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 .
Preliminary Amendment
The Preliminary Amendment submitted on 10/15/2024 has been entered and made of record.
Status of Claims
This communication is in response to the Application Filed on 10/15/2024
Claims 1–11 are pending in this application.
Drawings
The drawing(s) filed on 10/15/2024 are accepted by the Examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/15/2024 are 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 1 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.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
Regarding claim 1, the phrase "simulations of the thrombectomy surgery" renders the claim indefinite because it unclear what these simulations are. Are they images, videos, live simulations, etc. For the purpose of examination, the examiner is interpreting the simulations as images related to the surgery.
Dependent claims 2–11 are rejected for failing to remedy the ambiguity of their respective independent claims
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.
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 non-obviousness.
Claim(s) 1 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al. (See NPL attached, "Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning", hereafter, "Nishi") in view of Chen et al. (CN 111754452 A, hereafter, "Chen") further in view of Manhart et al. (US 2021/0004997 A1, hereafter, "Manhart").
Regarding claim 1, Nishi discloses method to estimate in real time the likelihood of success of thrombectomy surgery (See Nishi, [Background and Purpose], We aimed to model this process with machine learning and predict the long term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods), comprising the following steps:
[creating a database generated from thrombectomy patterns and training a predictive algorithm with that data using a processing unit associated with the database;
acquiring clinical images relating to the occluded vessels of a patient and extracting from said clinical images the geometric parameters of the occluded vessels and the composition parameters of the occlusion;
generating a three-dimensional model of the occluded vessels and of the occlusion by processing the geometric parameters of the occluded vessels and the composition parameters of the occlusion using the processing unit;
selecting indicator parameters for thrombectomy surgery by processing the three-dimensional model of the occluded vessels and of the occlusion];
calculating the likelihood of success of thrombectomy surgery with removal of the occlusion by processing the indicator parameters using the predictive algorithm (See Nishi, [Pg. 2380, Col. 2, Pretreatment Variables and Data Preprocessing, Last 3 lines – Pg. 2381, Col. 1, first 3 lines], For data preprocessing, the categorical variables were converted into numerical values with one-hot encoding, except for the site of occlusion. The site of occlusion was assigned an ordinal number according to its anatomic proximity: common carotid artery=0, in-ternal carotid artery=1, and middle cerebral artery M1 segment=2,M2 segment=3, and M3 segment=4. [Pg. 2380, Col. 2, ln. 14–18], Here, we report the prediction model of LVO based on the machine learning algorithm only with commonly collected pretreatment variables to be utilized in making the decision to perform MT, and we compare the model with the previously reported prediction models), [characterised in that the step of creating a database generated using thrombectomy patterns and training a predictive algorithm with said data using a processing unit associated with the database, comprises training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy surgery].
However, Nishi fail(s) to teach creating a database generated from thrombectomy patterns and training a predictive algorithm with that data using a processing unit associated with the database; acquiring clinical images relating to the occluded vessels of a patient and extracting from said clinical images the geometric parameters of the occluded vessels and the composition parameters of the occlusion; generating a three-dimensional model of the occluded vessels and of the occlusion by processing the geometric parameters of the occluded vessels and the composition parameters of the occlusion using the processing unit; selecting indicator parameters for thrombectomy surgery by processing the three-dimensional model of the occluded vessels and of the occlusion characterised in that the step of creating a database generated using thrombectomy patterns and training a predictive algorithm with said data using a processing unit associated with the database, comprises training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy surgery.
Chen, working in the same field of endeavor, teaches: creating a database generated from thrombectomy patterns and training a predictive algorithm with that data using a processing unit associated with the database (See Chen, ¶ [0045], Specifically, the magnetic resonance imaging (MRI) samples of the subjects to be tested can be obtained from MRI images of thrombotic patients provided by clinicians, or they can be obtained from MRI images simulated by the system. Once the training set is assembled, the thrombus region needs to be bounded on the MRI image, that is, the exact location of the detection box needs to be drawn; ¶ [0049], YOLOv3 divides the original 960x320x1 image into 30x10x1 cells, using each cell as the detection center region, and directly predicts the relative coordinates of the candidate box center point with respect to the top left corner of the grid cell);
characterised in that the step of creating a database generated using thrombectomy patterns and training a predictive algorithm with said data using a processing unit associated with the database, comprises training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy surgery (See Chen, ¶ [0045], Specifically, the magnetic resonance imaging (MRI) samples of the subjects to be tested can be obtained from MRI images of thrombotic patients provided by clinicians, or they can be obtained from MRI images simulated by the system. Once the training set is assembled, the thrombus region needs to be bounded on the MRI image, that is, the exact location of the detection box needs to be drawn. Note: the simulation of MR thrombus images which the examiner is interpreting as representing the simulation of the thrombectomy surgery).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s reference to creating a database generated from thrombectomy patterns and training a predictive algorithm with that data using a processing unit associated with the database; characterised in that the step of creating a database generated using thrombectomy patterns and training a predictive algorithm with said data using a processing unit associated with the database, comprises training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy surgery based on the method of Chen’s reference. The suggestion/motivation would have been to effect of rapidly clearing or reducing thrombus burden and relieving venous thrombosis (See Chen, ¶ [0007–0008]).
