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 .
Claim Objections
Claims 8 and 13 are objected to because of the following informalities:
Claim 8 recites the limitation “the one or processor”, however, the limitation should read –the one or more processors—
Claim 13 recites the limitation “the presence of the implantable device-induced inflammation”, however, no presence of any implantable device-induced inflammation has been recited previously. The limitation should read –a presence of an implantable device-induced inflammation.
Appropriate correction is required.
Claim interpretation
Claims 1, 16, and 17 recite the limitations “to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device” and “for the implantable device extraction procedure” which are directed toward intended use. Examiner notes that limitations directed towards intended use must result in a structural difference between the claimed invention and the prior art in order to distinguish from the prior art. It is therefore noted that the analyzing of the data of the prior art must merely be capable of being used to determine an amount of adhesion and the guidance information of the prior art must merely be capable of being used for the implantable device extraction in order to read on the claimed invention.
Claim 7 recites the limitation “to determine a value of a risk metric” which is directed toward intended use. Examiner notes that limitations directed towards intended use must result in a structural difference between the claimed invention and the prior art in order to distinguish from the prior art. It is therefore noted that the analyzing of the data of the prior art must merely be capable of being used to determine a value of a risk metric in order to read on the claimed invention.
Claim 11 recites the limitation “to determine a presence of implantable device-induced inflammation in the anatomical region” which is directed toward intended use. Examiner notes that limitations directed towards intended use must result in a structural difference between the claimed invention and the prior art in order to distinguish from the prior art. It is therefore noted that the analyzing of the data of the prior art must merely be capable of being used to determine a presence of implantable device-induced inflammation in the anatomical region in order to read on the claimed invention.
Claim 12 recites the limitation to determine “to determine a stability metric for the implantable device”. which is directed toward intended use. Examiner notes that limitations directed towards intended use must result in a structural difference between the claimed invention and the prior art in order to distinguish from the prior art. It is therefore noted that the analyzing of the data of the prior art must merely be capable of being used to determine a stability metric in order to read on the claimed invention.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more.
In a test for patent subject matter eligibility, the claims pass Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter.
When assessed under Step2A, Prong I, Independent claims 1 and 17 are found to recite a judicial exception (i.e. abstract idea). In this instance, claims 1 and 16-17 recite the limitations “analyze/analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device”. The cited limitation(s), under their broadest reasonable interpretation, encompass a mental process (i.e. abstract idea) of analyzing data which can be performed in the mind or by a human using a pen and a paper (e.g. observation, evaluation, judgment, opinion). In other words, a person could reasonably analyze attenuation data via observation/evaluation. Examiner notes that with the exception of generic computer-implemented steps (e.g. one or more processors recited in claim 1 and non-transitory computer-readable storage medium in claim 16), there is nothing in the claims that preclude the limitation from being performed by a human, mentally or with pen and paper, thus the cited limitation(s) recites a judicial exception (MPEP 2106.04(a)) and the claim must be reviewed under Step 2A, Prong II to determine patent eligibility.
Step 2A, Prong II determines whether any claim recites an additional element that integrates the judicial exception into a practical application. Independent claims recites the following additional element(s):
Receive/receiving attenuation data representing an implantable device in an anatomical region, the attenuation data defining X-ray attenuation within the anatomical region
Outputting guidance information for the implantable device extraction procedure based on the amount of adhesion
The additional element(s) in the cited independent claim(s) are not found to integrate the judicial exception into a practical application. In this case, receiving attenuation data amounts to merely insignificant pre-solution activity in the field of X-ray attenuation and outputting guidance information amounts to merely insignificant post-solution activity of outputting results/data. These elements are seen as adding insignificant extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use. Therefore, under step 2A Prong II the Judicial exception is not integrated into a practical application by additional elements of independent claims 1 and 16-17 and the claims must be reviewed under Step 2B to determine patent eligibility.
Step 2B determines where a claim amounts to significantly more.
