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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 06/12/2023 has/have been considered by the examiner. Claim Objections Claim 13 is objected to because of the following informalities: In claim 13, lines 2-3, “ the system comprising: to claim 1 a processor communicatively coupled to memory …” should read -- the system comprising: a processor communicatively coupled to memory … --. Appropriate correction is required. 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)(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. Claim(s) 1 -2, 4, 6- 15 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Mihalef et al ( US 20210290308 A1 ), hereinafter Mihalef . -Regarding claim 1, Mihalef discloses a computer-implemented method of providing an endovascular coil specification ( [0003], “one or more hemodynamic parameters to support the decision for endovascular treatment …” ; [0041], “characteristics”; [0065] ) of an endovascular coil ( [0040], “endovascular devices …” ) for treating an aneurysm in a coil embolization procedure ( [0040], “coiling of aneurysms” ), the method comprising ( Abstract; FIGS. 1-11 ) : receiving X-ray image data comprising a plurality of X-ray images including an aneurysm ( FIG. 1, imaging 10; FIG. 2, step 20; FIG. 11, scanner 110; [0021], “Aneurysm treatment planning in clinical practice depends on acquiring suitable medical images …”; [0032], “scanning … x-ray …”; [0033]; [0034], “acquired scan data”[0088] ) , the plurality of X-ray images representing different steps of the coil embolization procedure ( [0083], “ The intra-procedural image information may be used … ” ) ; extracting at least one image feature of the aneurysm from the plurality of X-ray images ( FIG. 1, machine learning 16; FIG. 2; FIG. 11, classifier 113; [0021]; [0023], “segmenting a boundary …”; [0036]; [0044]; [0045], “features of the input vector”; [0047], “machine learning or training … classifier … identifying features …”; [0048] ) ; predicting ( FIG. 1, stage 16 ; FIG. 11 ) , from the at least one extracted image feature of the aneurysm ( [0026], “based on the simulated deployment, imaging data (e.g., segmented vessel), and/or other patient-specific information ” ; [0048]-[0049] ) , endovascular coil data comprising an endovascular coil specification of an endovascular coil to be used in a next step of the coil embolization procedure for treating the aneurysm ( FIG. 1; [0026], “output implant information (e.g., type, size, placement, number of devices, configuration of device, etc.) used for the simulation in stage 14 … for a simulated deployment ” ; [0027], “ simulated deployment, predicted outcome, and/or a calculated hemodynamic characteristic based on the deployment … for planning an endovascular implantation ”; FIG. 2, act s, 21 24; [0044]; [0049]; [ 0065]-[0067] ; FIG. 11; [0095] ) ; and outputting the endovascular coil specification. ( FIG. 1, stage 18; FIG. 2, acts 25-26; FIG. 11; [0027]; [0068]; [0071] ; [0078]; [009 5 ] ) . -Regarding claim 13, Mihalef discloses a system for providing an endovascular coil specification ( [0003], “one or more hemodynamic parameters to support the decision for endovascular treatment …” ; [0040], “endovascular devices …” ; [0041], “characteristics”; [0065] ) ) for treating an aneurysm in a coil embolization procedure ( [0040], “coiling of aneurysms” ), the system comprising ( Abstract; FIGS. 1-11 ) : a processor communicatively coupled to memory, the processor configured to ( FIG. 11 ) : receiv e X-ray image data comprising a plurality of X-ray images including an aneurysm ( FIG. 1, imaging 10; FIG. 2, step 20; FIG. 11, scanner 110; [0021], “Aneurysm treatment planning in clinical practice depends on acquiring suitable medical images …”; [0032], “scanning … x-ray …”; [0033]; [0034], “acquired scan data”[0088] ) , the plurality of X-ray images representing different steps of the coil embolization procedure ( [0083], “The intra-procedural image information may be used … ” ); extract at least one image feature of the aneurysm from the plurality of X-ray images ( FIG. 1, machine learning 16; FIG. 2; FIG. 11, classifier 113; [0021]; [0023], “segmenting a boundary …”; [0036]; [0044]; [0045], “features of the input vector”; [0047], “machine learning or training … classifier … identifying features …”; [0048] ) ; predict ( FIG. 1, stage 16 ; FIG. 11 ) , from the at least one extracted image feature of the aneurysm ( [0026], “based on the simulated deployment, imaging data (e.g., segmented vessel), and/or other patient-specific information ”; [0048]-[0049] ) , endovascular coil data comprising an endovascular coil specification of an endovascular coil to be used in a next step of the coil embolization procedure for treating the aneurysm ( FIG. 1; [0026], “output implant information (e.g., type, size, placement, number of devices, configuration of device, etc.) used for the simulation in stage 14 … for a simulated deployment ” ; [0027], “ simulated deployment, predicted outcome, and/or a calculated hemodynamic characteristic based on the deployment … for planning an endovascular implantation ”; FIG. 2, acts, 21 24; [0044]; [0049]; [0065]-[0067]; FIG. 11; [0095] ) ; and output the endovascular coil specification. ( FIG. 1, stage 18; FIG. 2, acts 25-26; FIG. 11; [0027]; [0068]; [0071]; [0078]; [009 5 ] ). -Regarding claim 15, Mihalef discloses a non-transitory computer-readable storage medium having stored a computer program product comprising instructions which, when executed by a processor cause the processor to ( Abstract; FIGS. 1-11 ) : receiv e X-ray image data comprising a plurality of X-ray images including an aneurysm ( FIG. 1, imaging 10; FIG. 2, step 20; FIG. 11, scanner 110; [0021], “Aneurysm treatment planning in clinical practice depends on acquiring suitable medical images …”; [0032], “scanning … x-ray …”; [0033]; [0034], “acquired scan data”[0088] ) , the plurality of X-ray images representing different steps of the coil embolization procedure ( [0083], “The intra-procedural image information may be used … ” ); extract at least one image feature of the aneurysm from the plurality of X-ray images ( FIG. 1, machine learning 16; FIG. 2; FIG. 11, classifier 113; [0021]; [0023], “segmenting a boundary …”; [0036]; [0044]; [0045], “features of the input vector”; [0047], “machine learning or training … classifier … identifying features …”; [0048] ) ; predict ( FIG. 1, stage 16 ; FIG. 11 ) , from the at least one extracted image feature of the aneurysm ( [0026], “based on the simulated deployment, imaging data (e.g., segmented vessel), and/or other patient-specific information ”; [0048]-[0049] ) , endovascular coil data comprising an endovascular coil specification of an endovascular coil to be used in a next step of the coil embolization procedure for treating the aneurysm ( FIG. 1; [0026], “output implant information (e.g., type, size, placement, number of devices, configuration of device, etc.) used for the simulation in stage 14 … for a simulated deployment ” ; [0027], “ simulated deployment, predicted outcome, and/or a calculated hemodynamic characteristic based on the deployment … for planning an endovascular implantation ”; FIG. 2, acts, 21 24; [0044]; [0049]; [0065]-[0067]; FIG. 11; [0095] ) ; and output the endovascular coil specification. ( FIG. 1, stage 18; FIG. 2, acts 25-26; FIG. 11; [0027]; [0068]; [0071]; [0078]; [009 5 ] ). -Regarding claim 2, Mihalef discloses the method of claim 1. Mihalef further discloses wherein the X- ray image data comprises at least one of one or more X-ray fluoroscopy images, and/or and one or more contrast-enhanced X-ray images ( [0070]; [0088] ) . -Regarding claim 4, Mihalef discloses the method of claim 1. Mihalef further discloses segmenting ( FIG. 1; [0021]; [0023]; [0026]; [0036] ) the X-ray image data ( [0032]; [0070] ) to identify the aneurysm ( FIGS. 1-2; Table 2; [0036]; [0049] ), and extract the at least one image feature of the aneurysm to predict the endovascular coil data ( FIGS. 1-2; [0021]; [0023]; [0048]-[0049] ) . -Regarding claim 6 , Mihalef discloses the method of claim 1. Mihalef further discloses wherein a neural network is trained to predict, from the at least one extracted image feature, the endovascular coil data of the endovascular coil, by: receiving X-ray image training data comprising one or more X-ray images including an aneurysm; receiving ground truth endovascular coil specification data representing endovascular coil data of an endovascular coil used to treat the aneurysm in the X-ray image training data; and inputting the received X-ray image training data into the neural network, and adjusting parameters of the neural network based on a loss function representing a difference between the endovascular coil data, predicted by the neural network, and the endovascular coil data of the endovascular coil used to treat the aneurysm in the X-ray image training data represented by the received ground truth endovascular coil specification data ( FIGS. 1, 11; [0026]; [0044]-[0049]; [0052], [0057], “energy function … goodness-of-fit measure …”; Note : the recited claim limitation present a conventional machine learning method ). -Regarding claim 7, Mihalef discloses the method of claim 6. Mihalef further discloses wherein the neural network is trained to predict the endovascular coil data of the endovascular coil for treating the aneurysm, from the X-ray image training data, and from volumetric image training data representing the aneurysm in the X-ray image training data; and wherein the neural network is trained to predict the endovascular coil data of the endovascular coil, by further: receiving the volumetric image training data representing the aneurysm in the X-ray image training data; inputting the received volumetric image training data into the neural network; and predicting the endovascular coil data of the endovascular coil for treating the