However, Nishi and Chen fail to teach acquiring clinical images relating to the occluded vessels of a patient and extracting from said clinical images the geometric parameters of the occluded vessels and the composition parameters of the occlusion; generating a three-dimensional model of the occluded vessels and of the occlusion by processing the geometric parameters of the occluded vessels and the composition parameters of the occlusion using the processing unit; selecting indicator parameters for thrombectomy surgery by processing the three-dimensional model of the occluded vessels and of the occlusion.
Manhart, working in the same field of endeavor, teaches: acquiring clinical images (See Manhart, ¶ [0065], reception of the plurality of two-dimensional X-ray projection images) relating to the occluded vessels of a patient and extracting from said clinical images the geometric parameters of the occluded vessels and the composition parameters of the occlusion (See Manhart, ¶ [0072], The three-dimensional vessel image includes information on the spatial arrangement of vessels, that are mapped in the at least one further artifact-reduced image dataset. The information on the spatial arrangement of the vessels may for example include at least one centerline for each vessel and/or information on extension and/or information on branching and/or information on the course. ¶ [0075], The identification of the vascular occlusions by applying the method for identifying vascular occlusions may include a determination of a spatial extent and/or a spatial course and/or a position and/or an alignment and/or a morphological condition and/or a density of the respective vascular occlusion in the further artifact-reduced image dataset);
generating a three-dimensional model of the occluded vessels and of the occlusion by processing the geometric parameters of the occluded vessels and the composition parameters of the occlusion using the processing unit (See Manhart, ¶ [0072], The three-dimensional vessel image includes information on the spatial arrangement of vessels, that are mapped in the at least one further artifact-reduced image dataset. The information on the spatial arrangement of the vessels may for example include at least one centerline for each vessel and/or information on extension and/or information on branching and/or information on the course. ¶ [0075], The identification of the vascular occlusions by applying the method for identifying vascular occlusions may include a determination of a spatial extent and/or a spatial course and/or a position and/or an alignment and/or a morphological condition and/or a density of the respective vascular occlusion in the further artifact-reduced image dataset);
selecting indicator parameters for thrombectomy surgery by processing the three-dimensional model of the occluded vessels and of the occlusion (See Manhart, ¶ [0060], further, a plurality of maximum intensity projection images (MIP) may be created using the three-dimensional vessel image. Moreover, a further evaluation dataset may be created by applying a method for evaluating a manifestation of the identified vascular occlusions to the maximum intensity projection images. ¶ [0077], The evaluation of the identified vascular occlusions may furthermore include an evaluation of the spatial extent and/or the spatial course and/or the position and/or the alignment and/or the morphological condition and/or the density of the respective vascular occlusion in the plurality of maximum intensity projection images. The evaluation of the manifestation of the identified vascular occlusions may include an assignment of a value, for example a dimension, and/or a value tuple to at least one, for example all, identified vascular occlusions).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s and Chen’s references to acquire clinical images relating to the occluded vessels of a patient and extracting from said clinical images the geometric parameters of the occluded vessels and the composition parameters of the occlusion; generate a three-dimensional model of the occluded vessels and of the occlusion by processing the geometric parameters of the occluded vessels and the composition parameters of the occlusion using the processing unit; select indicator parameters for thrombectomy surgery by processing the three-dimensional model of the occluded vessels and of the occlusion based on the method of Manhart’s reference. The suggestion/motivation would have been to provide accurate and adequate treatment (See Manhart, ¶ [0003–0006]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Manhart with Nishi and Chen to obtain the invention as specified in claim 1.