The additional element(s) listed above do not amount to significantly more than the judicial exception. In this instance, as noted above, the additional elements amount to merely insignificant extra-solution activity of data gathering and outputting results. Additionally there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Therefore, under Step 2B in a test for patent subject matter eligibility, the judicial exception of the independent claim(s) do not amount to significantly more and the independent claim(s) remain patent ineligible.
Dependent claims 2-15, and 18-20 further limit the abstract idea of independent claims 1 and 16. When analyzed as a whole, these claims are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed towards an abstract idea and do not sufficiently integrate the subject matter into a practical application or recite elements which constitute significantly more than the abstract ideas identified. The dependent claims are directed toward additional elements which encompass abstract ideas
In this instance, dependent claims recite the following limitations:
Determine a Hounsfield unit attenuation value of the tissue contacting the implantable device (claims 2 and 18)
Determine the amount of adhesion between the implantable device and the tissue contacting the implantable device based on the Hounsfield Unit attenuation value (claims 2 and 18)
Determine an amount of adhesion between the implantable device and a tissue contacting the implantable device, is performed for a plurality of positions along a length of the implantable device (claim 5)
Determine a total amount of adhesion between the implantable device and a tissue contacting the implantable device, based on the amount of adhesion between the implantable device and the tissue contacting the implantable device at the plurality of positions along the implantable device (claim 6)
Analyze the attenuation data to determine a value of a risk metric, the value of the risk metric representing a risk of damage to a tissue represented in the attenuation data and/or a risk of fracturing the implantable device (claim 7)
Predicting the amount of adhesion between the implantable device and a tissue contacting the implantable device (claim 8)
Quantify the amount of adhesion between the implantable device and a tissue contacting the implantable device with one or more adhesion classes, or adhesion scores (claim 10)
Analyze the attenuation data to determine a presence of implantable device-induced inflammation in the anatomical region (claim 11)
Analyze the attenuation data to determine a stability metric for the implantable device (claim 12)
Calculate a recommended length of the implantable device to be extracted in the implantable device extraction procedure, the recommended length being calculated based on at least one of the amount of adhesion between the implantable device and a tissue contacting the implantable device, the stability metric, or the presence of the implantable device induced inflammation (claim 13)
Segment the received attenuation data to identify at least the implantable device, prior to analyzing the attenuation data (claim 14)
The cited limitation(s), under their broadest reasonable interpretation, encompass mental processes (i.e. abstract idea) which can be performed in the mind or by a human using a pen and a paper (e.g. observation, evaluation, judgment, opinion). In other words, a human could reasonably determine a Hounsfield unit attenuation value via observation/evaluation of attenuation data/pixel or voxel values, determine the amount of adhesion via observation/evaluation, determine the amount of adhesion for a plurality of positions and a total amount of adhesion via observation/evaluation, quantify the amount of adhesion, analyze the attenuation data, predict the amount of adhesion, calculate a recommended length and segment the received attenuation data via observation/evaluation. Examiner notes that with the exception of generic computer-implemented steps (e.g. the one or more processors and the neural network of claim 8), there is nothing in the claims that preclude the limitation from being performed by a human, mentally or with pen and paper, thus the claimed limitation is considered to be directed towards a judicial exception (MPEP 2106.04(a)).
Under Step 2A, Prong II for dependent claims 2-15 and 18-20 present additional elements which only further narrow the judicial exceptions (e.g. claims 3 and 19 which further narrow the nature of the attenuation data merely directed to the technological field of multiple energy x-ray/computed tomography, claims 4 and 20 which further introduce applying a material decomposition algorithm which is found to be insignificant extra-solution activity of data processing in the field of multiple energy x-ray/computed tomography, claims 6-7 and 9 which further narrow the nature of the outputting, claim 8 which further recites inputting the received attenuation data into a neural network amounting to merely inputting data and further narrowing the nature in which the neural network is trained which amounts to merely a generic training a neural network, claims 11-13 which further introduce outputting indications amounting to merely insignificant post-solution activity of outputting data/results and claim 15 which further narrows the nature of the implantable device) and provide no additional element which are found to integrate the judicial exception into a practical application.