aneurysm, from the received X-ray image training data, and from the received volumetric image training data ( FIGS. 1, 11; [0026]; [0044]-[0049]; [0052], [0057] ; [0035], “ a three-dimensional representation of the vessel of the patient …”; [0043], “3D mesh” ) . -Regarding claim 8, Mihalef discloses the method of claim 6. Mihalef further discloses wherein the neural network is further trained to predict the endovascular coil data of the endovascular coil for treating the aneurysm, from patient training data corresponding to the aneurysm in the X-ray image training data; and wherein the neural network is trained to predict the endovascular coil data of the endovascular coil for treating the aneurysm, by further: receiving the patient training data corresponding to the aneurysm in the X-ray image training data; inputting the received patient training data into the neural network; and predicting the endovascular coil data of the endovascular coil for treating the aneurysm, based further on the received patient training data ( FIGS. 1, 11; [0026]; [0044]- [0046] ; [0047], “neural network … Any deep learning approach or architecture may be used. For example, a convolutional neural network is used ”; [0048]- [0049]; [0052], [0057] ) . -Regarding claim 9 , Mihalef discloses the method of claim 6. Mihalef further discloses wherein the neural network is further trained to predict the endovascular coil data of the endovascular coil for treating the aneurysm, by further; receiving ground truth procedural outcome data representing an outcome of using the ground truth endovascular coil specification data to treat the aneurysm in the X-ray image training data; wherein the adjusting of the parameters of the neural network comprises reducing a value of the loss function; and wherein a negative procedural outcome is configured to increase a value of the loss function ( FIGS. 1, 11; [0026]; [0044]- [0046], “ The initial trained model may be continuously updated using online learning or other machine learning approaches ”; [0047], “neural network … Any deep learning approach or architecture may be used. For example, a convolutional neural network is used ”; [0048]- [0049]; [0052], [0057] , “ minimizes an energy function …” ) . -Regarding claim 10, Mihalef discloses the method of claim 6. Mihalef further discloses wherein the neural network is further trained to predict, from the X-ray image data, procedural outcome data representing at least one of: a fractional value representing the completeness of the coil embolization procedure; a risk of rupture of the aneurysm; a risk of recanalization of the aneurysm; a recommended follow-up interval; consequent to using the predicted endovascular coil data of the endovascular coil to treat the aneurysm in the coil embolization procedure ( FIGS. 1, 11; [0047]; [0049]; [0071], “ t he outcome may be a risk, such as a risk of rupture … prediction of recovery … a probability or estimate of reoccurrence” ; [0078], “p rovides an initial or starting recommendation ” ) ; and wherein the neural network is trained to predict the procedural outcome data by: receiving ground truth procedural outcome data representing an outcome of using the ground truth endovascular coil specification data to treat the aneurysm in the X-ray image training data; and inputting the received ground truth procedural outcome data, into the neural network; and wherein the loss function used in the adjusting parameters of the neural network is based further on a difference between the procedural outcome data predicted by the neural network, and the received ground truth procedural outcome data ( FIGS. 1, 11; [0026]; [0044]- [0046], “ The initial trained model may be continuously updated using online learning or other machine learning approaches ”; [0047], “neural network … Any deep learning approach or architecture may be used. For example, a convolutional neural network is used ”; [0048]- [0049]; [0052], [0057] , “ minimizes an energy function …” ) . -Regarding claim 1 1 , Mihalef discloses the method of claim 1 . Mihalef further discloses c omparing the outputted endovascular coil data with a database of endovascular coil data for each of a plurality of endovascular coils ( [0023]; [0049] ) ; and identifying one or more of the plurality of endovascular coils for treating the aneurysm in the coil embolization procedure, based on the comparing ( [0049] ) . -Regarding claim 1 2 , Mihalef discloses the method of claim 1 . Mihalef further discloses comprising computing a confidence estimate of the endovascular coil data predicted by the neural network ( FIGS. 1, 11; [0047], “neural network … Any deep learning approach or architecture may be used. For example, a convolutional neural network is used ”; [0098], “ ranked based on their corresponding confidence. The user may then select the final decision ” ). -Regarding claim 14, Mihalef discloses the method of claim 6. Mihalef further discloses wherein the neural network is trained