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 .
Response to Arguments
Applicant's arguments filed 12/24/2025 have been fully considered but they are not persuasive.
Applicant argues :
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Examiner’s response :
The size or amount of calculations does not preclude a claim from being an abstract idea. Additionally the claim does not discuss the number of pixels processed, how much time is required to complete the process. A difference could simply be image 1 is brighter than image2 or simply a single pixel. The Examiner agrees that claims 3 & 4 are not rejected over 35 USC 101, and the Examiner specifically did not include those claims under the rejection in this or previous Office Actions.
The Specification (pg. 7) indicates that DSA imaging, which is essentially what your differential image computation is, is well-known, notoriously conventional technique in the field of endeavor.
Applicant argues
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Examiner’s Response:
There is a difference between applying an abstract idea within a technological field and improving the technology field itself. This is clearly applying an abstract idea.
Applicant argues :
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Examiner’s Response:
Applicant’s argument is conclusionary. What causes the claimed invention to reduce error and reduced time problems common in the art? Simply saying I’m running an abstract idea using a computer/neural network doesn’t make a claim statutory.
For Example
claim 1 . Calculate Fibonacci’s sequence a 1000 times.
Claim 2. Calculate Fibonacci’s sequence a 1000 times using a computer.
Using a computer would obviously be faster and less prone to error, but it doesn’t make the claim statutory.
What causes the improvement must be recited in the claims. See MPEP 2106.05(a)
“If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.”
Applicant argues :
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Examiner’s Response;
The Examiner maintains that a temporal subset of differential images, where the temporal subset shows the contrast agent enter and leave the subregion is inherent to DSA imaging. It is clearly shown in the Digital subtraction angiography | Radiology Reference Article | Radiopaedia.org(https://radiopaedia.org/articles/digital-subtraction-angiography). Initially a mask image is acquired, before injecting contrast. contrast images are taken in succession whilst contrast material is being injected. mask image is then subtracted from the contrast images, pixel by pixel, which results in DSA (differential images)
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 1-2, 5-7, 10, 13-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea (mental process of identifying vascular constriction (stenosis)) without significantly more. The claim(s) recite(s) :
“receiving the temporal sequence of angiographic images representing a flow of a contrast agent within a vasculature; computing differential images corresponding to the temporal sequence of angiographic images, wherein each differential image represents a difference in image intensity values between a current angiographic image and an earlier angiographic image in the sequence;”, which is an additional element directed toward “Adding insignificant extra-solution activity to the judicial exception”, as discussed in MPEP 2106.05(g), in particular Mere Data Gathering. It is known to a PHOSITA, as well as explicitly disclosed in the Specification (pg. 7), that these limitation amount to nothing more than acquiring a DSA (Digital Subtraction angiography) imagery.
“identifying at least one temporal subset of the differential images, wherein the at least one temporal subset is associated with a sub-region of the vasculature, and wherein a first differential image in the at least one temporal subset is where the contrast agent enters the sub- region and a last differential image in the at least one temporal subset is where the contrast agent leaves the sub-region”, which is directed to the abstract idea/mental process. This can be reasonably interpreted as a person looking at the differential image (DSA) and identifying regions (i.e. identifying blood vessels).
“identifying , based on the identified at least one temporal subset of the subregions differential images, a sub-region that includes the vascular constriction”, which is directed to the abstract idea/mental process. This can be reasonably interpreted as a person mentally identifies regions of vascular constriction (i.e. finding narrow blood vessels).
This judicial exception is not integrated into a practical application. Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2B. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception is not in itself an inventive concept and does not guarantee eligibility:
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2 recites “a neural network is trained to classify, from temporal sequences of angiographic images of the vasculature, the a sub-region of the vasculature, the a sub-region of the vasculature as including the a vascular constriction and the sub-region is identified based on the classification, the neural network is trained to perform the classification by: “. The “neural network” is directed to an additional element recited at a high level of generality. It is akin to the words “apply it” using a neural network. The reset of the limitation is directed to the abstract idea.
“receiving angiographic image training data including a plurality of temporal sequences of angiographic images representing a flow of a contrast agent within a plurality of sub- regions of a vasculature; each temporal sequence being classified with a ground truth classification identifying the temporal sequence as including a vascular constriction or not including a vascular constriction;
inputting the received angiographic image training data into the neural network ; and adjusting parameters of the neural network based on a difference between the classification of each inputted temporal sequence generated by the neural network , and the ground truth classification. ”, is directed to simply appending well-understood, routine, conventional activities (i.e. Train my neural network with supervised learning), specified at a high level of generality. In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provided with the correct output.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 5 recites an additional element, but is not sufficient to amount to significantly more.