These dependent claims include no additional claims that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
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.
(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 1-2, 5-7, 9-12, and 14-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WIPO Grass et al. (WO 2020064518 A1), hereinafter Grass.
Regarding claims 1, 16, and 17,
Grass discloses a system for providing guidance information for an implantable device extraction procedure, the system comprising:
One or more processors (at least fig. 1 (2, 3, and 5) and corresponding disclosure in at least pg. 10 lines 14-19 and pg. 11 lines 22-29) configured to:
Receive attenuation data representing an implantable device in an anatomical region, the attenuation data defining X-ray attenuation within the anatomical region (pg. 10 lines 20-31 which discloses the input unit 2 may be adapted for receiving a cardiac CT dataset of the thorax and the heart of the subject, e.g. including the entire lead, e.g. including a volume from a pacemaker pocket to the electrode ends and pg. 2 lines 24-35 which discloses receiving a live stream of x-ray projection image data. Examiner notes that both CT data and x-ray projection image data are attenuation data defining x-ray attenuation)
Analyze X-ray data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device (pg. 4 lines 23-27 which discloses the preprocessor may be adapted for determining the degree of local adhesion to the vessel wall by quantifying a local motion of the lead relative to the vessel wall from a temporal sequence of three-dimensional image data received via the input and determining a degree of local adhesion based on the local motion and pg. 11 lines 4-12 which discloses the pre-processor may be adapted for determining parameters of interest from the segmented lead. For example a degree of local adhesion of the lead tip see also pg. 17 lines 21-32 disclosing registering the fitted three-dimensional parametric model to the live stream of x-ray projection image data to correlate the position of the extraction device in the live stream); and
Output guidance information for the implantable device extraction procedure based on the amount of adhesion (pg. 4 lines 16-22 which discloses the output may be adapted for outputting the parameter corresponding to a longitudinal position or ahead of the determined position of the extraction device. This parameter may comprise a local curvature and/or a degree of local adhesion of the lead and or a proximity of the lead to the vessel wall. Examiner notes that such outputting of the parameter is considered guidance information for the implantable device extraction procedure in its broadest reasonable interpretation)
Examiner notes that the system of Grass would further comprise the non-transitory computer-readable medium which cause the processor(s) to perform the corresponding functions of claim 16 and would perform the method of claim 17 having corresponding method steps.
Regarding claims 2 and 18,
Grass further discloses wherein analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device, comprises the one or more processor further configured to:
Determine a Hounsfield unit attenuation value of the tissue contacting the implantable device ( pg. 10 lines 1-5 which discloses the 3D image volume may comprise an image volume representative of a segmentation of the lead, e.g. in which the presence or absence of the lead at each voxel location is encoded in the voxel values. And pg. 10 lines 15-20 which discloses The pre-processor 5, further
discussed hereinbelow, may be adapted for segmenting the lead from the image volume or
image volumes to provide the 3D image volume in which the lead is segmented from 3D
image data. The pre-processor 5 may be adapted for fitting a 3D model, e.g. a parametric 3D
model, to the segmented 3D image of the lead. Examiner notes that such segmentation of the lead and/or 3D image data necessarily requires determination of a Hounsfield unit attenuation value (i.e. voxel value from CT image data)); and
determine the amount of adhesion between the implantable device and the tissue contacting the implantable device based on the Hounsfield Unit attenuation value (pg. 11 lines 4-9 which discloses the pre-processor 5 may be adapted for determining parameters of interest 5 from the segmented lead, e.g. from the 3D image volume in which the lead is segmented and/or based on the parametric 3D model. For example, a local curvature of the lead as function of a longitudinal position along the lead may be determined. For example, a degree of local adhesion of the lead tip, and/or of the lead as function of the longitudinal position along the lead, may be determined).
