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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-22 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over Claims 1-20 of Patent Application No. 12,165,036 and Claims 1-16 of Patent Application No. 11,886,975.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the scope of the claims is substantially similar and recites similar limitations. It is because the claims in the continuation application are broader than the ones in the patent application, In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982).
For example, claim 1 of the present application recites “inputting”, “extracting”, “training” and “outputting” steps which are similar to the patented claim 1. Furthermore, the cited patents has more limitations, thereby encompassing the present application's limitations.
Therefore, claim 1 of the present invention is broader than claim 1 of the patented applications.
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.
Claim(s) 1-4, 6-8, 11-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahmim et al., “Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images” in view of Mostafa et al., “Parkinson’s Disease Detection Using Ensemble Architecture from MR Images”.
Regarding claim 1, Rahmim discloses a method of predicting a prospective pathology score of a test subject having a pathology (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 2.3, Data analysis, Third paragraph; predict year 4 movement disorder society– unified Parkinson's disease rating scale (MDS-UPDRS)– part III (MDS-UPDRS III) motor score), detecting one or more pathologies having rapid rates of progression in the test subject, and/or staging one or more pathologies in the test subject (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 4 Discussion, First and Second paragraphs; predict disease outcome/progression using longitudinal biomarkers), the method comprising:
inputting one or more image feature vectors derived from the test subject into a model (Abstract; Section 2.3 Data analysis; The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77])), wherein the model was generated by:
extracting a plurality of image features from sets of longitudinal single photon emission computed tomography (SPECT) and/or positron emission tomography (PET) images obtained from a plurality of reference subjects having the pathology to produce at least one image feature vector (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 2.2. Image processing and quantification, 3) Feature extraction; extracts 92 radiomic features from DAT SPECT images); and
outputting a predicted prospective pathology score of the test subject having the pathology (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 2.3, Data analysis, Third paragraph; predict year 4 movement disorder society– unified Parkinson's disease rating scale (MDS-UPDRS)– part III (MDS-UPDRS III) motor score), detected pathologies having rapid rates of progression in the test subject, and/or staged pathologies in the test subject (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 4 Discussion, First and Second paragraphs; predict disease outcome/progression using longitudinal biomarkers).
Rahmim further discloses random forest analysis is an ensemble non-parametric machine learning method. An observation is that the prediction model cannot be visualized using a single straightforward formula because the prediction is done through a collection of trees, and each tree has its own formula, which is also the case with artificial neural networks (ANNs) (Section 2.3 Data analysis, First paragraph).
Rahmim discloses claim 1 as enumerated above, but Rahmim does not explicitly disclose training multiple artificial neural networks (ANNs) using the image feature vector to produce an ensemble of ANNs as claimed.
However, Mostafa discloses ensemble architectures combining some winning Convolutional Neural Network models for Parkinson’s disease detection. We used both untrained and pre-trained models to construct our ensemble networks and compared the performances of the resultant networks (Abstract; Section III. Proposed Method, C. Model).
Therefore, taking the combined disclosures of Rahmim and Mostafa as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate ensemble architectures combining some winning Convolutional Neural Network models for Parkinson’s disease detection. We used both untrained and pre-trained models to construct our ensemble networks and compared the performances of the resultant networks as taught by Mostafa into the invention of Rahmim for the benefit of achieving a higher accuracy for Parkinson’s disease detection (Mostafa: Section V. Conclusion and Future Works).
Regarding claim 2, the method of claim 1, Rahmim and Mostafa in the combination disclose further comprising inputting one or more non-imaging feature vectors derived from the test subject, and wherein the model was further generated by:
extracting a plurality of non-imaging features from non-imaging data obtained from the plurality of reference subjects having the pathology to produce at least one non-imaging feature vector (Rahmim: Abstract; Section 2.3 Data analysis); and
training the multiple artificial neural networks (ANNs) using the non-imaging vector to produce the ensemble of ANNs (Mostafa: Abstract; Section III. Proposed Method, C. Model).
Regarding claim 3, the method of claim 1, Mostafa in the combination disclose further comprising training at least one of the layers of the ANN using one or more imaging features obtained from the longitudinal SPECT and/or PET images (Abstract; Section III. Proposed Method, C. Model).
Regarding claim 4, the method of claim 1, Rahmim in the combination disclose wherein the SPECT and/or PET images comprise raw SPECT and/or raw PET images (Section 2.1 Longitudinal patient data, Second paragraph).
Regarding claim 6, the method of claim 1, Rahmim in the combination disclose wherein the SPECT and/or PET images comprise dopamine transporter SPECT (DatSPECT) and/or PET images (Abstract; Section 2.1 Longitudinal patient data).
Regarding claim 7, the method of claim 1, Mostafa in the combination disclose further comprising extracting one or more of the plurality of images features using at least one artificial neural network (ANN) (Abstract; Section III. Proposed Method, C. Model).
Regarding claim 8, the method of claim 1, Rahmim in the combination disclose wherein the ANN is not further trained on a classification task (Section 2.3 Data analysis).
Regarding claim 11, the method of claim 1, Mostafa in the combination disclose further comprising extracting the plurality of image features using one or more pre-trained convolutional neural networks (CNNs) (Abstract; Section II. Related Works).