Regarding claim 7, Nishi in view of Chen further in view of Manhart discloses method according to claim 1,wherein the clinical images comprise images obtained by computed tomography and/or magnetic resonance imaging (See Nishi, [Pg. 2380, Col. 2, Patient Population], Although some patients only underwent magnetic resonance imaging before treatment, those patients who lacked pretreatment brain computed tomography (CT) scan were excluded because this study aimed to compare the effectiveness of the model with the PRE and HIAT2 scores).
Claims 2, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al. (See NPL attached, "Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning", hereafter, "Nishi") in view of Chen et al. (CN 111754452 A, hereafter, "Chen") further in view of Manhart et al. (US 20210004997 A1, hereafter, "Manhart") further in view of Hong et al. (See NPL attached, "Effects of Anterior Borderzone Angle Grading on Predicting the 90-Day Prognosis After Recanalization of Acute Middle Cerebral Artery Occlusion", hereafter, "Hong") and further in view of Janardhan et al. (US 20180368965 A1, hereafter, "Janardhan").
Regarding claim 2, Nishi in view of Chen further in view of Manhart teaches method according to claim 1, [wherein the step of selecting indicator parameters comprises the sub-steps of:
calculating morphological parameters of the vessels by analysis of the median line of the occluded vessels and their diameters;
calculating morphological parameters of the occlusion by analysis of the length, diameter and composition of the occlusion].
However, Nishi and Chen fail(s) to teach wherein the step of selecting indicator parameters comprises the sub-steps of: calculating morphological parameters of the vessels by analysis of the median line of the occluded vessels and their diameters; calculating morphological parameters of the occlusion by analysis of the length, diameter and composition of the occlusion.
Manhart, working in the same field of endeavor, teaches: wherein the step of selecting indicator parameters (See Manhart, ¶ [0060], further, a plurality of maximum intensity projection images (MIP) may be created using the three-dimensional vessel image. Moreover, a further evaluation dataset may be created by applying a method for evaluating a manifestation of the identified vascular occlusions to the maximum intensity projection images) comprises the sub-steps of:
composition of the occlusion (See Manhart, ¶ [0050], The at least one tissue parameter of the thrombus may, for example, include a chemical composition and/or a density and/or a solubility).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s and Chen’s reference to wherein the step of selecting indicator parameters comprises the sub-steps of: composition of the occlusion based on the method of Manhart’s reference. The suggestion/motivation would have been to provide accurate and adequate treatment (See Manhart, ¶ [0003–0006]).
However, Nishi, Chen and Manhart fail to teach calculating morphological parameters of the vessels by analysis of the median line of the occluded vessels and their diameters; calculating morphological parameters of the occlusion by analysis of the length, diameter.
Hong, working in the same field of endeavor, teaches: calculating morphological parameters of the vessels by analysis of the median line of the occluded vessels and their diameters (See Hong, [Pg. 3, Col. 2, The Measurement of ABZA], The ABZA was measured as previously reported (6). Briefly, the projection of intracranial bifurcation of internal carotid artery on the median line of sagittal suture is defined as the vertex, and the median line of sagittal suture as the starting edge on the DSA image of Tang’s position. [Pg. 3, Col. 2, first 3 lines], simple stent, or stent combined with aspiration treatment. Stent (4 × 20mm or 6 × 30mm Solitair FR stent, EV3, CA, USA), was selected according to vessel diameter and thrombus length).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s and Manhart’s reference to calculating morphological parameters of the vessels by analysis of the median line of the occluded vessels and their diameters based on the method of Hong’s reference. The suggestion/motivation would have been to guide patients for the proper treatment (See Hong, [Pg. 2, Conclusion]).
However, Nishi, Chen, Manhart and Hong fail to teach calculating morphological parameters of the occlusion by analysis of the length, diameter.
Janardhan, working in the same field of endeavor, teaches: calculating morphological parameters of the occlusion by analysis of the length, diameter (See Janardhan, ¶ [1033], The length and diameter of the thrombus 500 can be measured, from which the volume of the thrombus 500, or the clot burden, can be calculated).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s, Manhart’s and Hong’s reference to calculating morphological parameters of the occlusion by analysis of the length, diameter based on the method of Janardhan’s reference. The suggestion/motivation would have been to provide different treatment of strokes (See Janardhan, ¶ [0003–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Janardhan with Nishi, Chen, Manhart, and Hong to obtain the invention as specified in claim 2.
Regarding claim 8, Nishi in view of Chen further in view of Manhart teaches Method according to claim 1,wherein
[the geometric parameters of the occluded vessels comprise median line and diameter for each occluded vessel;
the composition parameters of the occlusion comprise position, length and composition of the occlusion].