Regarding claim 5,
Grass further discloses wherein the analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device, is performed for a plurality of positions along a length of the implantable device (pg. 11 lines 8-21 which discloses the preprocessor 5 may be adapted for determining the local adhesion of the lead to the wall of the body structure that contains the lead. For example, the 4D dataset may comprise a plurality of 15 3D images corresponding to a plurality of points in time extending over at least one cardiac cycle and/or at least one breathing cycle, such that the local motion of the lead relative to the vessel wall, induced by the natural motion of the body, can be quantified, in which regions of the lead which shows a large motion relative to the wall, e.g. a large variability of its position relative to the wall, are indicative of a low or no adhesion, and in which regions of the lead 20 which show a small motion relative to the wall are indicative of a high adhesion. Thus, the adhesion can be quantified or determined in a straight-forward manner)
Regarding claim 6,
Grass further discloses wherein the one or more processors are configured to determine a total amount of adhesion between the implantable device and a tissue contacting the implantable device, based on the amount of adhesion between the implantable device and the tissue contacting the implantable device at the plurality of positions along the implantable device (pg. 11 lines 8-21 which discloses for example, the 4D dataset may comprise a plurality of 15 3D images corresponding to a plurality of points in time extending over at least one cardiac cycle and/or at least one breathing cycle, such that the local motion of the lead relative to the vessel wall, induced by the natural motion of the body, can be quantified, in which regions of the lead which shows a large motion relative to the wall, e.g. a large variability of its position relative to the wall, are indicative of a low or no adhesion, and in which regions of the lead 20 which show a small motion relative to the wall are indicative of a high adhesion. Thus, the adhesion can be quantified or determined in a straight-forward manner); and
Wherein the outputting guidance information for the implantable device extraction procedure, is based on the total amount of adhesion (pg. 14 lines 21-23 which discloses the output 4 may also provide additional information. For example, the output 4 may be adapted for outputting the local curvature and/or the degree of local adhesion at the determined position of the extraction device and pg. 17 lines 32-35 which discloses the parameter may comprise a degree of local adhesion. Examiner notes that any outputting of the adhesion at any position would necessarily be based on the total amount of adhesion determined).
Regarding claim 7,
Grass further discloses wherein the one or more processors are further configured to analyze the attenuation data to determine a value of a risk metric (pg. 3 lines 2-4 which discloses the processing unit is further adapted to determine a vessel and/or plaque characteristic at or near said determined position of said extraction device. Examiner notes that such a vessel or plaque characteristic is considered a risk metric in its broadest reasonable interpretation), the value of the risk metric representing a risk of damage to a tissue represented in the attenuation data (pg. 14 line 27 – pg. 15 line 15 which discloses vessel and/or plaque characteristics at (or near) the determined position of the extraction device may be determined from the 3D image data and after a region with hard plaque where a strong pressure needs to be applied to advance the extraction device and unexpected sudden advance of the cutting tip, once the hard plaque is passed could cause damage to the vessel. This can be advantageously avoided by automatically turning off the cutting device in regions with low adhesion, or after the characteristic sudden advance after a region with hard plaque).
