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 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.
Claim(s) 1-11 and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans to Zhang et al., hereinafter, “Zhang”.
Claim 1. Zhang teaches A system, comprising: a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters
memory storing input image data Fig. 1 I. Medical Images characterizing a current spatial distribution of glioblastoma multiforme (GBM), a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters of a reaction diffusion partial differential equation (PDE) model of GBM growth from a single 3D structural MRI snapshot.
wherein the current spatial distribution of the GBM is detected at a precursor examination of a patient receiving tumor treating fields (TTFields) therapy; Fig. 1 and Fig. 6 The data is provided at ̄𝑡 = 1.
[3.2. Patient Tumors] All patients received standard treatment, which consists of immediate tumor resection, followed by combined radiotherapy and chemotherapy (understood to be a form of treatment/therapy), and are monitored for recurrence regularly.
and a neural network processor Fig. 1. III. Physics Informed Neural Network (PINN), in communication with the memory,
and configured to cause a neural network Fig. 1. III. Physics Informed Neural Network (PINN) to process the input image data and, Fig. 1 I. Medical Images
in response, generate output probability data characterizing a future spatial distribution of the GBM at a follow-up examination of the patient receiving the TTFields therapy, and IV. Prediction and Validation, Figure 6 The data is provided at ̄𝑡 = 1.
wherein the neural network determines the future spatial distribution based in part on a time interval between the precursor examination and the follow-up examination. Fig. 6 the FDM at normalized times ̄ 𝑡 = 0.6, 0.8, 1.0, 1.2.
Claim 2. Zhang teaches The system of claim 1, further configured to use the future spatial distribution to predict a future tumor growth of the GBM at the follow-up examination. Fig. 6 Predicted dynamics of tumor from Patient 5
Claim 3. Zhang teaches The system of claim 1, wherein the neural network is a convolutional neural network. Fig. 1. III. Physics Informed Neural Network (PINN)
Claim 4. Zhang teaches The system of claim 3, wherein the convolutional neural network has an encoder-decoder architecture. Fig. 1. III. Physics Informed Neural Network (PINN) (inherent)
Claim 5. Zhang teaches The system of claim 4, wherein the encoder-decoder architecture is a three-dimensional (3D) encoder-decoder architecture. Fig. 1. III. Physics Informed Neural Network (PINN)
Claim 6. Zhang teaches The system of claim 1, wherein the input image data Fig. 1 I. Medical Images and the output probability data are two-dimensional (2D) image data. IV. Prediction and Validation
Claim 7. Zhang teaches The system of claim 1, wherein the input image data Fig. 1 I. Medical Images and the output probability data are 3D image data. IV. Prediction and Validation – 3D prediction (right )
Claim 8. Zhang teaches The system of claim 7, wherein the input image data Fig. 1 I. Medical Images and the output probability data are 3D magnetic resonance imaging (MRI) data. [2.1.2. Diffuse-domain method] We use the diffuse-domain method as it seamless integrate with the volumetric MRI data.
Claim 9. Zhang teaches The system of claim 8, wherein the input image data is a voxel grid, and the future spatial distribution is represented by a dense per-voxel prediction of a future tumor growth probability for each voxel in the voxel grid. [Appendix F. Distribution of collocation points] More specifically, 𝑡𝑖 is sampled from a truncated exponential probability density function 𝜆𝑒−𝜆𝑡, with 𝜆 = 0.5 and 𝑡 ∈ [0,1]., Fig. F.13.
Claim 10. Zhang teaches The system of claim 8, wherein the future spatial distribution is represented by a heat map of probability scores. Fig. 5.
Claim 11. Zhang teaches The system of claim 1, further configured to use a supplemental time feature channel to supply the neural network with temporal information characterizing the time interval. Fig. 6 the FDM at normalized times ̄ 𝑡 = 0.6, 0.8, 1.0, 1.2.
Claim 16. Zhang teaches The system of claim 1, wherein the neural network is trained using a binary cross-entropy loss function. 2.3. Loss functions
Claim 17. Zhang teaches The system of claim 16, wherein the neural network is trained on training image data in which certain regions of enhancing tumor core are delineated and aligned across time points using nonlinear deformable registration. 3.2. Patient tumors, Fig. 7 (E) segmentations
Claim 18. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
Claim 19. Reviewed and analyzed in the same way as claim 2. See the above analysis and rationale.
Claim 20. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans to Zhang et al., hereinafter, “Zhang” in view of CN 116705296 B to Zhang et al., hereinafter, “Zhang2”.
Claim 12. Zhang fails to explicitly teach concatenate the supplemental time feature channel with a penultimate feature map generated by the neural network. Zhang2, in the filed of analyzing GBM patient data using an neural network teaches The system of claim 11, further configured to concatenate the supplemental time feature channel with a penultimate feature map generated by the neural network. [page 5] The algorithm uses 3DCNN + LSTM to extract and fuse features from different granularities such as time and space, uses EM algorithm to extract the most critical sample segment, and
Fig. 2 shows a three-dimensional CNN architecture.,
[pages 9-10] describes feature maps, time and spatial features
[page 11] The method is based on a particle calculation theory, the features are extracted and fused from different particle sizes such as time and space, the most representative sample fragment is selected through a maximum expectation EM algorithm, and then the fragment is input into a built 4D-CNN network to finally obtain a prediction result.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhang with the teachings of Zhang2 [page 1] so an analysis tool can simplify the process of manually processing and analyzing data to reduce the burden on doctors and researchers.
Claim 13. Zhang2 further teaches The system of claim 12, further configured to cause the neural network to use the concatenation of the supplemental time feature channel and the penultimate feature map to generate the future spatial distribution. Fig. 2 shows a three-dimensional CNN architecture.,
[pages 9-10] describes feature maps, time and spatial features
[page 11] The method is based on a particle calculation theory, the features are extracted and fused from different particle sizes such as time and space, the most representative sample fragment is selected through a maximum expectation EM algorithm, and then the fragment is input into a built 4D-CNN network to finally obtain a prediction result.
[page 14]the output of the CNN is connected with a plurality of frames in series according to the time sequence
Claim 14. Zhang further teaches The system of claim 13, wherein the concatenation allows the neural network to calibrate the future spatial distribution based on elapsed time between the precursor examination and the follow-up examination. Zhang [Introduction] PINNs have also been used to calibrate the parameters, Fig. 1, IV. Prediction and Validation
Claim 15. Zhang2 further teaches The system of claim 13, wherein the concatenation is a four-dimensional (4D) representation. [Page 5] then inputs the most critical sample segment into a 4DCNN model for simultaneously learning space and time based on a particle computing theory to obtain the classification prediction of the sample.
[page 11] Thus, the obtained most representative sample fragment is input into the 4D-CNN model, the 4D-CNN is trained into a classifier with optimal effect through iteration, and a predicted result is finally obtained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20205/0336063 A1 to Kundu et al. and US 10614567 B2 to Tiwari et al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DELOMIA L GILLIARD whose telephone number is (571)272-1681. The examiner can normally be reached 8am-5pm.
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661
