and classification of aortic dissection (AD)), so as to generate an analysis result that is related to the chest CT image, where the analysis result indicates whether the chest CT image shows aortic dissection (p. 2281; Fig. 2, first step model, detection of aortic dissection);
making a first determination on whether among consecutive M chest CT images of the plurality of chest CT images, there is at least N chest CT image(s) the analysis result of each of which shows aortic dissection, where M and N are positive integers, and N is not greater than M (p. 2280, In the patient-based analysis, a case was predicted as positive (i.e., had aortic dissection) if 5 or more consecutive slices (i.e., at least N chest CT images) in the CTA series (i.e., M chest CT images) were recognized as positive);
in response to the first determination being affirmative, making a second determination on whether the detection result that is related to at least one of the at least N chest CT image(s) represents an ascending aorta (p. 2281; Fig. 2, a series was transferred to the second step model if identified as positive. The second step model for classification of aortic dissection. P. 2278, Stanford type A AD involves the ascending aorta with or without extension to the descending aorta. Stanford type B AD involves the descending thoracic aorta distal to the left subclavian artery);
in response to the second determination being affirmative, generating a first classification result that indicates type A aortic dissection (p. 2278, Stanford type A AD involves the ascending aorta with or without extension to the descending aorta (i.e., if the dissection involves the ascending aorta, it is type A)); and
in response to the second determination being negative, generating a second classification result that indicates type B aortic dissection (p. 2278, Stanford type B AD involves the descending thoracic aorta distal to the left subclavian artery (i.e., if the dissection does not involve the ascending aorta, it is type B)).
Regarding claim 2, Huang teaches the method as claimed in claim 1, further comprising:
in response to the first determination being negative, generating a third classification result that indicates no aortic dissection (p. 2281; Fig. 2, a series without aortic dissection would be filtered out (less than 5 consecutive slices containing predicted aortic dissection). Patent is predicted as having no aortic dissection).
Regarding claim 3, Huang teaches the method as claimed in claim 1, further comprising, before inputting the chest CT images into the status analysis model:
receiving a plurality of reference CT images, each of which includes an aorta (p. 2278-2279, only images demonstrating aortic intimal flaps were selected for label and annotation);
receiving a plurality of dissection indication datasets corresponding respectively to the reference CT images, where each of the dissection indication datasets indicates whether the corresponding one of the reference CT images shows aortic dissection (p. 2278-2279, only images demonstrating aortic intimal flaps were selected for label and annotation. The outer surface of the dissected aorta on the axial images was outlined by using the labelling tool and annotated as “Dissection”); and
inputting the reference CT images and the dissection indication datasets into a deep learning model, so as to generate the status analysis model (p. 2279; Fig. 1, labelled images are further analyzed and processed by the 2-step hierarchical model (i.e., input into the deep learning model)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography”) in view of Wang et al. (US 2024/0005479 A1, filed May 31, 2022).
Regarding claim 4, Huang teaches the method as claimed in claim 1, further comprising, before inputting the chest CT images into the part detection model:
receiving a plurality of reference CT images, each of which includes an aorta (Huang, p. 2278-2279, only images demonstrating aortic intimal flaps were selected for label and annotation);
inputting the reference CT images and the part identification datasets into a deep learning model, so as to generate the part detection model (Huang, p. 2279; Fig. 1, labelled images are further analyzed and processed by the 2-step hierarchical model (i.e., input into the deep learning model)).
Although Huang teaches annotating the images as “Alpha” and “Beta” (Huang, p. 2279), Huang does not explicitly teach “receiving a plurality of part identification datasets corresponding respectively to the reference CT images, where each of the part identification datasets indicates which one of an ascending aorta, an aortic arch, and a descending aorta the corresponding one of reference CT images represents”. However, in an analogous field of endeavor, Wang teaches manually labeling the aorta of a particular subject from an image (Wang, Para. [0073]). To ensure precise labeling, the relationship between the centerline and the ascending aorta, the aorta arch, the first descending aorta, and the second descending aorta are defined (Wang, Para. [0078]; Fig. 13F).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method of Huang with the teachings of Wang by including labeling the reference CT images with indications of an ascending aorta, an aortic arch, and a descending aorta. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for assessing medical images for cardiovascular risk prediction, as recognized by Wang. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claims 6-8 recite computer-readable storage mediums storing programs with instructions corresponding to the steps recited in Claims 1-3. The recited programming instructions of these claims are mapped to the proposed reference in the same manner as the corresponding steps in their corresponding method claims as described in the 35 USC 102(a)(1) rejections above. However, the Huang reference does not teach “a non-transitory computer readable storage medium storing a computer program”. In an analogous field of endeavor, Wang teaches a computer readable medium is also present, which stores computer executable code (Wang, Para. [0092]).
The proposed combination as well as the motivation for combining the Huang and Wang references presented in the rejection of Claim 4, apply to Claims 6-8 and are incorporated herein by reference. Thus, the computer-readable storage mediums recited in Claims 6-8 are met by Huang in view of Wang.
Claim 5 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 4. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Huang and Wang references, presented in rejection of Claim 4, apply to this claim. Finally, the combination of Huang and Wang discloses a computer readable storage medium (Wang, Para. [0092], a computer readable medium is also present, which stores computer executable code).
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography”) in view of Dehghan Marvast et al. (US 2019/0105008 A1).
Claims 9-11 recite systems with elements corresponding to the steps recited in Claims 1-3, respectively. Therefore, the recited elements of these claims are mapped to the proposed reference in the same manner as the corresponding steps in their corresponding method claims as described in the 35 USC 102(a)(1) rejections above. However, the Huang reference does not teach “a storage medium” and “a processor electrically connected to said storage medium”. In an analogous field of endeavor, Dehghan Marvast teaches execution by one or more of the respective processors via one or more of the respective RAMs (Dehghan Marvast, Para. [0045]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the systems of Huang with the teachings of Dehghan Marvast by including a processor and an electrically connected storage medium (i.e., RAM). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for a system for detecting and categorizing aortic pathologies, as recognized by Dehghan Marvast. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography”) in view of Wang et al. (US 2024/0005479 A1, filed May 31, 2022), as applied to claims 4-8 above, and further in view of Dehghan Marvast et al. (US 2019/0105008 A1).
Claim 12 recites a system with elements corresponding to the steps recited in Claim 4. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Huang and Wang references, presented in rejection of Claim 4, apply to this claim. However, Huang and Wang do not teach “a storage medium” and “a processor electrically connected to said storage medium”. In an analogous field of endeavor, Dehghan Marvast teaches execution by one or more of the respective processors via one or more of the respective RAMs (Dehghan Marvast, Para. [0045]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the system of Huang in view of Wang with the teachings of Dehghan Marvast by including a processor and an electrically connected storage medium (i.e., RAM). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for a system for detecting and categorizing aortic pathologies, as recognized by Dehghan Marvast. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Conclusion
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/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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