Regarding claim 9,
Grass further discloses wherein the outputting guidance information for the implantable device extraction procedure, comprises outputting an indication of one or more of:
The amount of adhesion between the implantable device and a tissue contacting the implantable device (pg. 4 lines 19-22 which discloses output may be adapted for outputting the parameter corresponding to 20 a longitudinal position at or ahead of the determined position of the extraction device. This parameter may comprise a local curvature and/or a degree of local adhesion of the lead and/or a proximity of the lead to the vessel wall), an outcome metric for the implantable device extraction procedure (examiner notes that any of the output information is an outcome metric in its broadest reasonable interpretation), a recommended device setting for use in the implantable device extraction procedure, a recommended procedural step for use in the implantable device extraction procedure (pg. 17 lines 7-9 which discloses the at least one parameter of the extraction device may comprise a power setting and/or an on/off setting of the extraction device. This controlling 105 may comprise (e.g. temporarily) deactivating the extraction device when the detected position corresponds to a region of low adhesion of the lead to the vessel wall and/or a softer region after a hard plaque. See also pg. 14 lines 27 through pg. 15 lines 6)
Regarding claim 10,
Grass further discloses wherein the analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device, comprises the one or more processors further configured to quantify the amount of adhesion between the implantable device and a tissue contacting the implantable device with one or more adhesion classes, or adhesion scores (pg. 11 lines 8-21 which discloses The preprocessor 5 may be adapted for determining the local adhesion of the lead to the wall of the body structure that contains the lead. For example, the 4D dataset may comprise a plurality of 3D images corresponding to a plurality of points in time extending over at least one cardiac cycle and/or at least one breathing cycle, such that the local motion of the lead relative to the vessel wall, induced by the natural motion of the body, can be quantified, in which regions of the lead which shows a large motion relative to the wall, e.g. a large variability of its position relative to the wall, are indicative of a low or no adhesion, and in which regions of the lead which show a small motion relative to the wall are indicative of a high adhesion. Thus, the adhesion can be quantified or determined in a straight-forward manner).
Regarding claim 11,
Grass further discloses wherein the one or more processors are further configured to:
Analyze the attenuation data to determine a presence of implantable device-induced inflammation in the anatomical region (pg. 3 lines 25-26 which discloses the processing unit may be adapted for determining a vessel and/or plaque characteristic at or near the determined position of the extraction device. Examiner notes that such analyzing to determine a plaque characteristic is considered to be capable of being used to determine a presence of implantable device-induced inflammation in the anatomical region as plaque is considered to be caused by inflammation) and
Output an indication of the presence of the implantable device-induced inflammation (pg. 3 lines 2-5 which discloses the processing unit is further adapted i) to determine a vessel and/or plaque characteristic at or near said determined position of said extraction device and ii) to control said at least one parameter of said extraction device by taking said characteristic into account. Examiner notes that such control requires outputting control information which is considered to be an indication of the presence of the implantable device-induced inflammation (e.g. an indication of the existence of the plaque).
Regarding claim 12,
Grass further discloses wherein the one or more processors are further configured to:
Analyze the attenuation data to determine a stability metric for the implantable device , the 4D dataset may comprise a plurality of 3D images corresponding to a plurality of points in time extending over at least one cardiac cycle and/or at least one breathing cycle, such that the local motion of the lead relative to the vessel wall, induced by the natural motion of the body, can be quantified, in which regions of the lead which shows a large motion relative to the wall, e.g. a large variability of its position relative to the wall, are indicative of a low or no adhesion, and in which regions of the lead which show a small motion relative to the wall are indicative of a high adhesion. Examiner notes that the variability in motion is considered a stability metric in its broadest reasonable interpretation); and
Output an indication of the stability metric (pg. 4 lines 16-22 which discloses the output may be adapted for outputting the parameter corresponding to a longitudinal position or ahead of the determined position of the extraction device. This parameter may comprise a local curvature and/or a degree of local adhesion of the lead and or a proximity of the lead to the vessel wall. The degree of local adhesion being an indication of the stability metric (i.e. variability in motion)).
Regarding claim 14,
Grass further discloses wherein the one or more processors are further configured to segment the received attenuation data to identify at least the implantable device, prior to the analyzing the attenuation data (pg. 10 lines 15-17 which discloses the pre-processor 5, further discussed hereinbelow, may be adapted for segmenting the lead from the image volume or image volumes to provide the 3D image volume in which the lead is segmented from 3D image data)
Regarding claim 15,
Grass further discloses wherein the implantable device comprises a pacemaker lead, or an implantable cardioverter defibrillater lead, or a pacemaker, or an implantable cardioverter defibrillator, or an IVC filter (pg. 1 lines 27-29 which disclose It is an advantage of embodiments of the present invention to provide good, efficient and useful image guidance during a procedure of extracting an implanted lead(s), e.g. of a pacemaker or defibrillator and pg. 8 lines 23-25 which disclose during an extraction procedure for extracting at least one lead from the body of a subject, e.g. an implanted lead, e.g. a lead wire such as a conductive 25 wire of a pacemaker, a defibrillator, neurostimulator and/or an electric potential sensor)
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.