However, Nishi and Chen fail(s) to teach the geometric parameters of the occluded vessels comprise median line and diameter for each occluded vessel; the composition parameters of the occlusion comprise position, length and composition of the occlusion.
Manhart, working in the same field of endeavor, teaches: composition of the occlusion (See Manhart, ¶ [0050], The at least one tissue parameter of the thrombus may, for example, include a chemical composition and/or a density and/or a solubility).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s and Chen’s reference composition of the occlusion based on the method of Manhart’s reference. The suggestion/motivation would have been to provide accurate and adequate treatment (See Manhart, ¶ [0003–0006]).
However, Nishi, Chen and Manhart fail(s) to teach the geometric parameters of the occluded vessels comprise median line and diameter for each occluded vessel; the composition parameters of the occlusion comprise position, length and composition of the occlusion.
Hong, working in the same field of endeavor, teaches: the geometric parameters of the occluded vessels comprise median line and diameter for each occluded vessel (See Hong, [Pg. 3, Col. 2, The Measurement of ABZA], The ABZA was measured as previously reported (6). Briefly, the projection of intracranial bifurcation of internal carotid artery on the median line of sagittal suture is defined as the vertex, and the median line of sagittal suture as the starting edge on the DSA image of Tang’s position. [Pg. 3, Col. 2, first 3 lines], simple stent, or stent combined with aspiration treatment. Stent (4 × 20mm or 6 × 30mm Solitair FR stent, EV3, CA, USA), was selected according to vessel diameter and thrombus length).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s and Manhart’s reference to the geometric parameters of the occluded vessels comprise median line and diameter for each occluded vessel based on the method of Hong’s reference. The suggestion/motivation would have been to guide patients for the proper treatment (See Hong, [Pg. 2, Conclusion]).
However, Nishi, Chen, Manhart and Hong fail(s) to the composition parameters of the occlusion comprise position, length.
Janardhan, working in the same field of endeavor, teaches: the composition parameters of the occlusion comprise position, length (See Janardhan, ¶ [1033], The length and diameter of the thrombus 500 can be measured, from which the volume of the thrombus 500, or the clot burden, can be calculated).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s, Manhart’s and Hong’s reference the composition parameters of the occlusion comprise position, length based on the method of Janardhan’s reference. The suggestion/motivation would have been to provide different treatment of strokes (See Janardhan, ¶ [0003–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Janardhan with Nishi, Chen, Manhart and Hong to obtain the invention as specified in claim 8.
Regarding claim 9, Nishi in view of Chen further in view of Manhart further in view of Hong and further in view of Janardhan teaches method according to claim 8, [wherein the step of extracting geometric parameters of occluded vessels and of the composition parameters of the occlusion from the clinical images, provides for processing the clinical images using a grayscale analysis algorithm to extract the composition parameters of the occlusion].
However, Nishi and Chen fail(s) to teach wherein the step of extracting geometric parameters of occluded vessels and of the composition parameters of the occlusion from the clinical images, provides for processing the clinical images using a grayscale analysis algorithm to extract the composition parameters of the occlusion.
Manhart, working in the same field of endeavor, teaches: wherein the step of extracting geometric parameters of occluded vessels and of the composition parameters of the occlusion from the clinical images (See Manhart, ¶ [0072], The three-dimensional vessel image includes information on the spatial arrangement of vessels, that are mapped in the at least one further artifact-reduced image dataset. The information on the spatial arrangement of the vessels may for example include at least one centerline for each vessel and/or information on extension and/or information on branching and/or information on the course. ¶ [0075], The identification of the vascular occlusions by applying the method for identifying vascular occlusions may include a determination of a spatial extent and/or a spatial course and/or a position and/or an alignment and/or a morphological condition and/or a density of the respective vascular occlusion in the further artifact-reduced image dataset), provides for processing the clinical images using a grayscale analysis algorithm to extract the composition parameters of the occlusion (See Manhart, ¶ [0098], For example, the determination of the image values using the phases of the plurality of second computed tomography datasets may include the assignment of a tuple to each of the vessels in the vessel image, for example a color value, for example an RGB value and/or a gray value. Further, the image values may be determined in dependence on a threshold value of the respective phase of the perfusion being undershot or exceeded in each case).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s and Chen’s reference to wherein the step of extracting geometric parameters of occluded vessels and of the composition parameters of the occlusion from the clinical images, provides for processing the clinical images using a grayscale analysis algorithm to extract the composition parameters of the occlusion based on the method of Manhart’s reference. The suggestion/motivation would have been to provide accurate and adequate treatment (See Manhart, ¶ [0003–0006]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Manhart with Nishi and Chen to obtain the invention as specified in claim 9.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al. (See NPL attached, "Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning", hereafter, "Nishi") in view of Chen et al. (CN 111754452 A, hereafter, "Chen") further in view of Manhart et al. (US 2021/0004997 A1, hereafter, "Manhart") further in view of Janardhan et al. (US 2018/0368965 A1, hereafter, "Janardhan") and further in view of Laksari et al. (US 2022/0093267 A1, hereafter, "Laksari").