Regarding claim 12, the method of claim 1, Rahmim in the combination disclose further comprising extracting a plurality of image features from semi-quantitative imaging measures of the sets of the longitudinal SPECT and/or PET images, wherein the semi-quantitative imaging measures are of striatal binding ratios and/or other radiomic features of the sets of the longitudinal SPECT and/or PET images (Abstract; Section 2.1 Longitudinal patient data, First paragraph).
Regarding claim 13, the method of claim 2, Rahmim in the combination disclose wherein the non-imaging features comprise pathology sub-scores, patient histories, medical records, patient demographic information, genomic data, and/or proteomic data (Abstract; Section 2.3 Data analysis).
Regarding claim 14, the method of claim 13, Rahmim in the combination disclose wherein the pathology sub-scores comprise unified Parkinson's disease rating scale (UPDRS) sub-scores (Abstract; Section 2.1 Longitudinal patient data, First paragraph and Section 2.3, Data analysis, Third paragraph).
Regarding claim 15, the method of claim 1, Rahmim in the combination disclose wherein the pathology comprises a type of dementia or brain disorder (Abstract; Section 1. Introduction).
Regarding claim 16, the method of claim 15, Rahmim in the combination disclose wherein the type of dementia is selected from the group consisting of: Parkinson's disease (PD), Alzheimer's disease (AD), Lewy Body Dementia (LBD), Creutzfeldt-Jakob disease (CJD), frontotemporal dementia (FTD), Huntington's disease (HD), normal pressure hydrocephalus (NPH), posterior cortical atrophy (PCA), vascular dementia, and Korsakoff syndrome (Abstract; Section 1. Introduction).
Regarding claim 18, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 19, this claim recites substantially the same limitations that are performed by claim 2 above, and it is rejected for the same reasons.
Regarding claim 20, this claim recites substantially the same limitations that are performed by claim 15 above, and it is rejected for the same reasons.
Claim(s) 5, 9-10, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahmim et al., “Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images” in view of Mostafa et al., “Parkinson’s Disease Detection Using Ensemble Architecture from MR Images” and further in view of Tang et al., US 2020/0311527.
Regarding claim 5, the method of claim 1, Rahmim and Mostafa in the combination disclose wherein the ensemble of ANNs (Rahmim: Section 2.3 Data analysis, First paragraph) comprises at least one convolutional neural network (CNN) (Mostafa: Abstract).
Rahmim and Mostafa in the combination disclose claims 5 as enumerated above, but they do not explicitly disclose at least one recurrent neural network (RNN) as claimed.
However, Tang discloses the neural network includes a recurrent neural network (RNN) unit (para 0005 and 0023).
Therefore, taking the combined disclosures of Rahmim, Mostafa, and Tang as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a recurrent neural network (RNN) unit as taught by Tang into the inventions of Rahmim and Mostafa for the benefit of better handle time variant data because these neural networks include nodes having connections that form a directed graph along a temporal sequence, allowing RNNs to process sequences of inputs using internal memory (Tang: para 0023).
Regarding claim 9, the method of claim 1, Rahmim and Mostafa in the combination disclose wherein the ANN (Rahmim: Section 2.3 Data analysis, First paragraph).
Rahmim and Mostafa in the combination disclose claims 9 as enumerated above, but they do not explicitly disclose one or more recurrent neural networks (RNNs) as claimed.
However, Tang discloses recurrent neural networks (RNNs) (para 0023).
Therefore, taking the combined disclosures of Rahmim, Mostafa, and Tang as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate recurrent neural networks (RNNs) as taught by Tang into the inventions of Rahmim and Mostafa for the benefit of better handle time variant data because these neural networks include nodes having connections that form a directed graph along a temporal sequence, allowing RNNs to process sequences of inputs using internal memory (Tang: para 0023).
Regarding claim 10, the method of claim 9, Tang in the combination disclose wherein the RNNs comprise one or more long short-term memory (LSTM) networks and/or one or more gated recurrent units (GRUs) (para 0042).
Regarding claim 21, this claim recites substantially the same limitations that are performed by claim 5 above, and it is rejected for the same reasons.
Regarding claim 22, this claim recites substantially the same limitations that are performed by claim 10 above, and it is rejected for the same reasons.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahmim et al., “Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images” in view of Mostafa et al., “Parkinson’s Disease Detection Using Ensemble Architecture from MR Images” and further in view of Vale et al., US 10,588,561.
Regarding claim 17, the method of claim 15, Rahmim and Mostafa in the combination disclose the type of brain disorder (Abstract; Section 1. Introduction).
Rahmim and Mostafa in the combination disclose claim 15 as enumerated above, but they do not explicitly disclose selected from the group consisting of: schizophrenia and epilepsy as claimed.
However, Vale discloses diagnosis of epilepsy (col. 6, lines 5-21).
Therefore, taking the combined disclosures of Rahmim, Mostafa, and Vale as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate diagnosis of epilepsy as taught by Vale into the inventions of Rahmim and Mostafa for the benefit of mapping individualized epilepsy networks of patients and simulating planned resective surgeries (Vale: col. 2, lines 42-47).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bengtsson et al., US 2025/0000473 discloses segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis.
Reith et al., US 2022/0223231 discloses methods and systems for predicting biomarker progression in medical imaging is provided.
Collins et al., US 2008/0101665 discloses a method for predicting a clinical state of a subject based on image data obtained from a Volume Of Interest in the subject.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665
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