DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to the remarks filed on 1/30/2026.
The amendments filed on 1/30/2026 have been entered. Accordingly claims 22-41 remain pending.
The claim rejections under 35 USC 112 have been withdrawn in light of the amendments and the applicant’s remarks that the “cluster analysis” to be interpreted as cluster of data obtained under BRI.
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 22-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim 32 recites “determine predicted blood-flow values”, “perform a cluster analysis” etc.
The limitation of “determine”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “determine” in the context of this claim encompasses the user manually calculating the values in mind or with the help of simple pen and paper. Similarly, the limitation of “perform cluster analysis”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “by a processor” language, “perform” in the context of this claim encompasses the user thinking that the where the values cluster. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the limitation of “determining and performing”. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “determining and performing” such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform these steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The depending claims also recite similar abstract ideas without additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Therefore, the claims are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. IEEE Transactions on Visualization and Computer Graphics ( Volume: 20, Issue: 5, May 2014).
Claims 22-27, 31-37 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitt et al (WO2014092755 cited in the IDS, 2014-06-19 below citations are from US20150297373), (hereinafter “Schmitt”) in view of Petraco et al (Classification performance of instantaneous wave-free ratio (iFR) and fractional flow reserve in a clinical population of intermediate coronary stenoses: results of the ADVISE registry, Euro Intervention, Volume 9 Number 1, May 21, 2013) and Oeltze et al (Blood Flow Clustering and Applications in Virtual Stenting of Intracranial Aneurysms, IEEE Transactions on Visualization and Computer Graphics ( Volume: 20, Issue: 5, May 2014)).
Regarding claims 22 and 32, Schmitt teaches a system and a method of planning deployment of one or more intravascular stents (“FIG. 15 is a screen shot of the graphic interface of the system shown in FIG. 14 with a section of lumen selected for stent placement” [0030] also see Figs. 15-17 and [0031]-[0033]; “pre-interventional stent planning” [0040]; “stent planning, evaluation and adjustment by automating the procedures” [0044]), the system comprising:
an electronic memory accessible by one or more processors (see computer in fig. 1 and the associated pars.), wherein the one or more processors are configured to:
access, from the electronic memory, blood vessel data collected with regard to a blood vessel (“the processing of the data collected using an OCT probe and the processor-based system is implemented as a set of computer program instructions that is converted into a computer executable form, stored as such in a computer readable medium, and executed by a microprocessor under the control of an operating system” [0095]);
identify a set of lumen cross-sectional values from the blood vessel data (“the image frame at which the lumen area is a minimum (the MLA cross section) serves as a marker for measurement of the percent area stenosis relative to the cross-sectional area measured at one or more reference frames. The reference diameters are intended to represent the diameters of the lumen in segments of the vessel that are acceptable points of contact between the vessel and the edges of the stent. The best points of contact are those regions of the artery where lumen area is a local maximum and where plaque is minimal” [0042]);
determine predicted blood-flow values in connection with simulation of virtual stent placements at a plurality of candidate stent landing zones (“the selection of the normal reference cross sections and the estimation of the tapered normal vessel profile for stent sizing” [0044]; “ determine if more than one stent is required; whether the stent will block too many branch vessels; and whether the position of the ends of the stent (“the landing zones”) will result in their being placed in an area of stenosis. The system also labels 297, 297′ the diameters of the vessel at each of the boundary indicators 282, 282′. The two numbers present are current vessel lumen diameter (smaller number) and target lumen diameter (greater number). The system also provides a label 300 for a given point in the lumen 302 that lists the current lumen diameter (smaller number) and target lumen diameter (greater number) (generally 303), the MLA 304 and the percent area stenosis” [0083]);
perform a cluster analysis of the predicted blood-flow values to determine one or more stenting regions in connection with the candidate stent landing zones (“The two contours thereby create a partition or segmentation of the frame into IM and OA region (FIG. 10 b). The ratio of the IM width to the OA width at each A-line provides an indication of normality (FIG. 10 a). Plaque regions have a high IM and a very low OA region, while normal regions have an almost equal IM and OA width. The mean ratio for all A-lines in the frame, ignoring the guide wire region, is an indication of normality as exemplified by the clustering in FIG. 10 a. Frames which have no plaque have low mean IM to OA ratio while those with plaque have large IM to OA ratio” [0072]);
identify one or more virtual stent configurations based on the cluster analysis performed for the candidate stent landing zones ([0030] FIG. 15 is a screen shot of the graphic interface of the system shown in FIG. 14 with a section of lumen selected for stent placement; [0031] FIG. 16 is a screen shot of the graphic interface of the system shown in FIG. 14 with another section of lumen selected for stent placement; [0032] FIG. 17 is a screen shot of the graphic interface of the system shown in FIG. 14 with yet another section of lumen selected for stent placement); and
select at least one of the virtual stent configurations for display in connection with a representation of the blood vessel and one or more predicted blood-flow values (“The user can continue to try various locations for the distal and proximal boundary indicators (FIG. 17), to make various measurements so as to be able to judge the best location to put the stent, what length the stent should be, and what diameter the stent should be. In this way, the OCT representation of the vessel and the lumen are configured as a deformable or modifiable representation that allows testing different stent placement scenarios” [0082]).