Regarding claim 5, Nishi in view of Chen further in view of Manhart teaches method according to claim 1, [wherein the step of selecting indicator parameters comprises the sub-steps of:
calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique;
calculating morphological parameters of the occlusion, by analysis of length, diameter and composition of the occlusion].
However, Nishi and Chen fail(s) to teach wherein the step of selecting indicator parameters comprises the sub-steps of: calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique; calculating morphological parameters of the occlusion, by analysis of length, diameter and composition of the occlusion.
Manhart, working in the same field of endeavor, teaches: wherein the step of selecting indicator parameters (See Manhart, ¶ [0060], further, a plurality of maximum intensity projection images (MIP) may be created using the three-dimensional vessel image. Moreover, a further evaluation dataset may be created by applying a method for evaluating a manifestation of the identified vascular occlusions to the maximum intensity projection images) comprises the sub-steps of:
composition of the occlusion (See Manhart, ¶ [0050], The at least one tissue parameter of the thrombus may, for example, include a chemical composition and/or a density and/or a solubility).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s and Chen’s reference to wherein the step of selecting indicator parameters comprises the sub-steps of: composition of the occlusion based on the method of Manhart’s reference. The suggestion/motivation would have been to provide accurate and adequate treatment (See Manhart, ¶ [0003–0006]).
However, Nishi, Chen and Manhart fail(s) to calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique; calculating morphological parameters of the occlusion, by analysis of length, diameter.
Janardhan, working in the same field of endeavor, teaches: calculating morphological parameters of the occlusion, by analysis of length, diameter (See Janardhan, ¶ [1033], The length and diameter of the thrombus 500 can be measured, from which the volume of the thrombus 500, or the clot burden, can be calculated).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s and Manhart’s reference calculating morphological parameters of the occlusion, by analysis of length, diameter based on the method of Janardhan’s reference. The suggestion/motivation would have been to provide different treatment of strokes (See Janardhan, ¶ [0003–0005]).
However, Nishi, Chen, Manhart and Janardhan fail(s) to teach calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique.
Laksari, working in the same field of endeavor, teaches: calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique (See Laksari, ¶ [0010], By applying a multi-scale second-order Gaussian smoothing filter, we improve signal to noise ratio in the multiple length scales of the cerebrovascular network. We then segment the vessels, using a method such as Chan-Vese active contours level-set algorithm, to achieve a high level of approximation).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s, Manhart’s, and Janardhan reference to calculating characteristic parameters of the geometry of the vessels by analysing the entire geometry of the occluded vessels using the level set technique based on the method of Laksari’s reference. The suggestion/motivation would have been the prediction and prevention of strokes (See Laksari, ¶ [0001–0003]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Laksari with Nishi, Chen, Manhart and Janardhan to obtain the invention as specified in claim 5.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al. (See NPL attached, "Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning", hereafter, "Nishi") in view of Chen et al. (CN 111754452 A, hereafter, "Chen") further in view of Manhart et al. (US 20210004997 A1, hereafter, "Manhart") and further in view of Mousavi et al. (See NPL attached, "Realistic computer modelling of stent retriever thrombectomy", hereafter, "Mousavi").
Regarding claim 11, Nishi in view of Chen further in view of Manhart teaches method according to claim 1, wherein:
[the step of training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy procedure provides that the thrombectomy surgery employs at least one thrombectomy procedure and at least one respective biomedical device];
prior to the step of calculating the likelihood of success of thrombectomy surgery (See Nishi, [Pg. 2380, Col. 2, ln. 14–18], Here, we report the prediction model of LVO based on the machine learning algorithm only with commonly collected pretreatment variables to be utilized in making the decision to perform MT, and we compare the model with the previously reported prediction models), there is a further step of:
selecting at least one thrombectomy procedure and at least one respective biomedical device to be used in the thrombectomy surgery (See Nishi, [Pg. 2382, Col. 2, Procedural and Clinical Results for All Patients, first 2 lines], A stent retriever was used in 304 patients (78.5%), and an aspiration catheter was used in 114 patients (29.4%)).