Claims 3-4 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Grass in view of Zhao et al. (US 20210244374 A1), hereinafter Zhao.
Regarding claims 3 and 19,
Grass teaches the elements of claims 1 and 16 as previously stated. Grass further teaches wherien the attenuation data comprises spectral attenuation data, and wherein the spectral attenuation data defines the X-ray attenuation within the anatomical region (pg. 9 lines 27-30 which discloses the 3D image data may comprise multiple coregistered image volumes, e.g. multiple spectral CT image volumes)
Grass fails to explicitly teach wherein the spectral attenuation data defines the x-ray attenuation data within the anatomical region in a plurality of different energy intervals.
Zhao, in a similar field of endeavor involving X-ray imaging, teaches wherein attenuation data comprises spectral attenuation data, and wherein the spectral attenuation data defines x-ray attenuation within an anatomical region in a plurality of different energy intervals ([0104] which discloses the x-ray imaging apparatus and methods disclosed herein can be multi-energy and a multi energy system at each energy level (also known as spectral imaging, if the application requires the use of multiple energy x-ray imaging and analysis for material decomposition).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Grass to include spectral attenuation data as taught by Zhao in order to allow for material decomposition to be performed which would allow for characterization of a relative position and structure of a region of cardiac tissue where an implant exists to be performed with greater precision and/or to allow for differentiation of a heart valve implant based on multiple energy decomposition (Zhao [0592]-[0593]). Additionally/alternatively, such a modification amounts to merely a simple substitution of one known spectral attenuation data for another yielding predictable results with respect to cardiac implant imaging thereby rendering the claim obvious (MPEP 2143).
Regarding claims 4 and 20,
Grass, as modified, teaches the elements of claims 3 and 19 as previously stated. Grass, as currently modified, fails to explicitly teach wherein analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device, comprises the one or more processor further configured to: apply a material decomposition algorithm to the spectral attenuation data to identify a type of tissue contacting the implantable device; and
Determine the amount of adhesion between the implantable device and the tissue contacting the implantable device based on the type of tissue.
Nonetheless, Zhao further teaches wherein one or more processors are configured to: apply a material decomposition algorithm to the spectral attenuation data to identify a type of tissue contacting an implantable device ([0025] which discloses the processor can be configured to output material decomposition analysis of the imaging subject having two or more materials. The material decomposition analysis can be based at least in part on a database of x-ray measurement properties of different materials).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Grass, as currently modified, to include applying a material decomposition algorithm to the spectral attenuation data as taught by Zhao in order to allow for characterization of a relative position and structure of a region of cardiac tissue where an implant exists to be performed with greater precision and/or to allow for differentiation of a heart valve implant based on multiple energy decomposition (Zhao [0592]-[0593]).
Examiner notes in the modified system the determination of the amount of adhesion between the implantable device and the tissue contacting the implantable device would be based on the segmentation/tracking of the implant which is enhanced due to the material decomposition of Zhao, therefore, the amount of adhesion is based on the result of the material decomposition in its broadest reasonable interpretation. Additionally/alternatively the amount of adhesion would be based on the type of tissue regardless (i.e. the type of tissue of which the implant is adhered) due to the breadth of “based on”.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Grass in view of WIPO Panse et al. (WO 2022128703 A1), hereinafter Panse.