As seen above, Schmitt teaches all the broad claimed limitations including the cluster analysis. If in any interpretation, one argues that Schmitt does not teach the cluster analysis (which the office does not concede), Petraco reference is brought in to show the details of the cluster analysis in an effort to provide compact prosecution.
In the same field of endeavor, Petraco teaches evaluate the classification agreement between instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) in patients with angiographic intermediate coronary stenoses. identify the iFR optimal cut-point corresponding to FFR 0.8. The classification agreement of coronary stenoses as significant or non-significant was established between iFR and FFR and between repeated FFR measurements for each 0.05 quantile of FFR values, from 0.2 to 1. Close agreement was observed between iFR and FFR (area under ROC curve= 86%). The optimal iFR cut-off (for an FFR of 0.80) was 0.89. After adjustment for the intrinsic variability of FFR, the classification agreement (accuracy) between iFR and FFR was 94% (abst).
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Figure 1. Agreement between two measurements depends on the data distribution. The level of agreement between two measurements –when they are both “significant” or “non-significant” – will vary within each range of disease severity (from mild to severe), depending on how close the data points are to the established cut-off (clusters of red dots). The overall agreement between them (the overall diagnostic accuracy) will therefore be influenced by the data distribution of the sample and depend on the proportional number of data points away from/close to diagnostic cut-off (fig. 1). For further details see methods section of the reference.
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with cluster analysis as taught by Petraco because the classification agreement between iFR and FFR is excellent and similar to that of repeated FFR measurements in the same sample (abst of Petraco).
The above noted combination does not teach wherein the cluster analysis includes identifying one or more groupings of predicted blood-flow values that result from the candidate stent landing zones.
However, in the same field of endeavor, Oeltze teaches the blood flow pattern captured therein is assumed to be related to the success of stenting. It is often visualized by a dense and cluttered set of streamlines. We present a fully automatic approach for reducing visual clutter and exposing characteristic flow structures by clustering streamlines and computing cluster representatives. We show that clustering based on streamline geometry as well as on domain-specific streamline attributes contributes to comparing and evaluating different virtual stenting strategies (abst). We quantitatively evaluate three conceptually different techniques for the grouping: k-means clustering, agglomerative hierarchical clustering (AHC) in four variations (single link, complete link, average link, and Ward’s method), and spectral clustering (SC). Compare the quantitatively best performing clustering techniques and the corresponding representatives. Furthermore, we show that clustering streamlines also based on domain-specific attributes supports the evaluation of virtual stenting strategies (Intro).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with cluster analysis includes identifying one or more groupings of predicted blood-flow values that result from the candidate stent landing zones as taught by because understanding the hemodynamics of blood flow in vascular pathologies such as intracranial aneurysms is essential for both their diagnosis and treatment (abst of Oeltze).
Regarding claims 23 and 33, Schmitt teaches wherein selecting at least one of the virtual stent configurations for display further comprises displaying a longitudinal view of the blood vessel that contains indicia of stent landing zones for the at least one virtual stent configuration that has been selected (see figs. 13-19 and the associated pars).
