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 .
Status of Claims
This action is in reply to the application filed on 28 March 2025.
Claims 1-15 are currently pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 03/28/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 460, 480, 580, 610 (A-E), 611 (A-E), 612 (A-E), 613 (A-E), 614 (A-E), and 1050. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1-15).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
using a part-aware generative model to learn a latent representation of each part of a multi-part organ shape for inclusion in a virtual population and output synthesised parts of the multi-part organ shape;
using a spatial composition model to align the outputted synthesised parts of the multi-part organ shape and output an anatomically meaningful example of an overall multi-part organ shape as a virtual chimera; and
storing the virtual chimera in the virtual population, for use in the in-silico trial.
These steps for generating virtual chimera populations of multi-part organ shapes, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through the evaluation and determination of virtual chimera populations of multi-part organ shapes. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. Additionally, the claimed steps map out a methodology that mirrors cognitive steps an analyst or doctor could perform by conceptualizing organ parts, aligning them in their head, and storing the conceptual image. In addition, the italicized portions containing the recitation of learning a latent representation at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations, which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind and mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements, non-italicized portions identified above for claim 1, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea (such as recitation of output synthesised parts of the multi-part organ shape; output an anatomically meaningful example of an overall multi-part organ shape as a virtual chimera; and, storing the virtual chimera in the virtual population amounts to mere data output an storage since it does not add meaningful limitations to the outputting and storing actions performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of output synthesised parts of the multi-part organ shape; and, output an anatomically meaningful example of an overall multi-part organ shape as a virtual chimera, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); such as recitation of storing the virtual chimera in the virtual population, e.g., storing and retrieving information in memory, Versata Dev. Group, Inc., MPEP 2106.05(d)(II)(iv).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-15 reciting particular aspects for generating virtual chimera populations of multi-part organ shapes such as
[Claim 2] wherein the part-aware generative model comprises an independent generator to learn a latent representation of each part of the multi-part organ shape;
[Claim 3] wherein the independent generator comprises a graph-convolutional variational autoencoder (gcVAE) network architecture operable to learn variability in each part of the multi-part organ shape observable across a training population;
[Claim 4] wherein the part-aware generative model further comprises a dependent generator to learn a shared latent representation of all of the parts of the multi-part organ shape;
[Claim 5] wherein the dependent generator comprises a graph-convolutional multi-channel variational autoencoder (gcmcVAE) network architecture operable to learn joint variability in shapes of each part of the multi-part organ shape observable across a training population;
[Claim 6] wherein the spatial composition model further comprises at least one of an affine composition network and a non-rigid composition network;
[Claim 7] wherein the gcVAE network, the gcmcVAE network, the affine composition network, the non-rigid composition network each further comprises at least one residual graph convolutional down-sampling function block and at least one residual graph convolutional up-sampling function block;
[Claim 8] wherein the at least one residual graph convolutional down-sampling function block or the at least one residual graph convolutional up-sampling function block comprises at least one Chebyshev graph convolution function, Exponential linear Unit function or instance normalisation function;
[Claim 9] wherein the at least one residual graph convolutional down-sampling function block comprises a mesh pooling layer function, and/or the at least one residual graph convolutional up-sampling function block comprises a mesh up-sampling layer function;
[Claim 10] wherein the affine composition network comprises at least one of: a scaling branch, a rotation branch and a translation branch;
[Claim 11] wherein the non-rigid composition network further comprises a feature embedding array or a Chebyshev convolution layer;
[Claim 12] wherein the method comprises providing non-overlapping patient data or partially-overlapping patient data during training;
[Claim 13] wherein the non-overlapping patient data or partially-overlapping patient data comprises shape data extracted from a same or different patients' imaging data, wherein the imaging data is acquired using a same or different imaging modality;
[Claim 14] wherein the different imaging modalities comprises magnetic resonance imaging, computed tomography, computed tomography angiography, ultrasound, positron emission tomography, single-photon emission tomography;
[Claim 15] carrying out in-silico design, testing or regulatory approval of a medical device (including, but not limited to, implants, digital health or artificial intelligence devices), or drug using the generated virtual population;
these italicized portions covers performance of the limitations in the mind. These identified limitations merely describe types of data and determinations that can be performed by humans. In addition, the italicized portions containing the recitation of learning (joint) variability at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations, which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. Furthermore, the italicized portions of claims 7 to 11 have been treated as part of the abstract, specifically as mathematical relationships (i.e., applied mathematics, geometry, and linear algebra) which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance).
Step 2A Prong 2
Dependent claims 3, 5, 7, and 13-14 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 3 (a graph-convolutional variational autoencoder (gcVAE) network), claim 5 (a graph-convolutional multi-channel variational autoencoder (gcmcVAE) network), claim 7 (gcVAE network; and, gcmcVAE network), claim 13 (using a same or different imaging modality), and claim 14 (wherein the different imaging modalities comprises magnetic resonance imaging, computed tomography, computed tomography angiography, ultrasound, positron emission tomography, single-photon emission tomography) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); add insignificant extra-solution activity to the abstract idea (such as recitation of claim 13 (wherein the imaging data is acquired) amounts to mere data gathering since it does not add meaningful limitations to the acquiring action performed, see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 3, 5, 7, and 13-14 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), MPEP 2106.05(f). Dependent claim 13 recites additional subject matter which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving the reference image set amounts; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
. Also, see [00134] which provides examples of generic computing devices, [00130] which provides examples of generic processors, and [00130] and [00138] which provides examples of generic memory types. There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolize every possible application of generative models to simulate organs for clinical testing, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates. Using AI tools is generally not enough to transform an abstract idea into patent-eligible subject matter if the core of the invention is still abstract; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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 nonobviousness.