Regarding claim 8,
Grass teaches the elements of claim 8 as previously stated. Gras further teaches wherien analyzing the attenuation data to determine an amount of adhesion between the implantable device and a tissue contacting the implantable device comprises the one or more processor further configured to:
Input the received attenuation data into a neural network (pg. 11 lines 1-3 which discloses the input may receive the 3D image data and the pre-processor may perform a machine learning based artefact removal using e.g. a trained convolutional neural network for metal artefacts and/or motion artefacts); and
Predicting, using the neural network, the amount of adhesion between the implantable device and a tissue contacting the implantable device (Examiner notes that the amount of adhesion is necessarily predicted using the neural network (e.g. using image data having artefacts removed by the neural network).
Grass fails to explicitly teach wherein the neural network is trained to predict the amount of adhesion between the implantable device and a tissue contacting the implantable device using training attenuation data comprising a plurality of training images representing the implantable device in an anatomical region, and ground truth data comprising ground truth adhesion values corresponding to the training images, the ground truth adhesion values representing the amount of adhesion between the implantable device and a tissue contacting the implantable device.
Panse, in a similar field of endeavor involving X-ray evaluation of Implant adhesion, teaches wherein a neural network is trained to predict an amount of adhesion between an implantable device and a tissue contacting the implantable device (pg. 4 lines 6-12 which discloses the machine learning algorithm outputs an adherence level and pg. 16 lines 15-17 which discloses examples of machine learning algorithms include artificial neural networks) using training attenuation data comprising a plurality of training images representing the implantable device in an anatomical region, and ground truth data comprising ground truth adhesion values corresponding to the training images, the ground truth adhesion values representing the amount of adhesion between the implantable device and a tissue contacting the implantable device (pg. 14 lines 25-30 which discloses the training data used to train the machine learning algorithm 302 consists of motion vectors 202 of the implanted lead 104, motion vectors of blood vessels in which the leads 104 are commonly implanted and the ground truth (i.e. is the lead 104 adhered to the blood vessel 102… the ground truth can be obtained from pre-operational CT images (i.e. training images)).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Grass to include a neural network as taught by Panse in order to provide an improved method of determining the adhesion of the lead inside a blood vessel (Panse pg. 2 lines 23-25).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Grass in view of NPL Holm et al. (“Algorithm for the analysis of pre-extraction computed tomographic images to evaluate implanted lead-lead interactions and lead-vascular attachments”), hereinafter Holm.
Regarding claim 13,
Grass teaches the elements of claim 12 as previously stated. Grass fails to explicitly teach wherein the one or more processors are further configured to:
Calculated a recommended length of the implantable device to be extracted in the implantable device extraction procedure, the recommended length being calculated based on at least one of the amount of adhesion between the implantable device and a tissue contacting the implantable device, the stability metric, or the presence of the implantable device-induced inflammation; and
Output an indication of the recommended length.
Holm, in a similar field of endeavor involving x-ray evaluation of implants and adhesion thereof, teaches one or more processors configured to:
calculate a recommended length of an implantable device to be extracted in the implantable device extraction procedure, the recommended length being calculated based on at least one of an amount of adhesion (pg. 1013 which discloses The length of each lead was averaged across the 30 leads analyzed. The average lengths of red and yellow zones were also recorded. See also pg. 1014 results which discloses the average lead length was 69.8 +- 14.9 mm and the length of the red zone was 1.9 +-1.7 mm and the length of the yellow zones was 1.3 +-1.2 mm and pg. 1013 which discloses red zones represent lead-vessel adherence, yellow zones indicate possible adherence and green zones represent no lead vessel adherence); and
Output an indication of the recommended length (See table 1).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Grass to include calculating a recommended length as taught by Holm in order to provide a user with additional information regarding the amount of adhesion. Such a modification would allow a user to readily recognize how much of the lead is implanted as well as how much of the lead exhibits adhesion or possible adhesion.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROOKE L KLEIN whose telephone number is (571)270-5204. The examiner can normally be reached Mon-Fri 7:30-4.
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/BROOKE LYN KLEIN/Primary Examiner, Art Unit 3797