Regarding claims 24 and 34, Schmitt teaches wherein the lumen cross-sectional values are at least one of a lumen area, a lumen radius, a lumen diameter, a lumen chord, and a distance that is measured from a point on a boundary of a lumen (“the lumen area is a minimum (the MLA cross section) serves as a marker for measurement of the percent area stenosis relative to the cross-sectional area measured at one or more reference frames. The reference diameters are intended to represent the diameters of the lumen in segments of the vessel that are acceptable points of contact between the vessel and the edges of the stent. The best points of contact are those regions of the artery where lumen area is a local maximum and where plaque is minimal (i.e., the intima is thin and uniform)” [0042]).
Regarding claims 25 and 35, Schmitt teaches wherein the lumen cross-sectional values comprises a set of lumen area values corresponding to cross-sections of the blood vessel (“the lumen area is a minimum (the MLA cross section) serves as a marker for measurement of the percent area stenosis relative to the cross-sectional area measured at one or more reference frames. The reference diameters are intended to represent the diameters of the lumen in segments of the vessel that are acceptable points of contact between the vessel and the edges of the stent. The best points of contact are those regions of the artery where lumen area is a local maximum and where plaque is minimal (i.e., the intima is thin and uniform)” [0042]).
Regarding claims 26 and 36, Schmitt teaches wherein the cluster analysis identifies an overlap of stenting configurations corresponding to the candidate stent landing zones (“The two contours thereby create a partition or segmentation of the frame into IM and OA region (FIG. 10 b). The ratio of the IM width to the OA width at each A-line provides an indication of normality (FIG. 10 a). Plaque regions have a high IM and a very low OA region, while normal regions have an almost equal IM and OA width. The mean ratio for all A-lines in the frame, ignoring the guide wire region, is an indication of normality as exemplified by the clustering in FIG. 10 a. Frames which have no plaque have low mean IM to OA ratio while those with plaque have large IM to OA ratio” [0072]).
Petraco teaches evaluate the classification agreement between instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) in patients with angiographic intermediate coronary stenoses. identify the iFR optimal cut-point corresponding to FFR 0.8. The classification agreement of coronary stenoses as significant or non-significant was established between iFR and FFR and between repeated FFR measurements for each 0.05 quantile of FFR values, from 0.2 to 1. Close agreement was observed between iFR and FFR (area under ROC curve= 86%). The optimal iFR cut-off (for an FFR of 0.80) was 0.89. After adjustment for the intrinsic variability of FFR, the classification agreement (accuracy) between iFR and FFR was 94% (abst).
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Figure 1. Agreement between two measurements depends on the data distribution. The level of agreement between two measurements –when they are both “significant” or “non-significant” – will vary within each range of disease severity (from mild to severe), depending on how close the data points are to the established cut-off (clusters of red dots). The overall agreement between them (the overall diagnostic accuracy) will therefore be influenced by the data distribution of the sample and depend on the proportional number of data points away from/close to diagnostic cut-off (fig. 1). For further details see methods section of the reference.
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with cluster analysis as taught by Petraco because the classification agreement between iFR and FFR is excellent and similar to that of repeated FFR measurements in the same sample (abst of Petraco).
Regarding claims 27 and 37, Schmitt teaches wherein selection of the at least one of the virtual stent configurations is based on the overlap that is identified within the cluster analysis (“The two contours thereby create a partition or segmentation of the frame into IM and OA region (FIG. 10 b). The ratio of the IM width to the OA width at each A-line provides an indication of normality (FIG. 10 a). Plaque regions have a high IM and a very low OA region, while normal regions have an almost equal IM and OA width. The mean ratio for all A-lines in the frame, ignoring the guide wire region, is an indication of normality as exemplified by the clustering in FIG. 10 a. Frames which have no plaque have low mean IM to OA ratio while those with plaque have large IM to OA ratio” [0072]).
Petraco teaches evaluate the classification agreement between instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) in patients with angiographic intermediate coronary stenoses. identify the iFR optimal cut-point corresponding to FFR 0.8. The classification agreement of coronary stenoses as significant or non-significant was established between iFR and FFR and between repeated FFR measurements for each 0.05 quantile of FFR values, from 0.2 to 1. Close agreement was observed between iFR and FFR (area under ROC curve= 86%). The optimal iFR cut-off (for an FFR of 0.80) was 0.89. After adjustment for the intrinsic variability of FFR, the classification agreement (accuracy) between iFR and FFR was 94% (abst).