Claims 1-2, 4, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Beetz et al. (Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders) in view of Fuertinger et al. (US20220270719A1).
Regarding claim 1, Beetz discloses using a part-aware generative model to learn a latent representation of each part of a multi-part organ shape for inclusion in a virtual population and output synthesised parts of the multi-part organ shape ([pg. 7] “The quality of the latent space distribution plays an important role in the VAE’s ability to synthesize artificial populations of realistic hearts that are also sufficiently diverse. We analyze the contributions of each part of the latent space to the generated point clouds by varying individual latent space components, while keeping the remaining latent space constant, and passing the resulting vectors through the decoder to obtain the respective outputs.”)
using a spatial composition model to align the outputted synthesised parts of the multi-part organ shape and output an anatomically meaningful example of an overall multi-part organ shape as a virtual chimera; […] the virtual chimera[…] ([pg. 7] “synthesize artificial populations of realistic hearts” [pg. 8 to pg. 9] “In this work, we have developed an efficient and easy-to-use method for synthesizing 3D biventricular anatomies conditioned on subject metadata. […] We achieve mean Chamfer distances considerably below the pixel resolution of the underlying images, demonstrating good reconstruction quality, while the small standard deviation values indicate that our method is highly robust and can successfully cope with a variety of different morphologies, both within and between subpopulations”)
Beetz does not explicitly disclose however Fuertinger teaches and storing […] in the virtual population, for use in the in-silico trial ([0043] “Memory unit 130 may store a virtual clinical trial application 132 that may operate, alone or in combination with virtual clinical trial logic 122, to perform various functions for generating, performing, and/or evaluating VCTs according to some embodiments.” [0044] “In some embodiments, memory unit 130 may store various information associated with generating, performing, and/or evaluating VCTs, including, without limitation, reference population information 134, model information 136, avatars 138, clinical data 140, simulation results 142, and/or the like.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Beetz storing […] in the virtual population, for use in the in-silico trial as taught by Fuertinger since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Beetz discloses wherein the part-aware generative model comprises an independent generator to learn a latent representation of each part of the multi-part organ shape ([pg. 2] “We choose the PointNet++ [12] and the Point Completion Network [15] as the baseline architectures of our encoder […] We adapt them to our multi-class setting by adding class information about the cardiac substructures (left ventricular (LV) endocardium, LV epicardium, right ventricular (RV) endocardium) to the encoder input. […] In order to effectively process high-density surface data and cope with the difficulty of latent space sampling, we also insert multiple fully connected layers to facilitate the exchange of spatial, class, and condition information. The standard reparameterization approach [8] is applied in the net work’s latent space.”)
Regarding claim 4, Beetz discloses wherein the part-aware generative model further comprises a dependent generator to learn a shared latent representation of all of the parts of the multi-part organ shape ([pg. 5] “We then pass the samples through the trained decoder part of our network. […] Comparing the point clouds in Fig. 3, we observe noticeable differences in sizes and shapes, indicating the decoder’s ability to generate diverse point clouds.”)
Regarding claim 12, Beetz discloses wherein the method comprises providing non-overlapping patient data or partially-overlapping patient data during training ([pg. 8] “we do not observe any sizeable disconnected or overlapping components between them.”)
Regarding claim 13, Beetz discloses wherein the non-overlapping patient data or partially-overlapping patient data comprises shape data extracted from a same or different patients' imaging data ([pg. 8] “It is also capable of efficiently working with high-dimensional 3D MRI-based surface data due its usage of pointclouds instead of highly-sparse and memory-intensive voxelgrids. We achieve mean Chamfer distances considerably below the pixel resolution of the underlying images, demonstrating good reconstruction quality, while the small standard deviation values indicate that our method is highly robust and can successfully cope with a variety of different morphologies, both within and between subpopulations.”
wherein the imaging data is acquired using a same or different imaging modality ([pg. 2] “Our point cloud dataset is based on 3D reconstructions of cine MRI acquisitions obtained from volunteers of the UK Biobank study [10].”)
Regarding claim 14, Beetz discloses wherein the different imaging modalities comprises magnetic resonance imaging, computed tomography, computed tomography angiography, ultrasound, positron emission tomography, single-photon emission tomography ([pg. 2] “MRI acquisitions.”)
Regarding claim 15, Beetz does not explicitly disclose however Fuertinger teaches carrying out in-silico design, testing or regulatory approval of a medical device (including, but not limited to, implants, digital health or artificial intelligence devices), or drug using the generated virtual population ([0065] “In some embodiments, the trial population may include patients typically excluded from conventional clinical trials, such as elderly, frail, and multimorbid patients.” [0066] “VIAT process 500 may generate avatars at step 525. In general, an avatar (or virtual patient) may include a mathematical model configured to represent a patient and/or physiological systems or subsystems thereof. […] For example, an avatar may have an input for a prescription for drug A and a corresponding model for a patient taking drug A according to the prescription (for instance, modeling the effect on Hgb levels for drug A).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Beetz storing […] in the virtual population, for use in the in-silico trial as taught by Fuertinger since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art
No prior Art Rejection
Regarding claims 3 and 5-11, no prior art rejection is being presented at this time.
Prior Art Cited but Not Relied Upon
Dou, H., Virtanen, S., Ravikumar, N., & Frangi, A. F. (2022). A generative shape compositional framework: towards representative populations of virtual heart chimaeras.
This reference is relevant because is discloses the applicant’s claimed invention.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687
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