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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 12 is 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 12 recites the limitation "the at least one neural network" in lines 2 and 5. There is insufficient antecedent basis for this limitation in the claim.
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, 15, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “receiving input indicative of the target site”, receiving image data”, “computing, based on the image data, a success metric”, “computing values of a plurality of success factors”, “weighting the computed values to provide the success metric”, and “identifying the vascular access site from the plurality of potential vascular access sites” involve organizing information and manipulating information through mathematical correlations (i.e., mathematical concepts grouping) and mental processes which are abstract ideas. This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a compute. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 12, and 15-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Goyal et al. (Pub. No.: US 2022/0338929).
Consider claims 1, 15, Goyal discloses a computer-implemented method of identifying a vascular access site for inserting an interventional device in order to reach a target site in a vasculature (paragraph [0001], systems and methods for helping a physician decide if access to the cervical and cerebral arteries is best achieved via a radial artery or femoral artery access route), the method comprising:
receiving input indicative of the target site (paragraphs [0232], [0259], input a desired target vessel);
receiving image data representing at least a portion of the vasculature (paragraphs [0231], [0259], vascular imaging data collected from the current patient);
for a plurality of potential vascular access sites (paragraph [0232], choose a “four-vessel option” to get an overall assessment for all four vessels as well as a separate assessment with potential points of difficulty for each vessel):
computing, based on the image data, a success metric representing an ease of navigating the interventional device from the vascular access site to the target site via the vasculature (paragraph [0265], DAT3 model may output an estimated success score to navigate the GC (i.e., guide catheter) to the target vessel via different access points wherein a comparison between images from a current patient to a database of past patient images is enabled, see paragraph [0188]), wherein computing the success metric comprises:
computing values of a plurality of success factors affecting the outcome of performing the vascular intervention at the target site using the vascular access site (paragraph [0192], current patient images 6e are compared to the past patient images to find the closest match 6f wherein success score information corresponding to one or more of the past patient images is displayed 6g and classification of tortuosity, difficulty of navigating a catheter etc. can be derived from analysis of the past patient database, see paragraph [0226]), and
weighting the computed values to provide the success metric (paragraphs [0318] to [0324], assigning weighting factors); and
identifying the vascular access site from the plurality of potential vascular access sites based on the success metrics computed for the potential vascular access sites (paragraph [0374], presenting a recommendation for the safest and most efficient access route).
Consider claims 2, 16, Goyal discloses wherein the success factors represent one or more of the following features: a tortuosity of a portion of the vasculature between the vascular access site and the target site; a difficulty of passing a stenosis in the vasculature between the vascular access site and the target site; a difficulty of passing an implanted device in the vasculature between the vascular access site and the target site; and a difficulty of passing a calcification in the vasculature between the vascular access site and the target site (paragraph [0207], tortuosity scores automatically calculated to gain access to the LCCA from either the FA or RA).
Consider claim 3, Goyal discloses wherein the identifying the vascular access site, comprises: providing a ranking of the potential vascular access sites (paragraph [0217], single best matched past images/data is presented and/or a ranking of the closest past images/data is presented wherein it is known that the target vessel is the LCCA, see paragraph [0216]); and wherein the ranking is based on the computed success metrics (paragraph [0214], repeating the comparison across a plurality of patient records, one or more past patient record(s) most similar to the current patient can be identified).
Consider claims 4, 5, Goyal discloses wherein the providing a ranking of the potential vascular access sites, comprises: outputting an anatomical image representing the target site and the potential vascular access sites (paragraph [0217], single best matched past images/data is presented and/or a ranking of the closest past images/data is presented wherein it is known that the target vessel is the LCCA, see paragraph [0216]), and for each potential vascular access site, identifying in the anatomical image a location of at least a dominant feature affecting one or more of the success factors (paragraph [0218], if the physician has entered the target, displaying only relevant data).
Consider claims 6, 17, Goyal discloses wherein at least one of the computing a success metric and the identifying the vascular access site, is determined by inputting the target site (paragraphs [0232], [0259], input a desired target vessel), and the image data representing the at least a portion of the vasculature (paragraph [0223], inputs that may be provided by the physician for the current patient to the past patient database), into a neural network (paragraph [0184], DAT model built algorithms including machine learning algorithms); and wherein the neural network is trained to at least one of predict the success metric and to identify the vascular access site, based on the inputted target site and the image data (paragraph [0185], predictions can be based on a past patient database).
Consider claim 12, Goyal discloses wherein the at least one neural network is further trained to predict a simulated path for the interventional device to reach the target site in the vasculature from each of the potential vascular access sites; and wherein the at least one neural network is trained to at least one of compute the success metric and/or to identify the vascular access site, based further on a complexity metric representing a difficulty of reaching the target site in the vasculature from each of the potential vascular access sites with the interventional device (paragraph [0127], system allows the physician to simulate different access routes to compare the difficulty and risk of complications).
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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Goyal in view of Sganga et al. (Pat. No.: US 11,147,635).