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Figure 1. Agreement between two measurements depends on the data distribution. The level of agreement between two measurements –when they are both “significant” or “non-significant” – will vary within each range of disease severity (from mild to severe), depending on how close the data points are to the established cut-off (clusters of red dots). The overall agreement between them (the overall diagnostic accuracy) will therefore be influenced by the data distribution of the sample and depend on the proportional number of data points away from/close to diagnostic cut-off (fig. 1). For further details see methods section of the reference.
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with cluster analysis as taught by Petraco because the classification agreement between iFR and FFR is excellent and similar to that of repeated FFR measurements in the same sample (abst of Petraco).
Regarding claims 31 and 41, Schmitt teaches wherein the cluster analysis is based on a comparison of blood-flow values with respect to varying stent lengths (“The two contours thereby create a partition or segmentation of the frame into IM and OA region (FIG. 10 b). The ratio of the IM width to the OA width at each A-line provides an indication of normality (FIG. 10 a). Plaque regions have a high IM and a very low OA region, while normal regions have an almost equal IM and OA width. The mean ratio for all A-lines in the frame, ignoring the guide wire region, is an indication of normality as exemplified by the clustering in FIG. 10 a. Frames which have no plaque have low mean IM to OA ratio while those with plaque have large IM to OA ratio” [0072]).
Petraco teaches evaluate the classification agreement between instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) in patients with angiographic intermediate coronary stenoses. identify the iFR optimal cut-point corresponding to FFR 0.8. The classification agreement of coronary stenoses as significant or non-significant was established between iFR and FFR and between repeated FFR measurements for each 0.05 quantile of FFR values, from 0.2 to 1. Close agreement was observed between iFR and FFR (area under ROC curve= 86%). The optimal iFR cut-off (for an FFR of 0.80) was 0.89. After adjustment for the intrinsic variability of FFR, the classification agreement (accuracy) between iFR and FFR was 94% (abst).
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Figure 1. Agreement between two measurements depends on the data distribution. The level of agreement between two measurements –when they are both “significant” or “non-significant” – will vary within each range of disease severity (from mild to severe), depending on how close the data points are to the established cut-off (clusters of red dots). The overall agreement between them (the overall diagnostic accuracy) will therefore be influenced by the data distribution of the sample and depend on the proportional number of data points away from/close to diagnostic cut-off (fig. 1). For further details see methods section of the reference.
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with wherein the cluster analysis is based on a comparison of blood-flow values with respect to varying stent lengths as taught by Petraco because the classification agreement between iFR and FFR is excellent and similar to that of repeated FFR measurements in the same sample (abst of Petraco).
Claims 28, 30, 38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitt in view of Petraco and Oeltze as applied to above claims and further in view of Morris et al (“Virtual” (Computed) Fractional Flow Reserve, JACC: CARDIOVASCULAR INTERVENTIONS, VOL. 6, NO. 2, 2013), (hereinafter “Morris”).
Regarding the claims 28 and 38, above noted of reference(s) teach all the limitations of the claim except for generate a stent effectiveness score (SES) for the one or more virtual stent configurations.
However, in the same field of endeavor, Morris teaches Virtual fractional flow reserve (vFFR) is computed using coronary imaging and computational fluid dynamics modeling. The method used to generate 3-dimensional geometric arterial models (segmentation) and selection of appropriate, patient-specific boundary conditions represent the primary scientific limitations. Many conflicting priorities and design features must be carefully considered for vFFR models to be sufficiently accurate, fast, and intuitive for physicians to use (abst). A “virtual stenting” facility, whereby the physiological effect of alternative interventional strategies can be trialed in silico (by computer simulation) before treatment is delivered in vivo. vFFR can also assess any segment of the coronary tree, including those to which it might be challenging to pass a pressure wire (intro). In the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study, constructed a computational work flow that computed vFFR from CAG images. In 35 diseased vessels, the VIRTUHEART model physiological lesion significance with 97% accuracy, albeit with a paucity of FFR cases within the critical 0.75 to 0.85 range. Average error between vFFR and measured FFR was 0.06.