Claims 6,7,10 are directed to the abstract idea without significantly more.
Claim 13 and 16-17 are rejected under similar grounds as claim 2 above.
Claims 14-15 are rejected under similar grounds as claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-8, 12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7: “the neural network” lacks antecedent basis.
Claim 8 : “wherein the inputting the identified at least one temporal subset into a neural network further comprises” lacks antecedent basis
Claim 12 : “prior to the inputting the identified at least one temporal subset into a neural network” lacks antecedent basis
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-6,9-10, 12-17 is/are rejected under 35 U.S.C. 10(a)(1) as being anticipated by Zeng (PGPub 2011/0081057).
Regarding claim 1, Zeng discloses 1. (Currently amended) A computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, the method comprising:
receiving the temporal sequence of angiographic images representing a flow of a contrast agent within a vasculature; for images of the angiographic images in the temporal sequence, computing differential images representing a difference in image intensity values between a current image and an earlier image in the sequence in a plurality of sub-regions of the vasculature; (Zeng, “[0056] Instead of using additional equipments to indicate stenosis area, more researchers focus on stenosis detection based on information contained in DSA images only. A classical approach to the stenosis detection in images is based on a two step procedure. The first step is to segment vessels in the region of interest, and then the second step is applied to estimate the stenosis by measuring the width variation of the segmented vessels.”; Examiner Note: The above claim limitations merely describe how DSA images are acquired. While notoriously known by PHOSITA, The Examiner has provided https://radiopaedia.org/articles/digital-subtraction-angiography?lang=gb (2018) as evidence
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identifying at least one temporal subset of the differential images, wherein the at least one temporal subset is associated with a sub-region of the vasculature, and wherein a first differential image in the at least one temporal subset is where the contrast agent enters the sub- region and a last differential image in the at least one temporal subset is where the contrast agent leaves the sub-region”, and(Zeng, “[0056] Instead of using additional equipments to indicate stenosis area, more researchers focus on stenosis detection based on information contained in DSA images only. A classical approach to the stenosis detection in images is based on a two step procedure. The first step is to segment vessels in the region of interest, and then the second step is applied to estimate the stenosis by measuring the width variation of the segmented vessels.”, as shown above in the radiopaedia article contrast images are taken in succession while the contrast material in being injected. DSA imaging typically stop after a few seconds after the contrast injection stops. Therefore the process of acquiring DSA images comprises a set from when the contrast enters the subregion and when it exits the subregion.
Note: if there is no contrast in the system the ”contrast image” would be identical to the mask image therefore the DSA image would not show anything.)
“identifying , based on the identified at least one temporal subset of the subregions differential images, a sub-region that includes the vascular constriction. (Zeng, “[0056] Instead of using additional equipments to indicate stenosis area, more researchers focus on stenosis detection based on information contained in DSA images only. A classical approach to the stenosis detection in images is based on a two step procedure. The first step is to segment vessels in the region of interest, and then the second step is applied to estimate the stenosis by measuring the width variation of the segmented vessels.”)