Consider claim 11, Goyal does not specifically disclose wherein the image data comprises ultrasound image data generated prior to inserting the interventional device into the identified vascular access site.
Sganga discloses wherein the image data comprises ultrasound image data generated prior to inserting the interventional device into the identified vascular access site (col. 9, lines 43-57, intravascular ultrasound is configured to acquire real-time images of the subject prior to the execution of a procedure).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the device as disclosed by Goyal with the device as taught by Sganga to acquire real-time images of the subject while navigating to the site of the intervention (Sganga, col. 9, lines 43-57).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal in view of Goyal (Pub. No.: US 2021/0161598, hereinafter, Goyal 2).
Consider claim 18, Goyal discloses identifying a vascular access site for inserting an interventional device in order to reach a target site in a vasculature (paragraph [0001], systems and methods for helping a physician decide if access to the cervical and cerebral arteries is best achieved via a radial artery or femoral artery access route),
receiving input indicative of the target site (paragraphs [0232], [0259], input a desired target vessel);
receiving image data representing at least a portion of the vasculature (paragraphs [0231], [0259], vascular imaging data collected from the current patient);
for a plurality of potential vascular access sites (paragraph [0232], choose a “four-vessel option” to get an overall assessment for all four vessels as well as a separate assessment with potential points of difficulty for each vessel):
computing, based on the image data, a success metric representing an ease of navigating the interventional device from the vascular access site to the target site via the vasculature (paragraph [0265], DAT3 model may output an estimated success score to navigate the GC (i.e., guide catheter) to the target vessel via different access points wherein a comparison between images from a current patient to a database of past patient images is enabled, see paragraph [0188]), wherein computing the success metric comprises:
computing values of a plurality of success factors affecting the outcome of performing the vascular intervention at the target site using the vascular access site (paragraph [0192], current patient images 6e are compared to the past patient images to find the closest match 6f wherein success score information corresponding to one or more of the past patient images is displayed 6g and classification of tortuosity, difficulty of navigating a catheter etc. can be derived from analysis of the past patient database, see paragraph [0226]), and
weighting the computed values to provide the success metric (paragraphs [0318] to [0324], assigning weighting factors); and
identifying the vascular access site from the plurality of potential vascular access sites based on the success metrics computed for the potential vascular access sites (paragraph [0374], presenting a recommendation for the safest and most efficient access route).
Goyal does not specifically a non-transitory computer-readable storage medium having stored a computer program comprising instructions, the instruction, when executed by a processor, cause the processor to: perform the claim limitations.
Goyal 2 discloses a non-transitory computer-readable storage medium having stored a computer program comprising instructions, the instruction, when executed by a processor, cause the processor to: perform the claim limitations (paragraph [0083], non-transitory computer readable medium encoded with instructions).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the device as disclosed by Goyal with the device as taught by Goyal 2 to improve the efficiency of surgical procedures conducted using catheter systems (Goyal 2, paragraph [0083]).
Consider claim 19, the combination of Goyal and Goyal 2 discloses wherein the success factors represent one or more of the following features: a tortuosity of a portion of the vasculature between the vascular access site and the target site; a difficulty of passing a stenosis in the vasculature between the vascular access site and the target site; a difficulty of passing an implanted device in the vasculature between the vascular access site and the target site; and a difficulty of passing a calcification in the vasculature between the vascular access site and the target site (paragraph [0207], tortuosity scores automatically calculated to gain access to the LCCA from either the FA or RA).
Consider claim 20, the combination of Goyal and Goyal 2 discloses wherein the instruction, when executed by the processor, further cause the processor to input the target site (paragraphs [0232], [0259], input a desired target vessel) and the image data representing the at least a portion of the vasculature (paragraph [0223], inputs that may be provided by the physician for the current patient to the past patient database), into a neural network (paragraph [0184], DAT model built algorithms including machine learning algorithms) into a neural network to at least one of compute the success metric and identify the vascular access site; and wherein the network is trained to at least one of predict the success metric and to identify the vascular access site based on the inputted target site and the image data (paragraph [0185], predictions can be based on a past patient database).
Allowable Subject Matter
Claims 7-10, 13, and 14 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.
Regarding claim 7, the prior art of record fails to disclose wherein the neural network is trained to at least one of predict the success metric and to identify the vascular access site, based further on electronic health record data.
Regarding claim 10, the prior art of record fails to disclose a separate neural network is trained to predict each of the success factors.
Regarding claim 13 the prior art of record fails to disclose adjusting parameters of the neural network until a value of a loss function representing a difference between at least one of a success metric predicted by the neural network and/or a vascular access site predicted by the neural network, and the ground truth data, meets a stopping criterion.
Regarding the remaining claims, the claims are objected to due to dependency on objected claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERALD JOHNSON whose telephone number is (571)270-7685. The examiner can normally be reached Monday-Friday 8am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carey Michael can be reached at (571)270-7235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gerald Johnson/
Primary Examiner, Art Unit 3797