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and device of the combination of references with generate a stent effectiveness score (SES) as taught by Morris because FFR-guided percutaneous coronary intervention improves patient outcomes and reduces stent insertion and cost (abst of Morris).
Regarding the claims 30 and 40, above noted of reference(s) teach all the limitations of the claim except for adjust the SES with one or more weighting factors.
However, in the same field of endeavor, Morris teaches Virtual fractional flow reserve (vFFR) is computed using coronary imaging and computational fluid dynamics modeling. The method used to generate 3-dimensional geometric arterial models (segmentation) and selection of appropriate, patient-specific boundary conditions represent the primary scientific limitations. Many conflicting priorities and design features must be carefully considered for vFFR models to be sufficiently accurate, fast, and intuitive for physicians to use (abst). A “virtual stenting” facility, whereby the physiological effect of alternative interventional strategies can be trialed in silico (by computer simulation) before treatment is delivered in vivo. vFFR can also assess any segment of the coronary tree, including those to which it might be challenging to pass a pressure wire (intro). In the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study, Morriset al. (13) constructed a computational work flow that computed vFFR from CAG images. In 35 diseased vessels, the VIRTUHEART model (University of Sheffield, Sheffield, United Kingdom) predicted (dichotomized) physiological lesionsignificancewith97%accuracy, albeit with a paucity of FFR cases within the critical 0.75 to 0.85 range. Average error between vFFR and measured FFR was 0.06. These data are used to generate predictions regarding pressure and flow changes across coronary stenoses, from which vFFR can be calculated at any point along the vessel (COMPUTATIONAL FLUID DYNAMICS section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and device of the combination of references with adjust the SES with one or more weighting factors as taught by Morris because FFR-guided percutaneous coronary intervention improves patient outcomes and reduces stent insertion and cost (abst of Morris).
Response to Arguments
Regarding the rejection of claims under 35 USC 101, the applicant argues the following;
Applicant submits that the claims are not directed to an abstract idea that merely recites limitations to be performed in the mind. First, in order for the claims to be considered directed to the abstract idea of a "mental process", the limitations must be able to "practically be performed in the human mind." (MPEP 2106.04(a)(2).) Applicant submits that the pending claims are directed to a technological improvement that could not practically be performed in the human mind.
The recited claims 22 and 32 recite using blood vessel data so as to determine "predicted blood-flow values in connection with simulation of virtual stent placements at a plurality of candidate stent landing zones." These candidate stent landing zones are then further analyzed based on a cluster analysis of the predicted blood-flow values. A virtual stent configuration is then selected for display based on the analysis of the candidate stent landing zones. Applicant submits that the recited features of claims 22 and 32 could not be "practically performed in the human mind," in that no physician would be able to perform a mental process that would in any practical sense, allow a physician to simulate virtual stent placement so as to identify predicted blood-flow values in connection with a plurality of candidate stent landing zones in the manner recited.
Contrary to the applicant’s assertion, claims still recite abstract idea as "identifying" and "determining" and "performing" which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Other than the recitation of generic computer components (“processer”) nothing in the claim element precludes the step from practically being performed in the mind.
Further, the applicant also argues that features of claims 22 and 32 could not be "practically performed in the human mind," in that no physician would be able to perform a mental process that would in any practical sense. However, the claims are not specific to any data or data size rather merely reciting a broad blood vessel data, simulation of virtual stent placements etc. Therefore, since there is specific data size is claimed, under BRI, the claims may merely be interpreted a minimum data size (e.g., 2x2 data for argument sake) which could be very easy to perform in mind as a mental process.
Also claims merely recite and broadly recite all generic component that is used for mere data gathering which are examples of activities that courts have found to be insignificant extra-solution activity. These components are widely practiced and commonly known with no specificity which courts have found to be insignificant extra-solution activity.
Therefore, under its broadest reasonable interpretation, claims cover performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Judicial exception is not integrated into a practical application since the claim only recites additional element generic computer components (“processer”).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are not patent eligible.
Regarding the rejection of claims under 35 USC 103, the applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Claims 29 and 39 are free from prior art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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