However, Nishi fail(s) to teach the step of training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy procedure provides that the thrombectomy surgery employs at least one thrombectomy procedure and at least one respective biomedical device.
Chen, working in the same field of endeavor, teaches: the step of training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy procedure (See Chen, ¶ [0045], Specifically, the magnetic resonance imaging (MRI) samples of the subjects to be tested can be obtained from MRI images of thrombotic patients provided by clinicians, or they can be obtained from MRI images simulated by the system. Once the training set is assembled, the thrombus region needs to be bounded on the MRI image, that is, the exact location of the detection box needs to be drawn).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s reference the step of training the predictive algorithm by processing the data using machine learning techniques on a predefined number of finite element numerical simulations of the thrombectomy procedure based on the method of Chen’s reference. The suggestion/motivation would have been to effect of rapidly clearing or reducing thrombus burden and relieving venous thrombosis (See Chen, ¶ [0007–0008]).
However, Nishi and Chen and Manhart fail(s) to the thrombectomy surgery employs at least one thrombectomy procedure and at least one respective biomedical device.
Mousavi, working in the same field of endeavor, teaches: the thrombectomy surgery employs at least one thrombectomy procedure and at least one respective biomedical device (See Mousavi, [Pg. 2, Col. 1, ln. 38–40], In this study, we present a novel, full-physics approach to simulate packaging, delivery, deployment and clot extraction during stent retriever MT).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Nishi’s, Chen’s and Manhart’s reference the thrombectomy surgery employs at least one thrombectomy procedure and at least one respective biomedical device based on the method of Mousavi’s reference. The suggestion/motivation would have been to accurately simulate thrombectomy surgery for different conditions (See Mousavi, [Pg. 2, Col. 1, ln. 1–60]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Mousavi with Nishi, Chen and Manhart to obtain the invention as specified in claim 11.
Allowable Subject Matter
Claim(s) 3, 4, 6 and 10 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.
Claim(s) 3, 4, 6 and 10 contain subject matter that is not disclosed or made obvious in the cited art.
In regard to claim 3, when considering claim 3 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“wherein the morphological parameters of the vessels comprise one or more parameters chosen from the angles that form where the Internal Carotid Artery bifurcates onto the Anterior Cerebral Artery and Middle Cerebral Artery, the average diameters of the Middle Cerebral Artery and Anterior Cerebral Artery, and characteristic parameters of the carotid siphon of the Internal Carotid Artery”.
In regard to claim 6, when considering claim 6 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“wherein the step of calculating characteristic parameters of the vessel geometry comprises the sub-steps of:
defining a fixed volume scaled to be able to accommodate all possible patient vascular geometries;
discretising the volume in a grid according to a sensitivity analysis;
placing each reconstructed vascular geometry into the grid in the three-dimensional model of the occluded vessels and of the occlusion and calculating a matrix of the same size as the number of grid points by measuring the distances between each grid point and the nearest point of the vascular geometry;
calculating the characteristic parameters of the geometry of the vessels by processing the matrix using a technique of principal component analysis”.
In regard to claim 10, when considering claim 10 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“wherein the step of calculating the likelihood of success of the thrombectomy surgery with removal of the occlusion by processing the indicator parameters using the predictive algorithm comprises the sub-step of:
calculating the likelihood of an occlusal fracture occurring by processing the indicator parameters using the predictive algorithm”.
In regard to claim 4, claim 4 depend on objected claim 3. Therefore, by virtue of their dependency, claim 4 is also indicated as objected subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Faber (US 20170100029 A1) teaches the present invention provides a retinal predictor index (RPI), composed of discrete geometric and fractal descriptors of the branch-patterning of the outer retinal circulation, as a biomarker for differences in the extent (number and diameter) of collateral blood vessels in brain, heart, lower extremities and other tissues.
Goyal et al. (US 20220338929 A1) teaches the invention relates to systems and methods for assisting a physician in making treatment decisions and to assist in the planning of endovascular surgical procedures. In particular, the invention relates to 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 (or other) based on objective assessment of the likelihood of success by a specific access point and having consideration to the available endovascular equipment and a particular patient's anatomy
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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