by: receiving X-ray image training data comprising a plurality of X-ray images including an aneurysm, the plurality of X-ray images including pre- procedural X-ray images ( [0069]; [0083], “pre-procedure imaging”) and intra-procedural X-ray images representing the different steps during the coil embolization procedure ( [0083], “intra-procedural image information may be used … ” ); receiving ground truth endovascular coil specification data representing endovascular coil data of an endovascular coil used to treat the aneurysm in the X-ray image training data; and inputting the received X-ray image training data, into the neural network, and adjusting parameters of the neural network based on a loss function representing a difference between the endovascular coil data, predicted by the neural network, and the endovascular coil data of the endovascular coil used to treat the aneurysm in the X-ray image training data represented by the received ground truth endovascular coil specification data ( FIGS. 1, 11; [0026]; [0044]- [0046], “ The initial trained model may be continuously updated using online learning or other machine learning approaches ”; [0047], “neural network … Any deep learning approach or architecture may be used. For example, a convolutional neural network is used ”; [0048]- [0049]; [0052], [0057] , “ minimizes an energy function …” ) . 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 (s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mihalef et al ( US 20210290308 A1 ), hereinafter Mihalef in view of Conner et al ( US 20200297287 A1 ), hereinafter Conner . -Regarding claim 3, Mihalef discloses the method of claim 1. Mihalef discloses wherein the plurality of X-ray images represent ing different timesteps during the coil embolization procedure ( [0083] ) . Mihalef does not disclose the images compris ing multiple different viewing angles of the aneurysm . In the same field of endeavor, Conner teaches a method for rules based assessment of endovascular coil stability ( Conner: Abstract; 1-7D ) . Conner also teaches providing an endovascular coil specification of an endovascular coil for treating an aneurysm ( Conner: FIGS. 5-6; [0042]-[0043]; [0045]; [0071]; [0088]-[0089] ). Conner further teaches wherein the plurality of X-ray images at least one of i) representing different timesteps during the coil embolization procedure ( Conner: [0011]; [0020]; [0038]) and ii) comprising multiple different viewing angles of the aneurysm ( Conner: FIGS. 2, 7A-7D ; [0045]; [0064], “ an AP x-ray image or lateral x-ray image ” ). Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Mihalef with the teaching of Conner by using X-ray images representing different timesteps comprising multiple different viewing angles and in order to provide accurate assessment of endovascular coil stability . Claim (s) 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mihalef et al ( US 20210290308 A1 ), hereinafter Mihalef in view of Nair et al ( US 20180092690 A1 ), hereinafter Nair . -Regarding claim 17 , Mihalef discloses the method of claim 1 and the system of claim 13 . Mihalef does not disclose wherein the at least one extracted image feature of the aneurysm includes at least one of aneurysm bifurcation, aneurysm size, aneurysm position, aneurysm angle relative to the blood flow, curvature of a parent vessel, and aneurysm neck diameter. In the same field of endeavor, Nair teaches a method for p atient-specific 3D complex coils to improve treatment outcomes for cerebral aneurysm repair ( Nair: Abstract; FIGS. 1-16 ) . Nair further teaches wherein the at least one extracted image feature of the aneurysm includes at least one of aneurysm bifurcation, aneurysm size, aneurysm position, aneurysm angle relative to the blood flow, curvature of a parent vessel, and aneurysm neck diameter ( Nair: FIG. 3 ; [0040]-[0041] ) . Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Mihalef with the teaching of Nair by extract ing image feature of the aneurysm includes at least one of aneurysm bifurcation, aneurysm size, aneurysm position, aneurysm angle relative to the blood flow, curvature of a parent vessel, and aneurysm neck diameter in order to more efficiently and accurately classify aneurysm types (Nair: [0041]). Allowable Subject Matter Claim s 5 , 16, 18 and 20 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 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Malik et al (A Framework for Intracranial Saccular Aneurysm Detection and Quantification using Morphological Analysis of Cerebral Angiograms, IEEE Access, Vol. 6, 2018), hereinafter Malik teaches a method to perform morphological analysis of digital subtraction angiography (DSA) for detecting saccular aneurysms and determining their sizes, and estimate the probability of heir ruptures. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT XIAO LIU whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4539 . 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