Regarding claim 2, Zeng discloses 2. (Currently amended) The computer-implemented method according to claim 1, wherein :
a neural network is trained to classify, from temporal sequences of angiographic images of the vasculature, the a sub-region of the vasculature as including the a vascular constriction and the sub-region is identified based on the classification, the neural network is trained to perform the classification by:
receiving angiographic image training data including a plurality of temporal sequences of angiographic images representing a flow of a contrast agent within a plurality of sub- regions of a vasculature; each temporal sequence being classified with a ground truth classification identifying the temporal sequence as including a vascular constriction or not including a vascular constriction; inputting the received angiographic image training data into the neural network ; and adjusting parameters of the neural network based on a difference between the classification of each inputted temporal sequence generated by the neural network , and the ground truth classification. (Zeng, “[0055] Before the neural network can be used to generate the output O.sub.k form the inputs I.sub.i, the neural network is trained to recognize patterns using supervised training methods. Training is accomplished first by identifying a number of sets of training inputs I.sub.i, each training input sets has a corresponding desired outputs. During training, the neural network is exposed to the training input sets, thereby generating a corresponding output. The corresponding output O.sub.k from the output node M.sub.k is compared to the desired output from each training input set. A mean squared network error is then calculated between the corresponding and desired output and thereafter, the neural network adjusts its weighting factors W to minimize this error. The application of all of the training inputs sets to be used in a given circumstance is called an epoch. Generally, several epochs occur before the neural network is trained acceptably. This process is repeated with sets of known input(s)/output(s) until the mean-squared error of the outputs is below a prescribed tolerance.”; discloses supervised learning of a neural network using a ground truth { “each training input sets has a corresponding desired outputs” }; MSE teaches the difference; weighting factors teaches the parameters)
Regarding claim 3, Zeng discloses 3. (Currently amended) The computer-implemented method according to claim 1, wherein a time period between the generation of the current image and the generation of the earlier image in the sequence, that is used to compute each differential image, is predetermined, such that each differential image represents a rate of change in image intensity values between the current image and the earlier image in the sequence. (see claim 1, inherent to DSA images, where DSA images are viewed in real-time, thus the time period between frames is the capture frequency and the difference is between the mask image(non-contrast) and the contrast images)
Regarding claim 4, Zeng discloses 4. (Original) The computer-implemented method according to claim 1, wherein the earlier image is provided by a mask image, and wherein the same mask image is used to compute each differential image. (See claim 1; inherent to DSA images; see radiopaedia.org citation above)
Regarding claim 5, Zeng discloses 5. (Currently amended) The computer-implemented method according to claim 1,wherein the identifying, the sub-region that includes the vascular constriction comprises: displaying a temporal sequence of angiographic images representing a flow of a contrast agent within the identified sub-region that includes the vascular constriction or displaying a temporal sequence of differential images representing a flow of a contrast agent within (Zeng, Fig. 16)
Regarding claim 6, Zeng discloses 6. (Currently amended) The computer-implemented method according to claim 1, wherein the identifying (Zeng, paragraph 53, “[0053] When the signal void subroutine ends, all the extracted characteristics are sent as the input parameters I.sub.i to the percent stenosis calculation subroutine which employs a neural network to generate one or more output O.sub.k. The output O.sub.k is preferably the percent stenosis in the blood vessel. However, other outputs may be included such as a certainty value which, for example, may range from 0 to 1 thereby indicating the level of certainty that the percent stenosis is correct.”)
Regarding claim 9, Zeng discloses 9. (Original) The computer-implemented method according to claim 1, comprising defining the sub-regions of the vasculature by dividing the angiographic images in the received temporal sequence into a plurality of predefined sub-regions. (Zeng, “[0056] Instead of using additional equipments to indicate stenosis area, more researchers focus on stenosis detection based on information contained in DSA images only. A classical approach to the stenosis detection in images is based on a two step procedure. The first step is to segment vessels in the region of interest, and then the second step is applied to estimate the stenosis by measuring the width variation of the segmented vessels.”)
Regarding claim 10, Zeng discloses 10. (Original) The computer-implemented method according to claim 1, comprising defining the sub-regions of the vasculature by segmenting the vasculature in the angiographic images and defining a plurality of sub-regions that overlap the vasculature.(Zeng, “[0056] Instead of using additional equipments to indicate stenosis area, more researchers focus on stenosis detection based on information contained in DSA images only. A classical approach to the stenosis detection in images is based on a two step procedure. The first step is to segment vessels in the region of interest, and then the second step is applied to estimate the stenosis by measuring the width variation of the segmented vessels.”)
Regarding claim 12, Zeng discloses 12. (Currently amended) The computer-implemented method according to claim 1, comprising stacking, in the time domain, the identified temporal sequences of the subset of the subregions, prior to the inputting the identified temporal sequences of the subset into a neural network (see claim 2 )
Regarding claim 13, Zeng discloses 13. (Currently amended) The computer implemented method according to claim 1, further comprising -training a neural network to locate a vascular constriction in a temporal sequence of angiographic images, comprising by: receiving angiographic image training data including a plurality of temporal sequences of angiographic images representing a flow of contrast agent within a plurality of subregions of a vasculature; each temporal sequence being classified with a ground truth classification as including a vascular constriction or classified as not including a vascular constriction; inputting the received angiographic image training data into the neural network~; and adjusting parameters of the neural network based on a difference between the classification of each inputted temporal sequence generated by the neural network and the ground truth classification. (See claim 2)
Claims 14-15 are rejected under similar grounds as claim 1.
Claims 16-17 are rejected under similar grounds as claim 2.
Allowable Subject Matter
Claims 7-8 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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 GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/Primary Examiner, Art Unit 2672