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 § 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 27-31 are not directed to signals per se because Spec. 40 specifically excludes signals per se, “computer-readable storage media excludes media consisting solely of a modulated data signal, a carrier wave, or a propagated signal, per se.”
Claims 1-9 and 21-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept and a mathematical relationship without significantly more. The claims recite the abstract idea of generating a first graph, a second graph, training a first model and an ensemble model using a loss function, and generating a consensus graph. This judicial exception is not integrated into a practical application because receiving expression data is insignificant extra-solution activity; and the RNA sequencing elements (e.g. claims 2 and 9) merely link the abstract idea to the field of genes and sequencing. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the elements directed to a processor, memory, or storage media are directed to generic computer parts.
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.
Claims 1-9 and 21-31 are rejected under 35 U.S.C. 103 as being unpatentable over EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks by Maneesha et al (from IDS filed 10/13/2023) and uGLAD: Sparse graph recovery by optimizing deep unrolled networks by Shrivastava et al (the attached 5/23/2022 version, not the version filed with the IDS filed 10/13/2023).
EnGRaiN teaches claims 1, 21 and 27. A method for visualizing complex data relationships comprising:
receiving expression data; (EnGRaiN sec. 1 p. 1313 “Using EnGRaiN, we report the construction and analysis of a whole-genome ensemble network of the plant Arabidopsis thali ana, created from painstaking curation of heterogeneous microarray datasets from multiple public repositories.” The microarrays are the claimed expression data.)
providing the expression data to a first generator model that generates a first graph representing relationships in the expression data, wherein the first generator model is a trainable generator model that is (EnGRaiN sec. 2.1.1 “Consider ‘M’ GRN predictions generated by as many distinct GRN recovery methods, each run independently of the others. GRNs may have edge weights, denoting the confidence level in each predicted edge.” The Gene Regulatory Network (GRN) recovery methods are each a generator model that generates a graph GRN.)
providing the expression data to a second generator model that, in parallel with the first generator model, generates a second graph representing relationships in the expression data; (EnGRaiN sec. 2.1.1 “Consider ‘M’ GRN predictions generated by as many distinct GRN recovery methods, each run independently of the others. GRNs may have edge weights, denoting the confidence level in each predicted edge.” The second model in the set of M models generates a second graph. See also EnGRaiN fig. 1 where there are several GRNs, below.)
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providing the first graph and the second graph to an ensemble model that is a machine learning model and creates consensus relationship data from the first graph and the second graph, wherein the ensemble model and the first generator model (EnGRaiN fig. 1 “Gene networks for each tissue and condition using 10 different network inference methods. These were then used as input to generate genome-scale ensemble networks using both unsupervised and supervised ensemble learning methods.” The ensemble methods is the ensemble model and the ensemble network is the consensus graph, see below.)
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generating a consensus graph having nodes and edges from the consensus relationship data. (EnGRaiN fig. 1 “Gene networks for each tissue and condition using 10 different network inference methods. These were then used as input to generate genome-scale ensemble networks using both unsupervised and supervised ensemble learning methods.” The ensemble methods is the ensemble model and the ensemble network is the consensus graph, see below.)
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EnGRaiN doesn’t teach end-to-end differentiable models nor joint training.
However, uGLAD teaches a trainable generator model that is end-to-end differentiable… (uGLAD sec. 3.2 “They leveraged the interpretable nature of the GLAD’s deep architecture to define the loss for training.” The uGLAD model is end-to-end differentiable, according to Applicant’s spec. and claim 4.) the ensemble model and the first generator model are jointly trained with an ensemble model loss function that includes the generator model loss function as a regularization term. (uGLAD sec. 3.2 “They leveraged the interpretable nature of the GLAD’s deep architecture to define the loss for training. Specifically, each iteration of the model will output a valid precision matrix estimation and this allowed them to add auxiliary losses to regularize the intermediate results of GLAD, guiding it to learn parameters which can generate a smooth solution trajectory.” The Deep learning model is the claimed first model and the ensemble model. Auxiliary losses are losses from the first model. The intermediate results of GLAD is the ensemble model loss function.)
uGLAD, EnGRaiN and the claims all generate GRNs with gene expression data. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use and end-to-end differentiable model and joint training with auxiliary loss as a regularization term so that “the similarity among the tasks is automatically learned from data.” uGLAD sec. 4
EnGRaiN teaches claims 2, 22 and 28. The method of claim 1, wherein the expression data is microarray data generated by single cell RNA sequencing or bulk sequencing. (EnGRaiN sec. 1 p. 1313 “Using EnGRaiN, we report the construction and analysis of a whole-genome ensemble network of the plant Arabidopsis thali ana, created from painstaking curation of heterogeneous microarray datasets from multiple public repositories.” The microarrays are the claimed expression data generated by bulk sequencing.)
EnGRaiN teaches claim 3. The method of claim 2, further comprising generating expression data with a microarray. (EnGRaiN sec. 1 p. 1313 “Using EnGRaiN, we report the construction and analysis of a whole-genome ensemble network of the plant Arabidopsis thali ana, created from painstaking curation of heterogeneous microarray datasets from multiple public repositories.”)
uGLAD teaches claims 4 and 23. The method of claim 1, wherein the first generator model is one of GLAD, uGLAD, Neural Graph Revealers (NGR), or GRNUlar. (uGLAD abs “uGLAD1 , builds upon and extends the state-of the-art model GLAD [42] to the unsupervised setting.”)
EnGRaiN teaches claims 5 and 24. The method of claim 1, wherein the second generator model is a fixed model that is not trainable. (EnGRaiN sec. 2.1.4 “In EnGRaiN, we do not modify the individual methods participating in the ensemble as done in a ‘joint training’ process (Cheng et al., 2016). Instead, all the networks in the input are created independently by the respective methods without the knowledge of each other.”)
EnGRaiN teaches claim 6. The method of claim 5, wherein the second generator model is one of GENIE3 or GRNBoost2. (EnGRaiN sec. 2.2.1 “all parallel methods… GENIE3, GRNBoost…”)
EnGRaiN teaches claims 7, 25 and 30. The method of claim 1, wherein the ensemble model learns a function for each edge in the consensus graph over the edges present in the first graph and the second graph. (EnGRaiN sec. 2.1.4 “Our ensemble model is able to learn a weighing function over the input methods by using the data of a few thousand edges. Second, this allows us to scale to millions of edges in an efficient manner, as each of these edge-wise predictions can be executed in parallel. Last, the edge-wise prediction approach also facilitates fast inclusion of additional GRN recovery methods into the EnGRaiN framework.”)
EnGRaiN teaches claims 8, 26 and 31. The method of claim 7, wherein the ensemble model is an edge-selector neural network. (EnGRaiN teaches an edge-selector neural network because Spec. para 61 says, “Thus, the ensemble model may be implemented as an edge-selector neural network. One example of a suitable ensemble model is EnGRaiN.”)
EnGRaiN teaches claim 9. The method of claim 1, wherein, in the consensus graph, the nodes represent genes and the edges represents a regulatory relationship between a first one of the genes and a second one of the genes, wherein the first one of the genes is a transcription factor gene. (EnGRaiN fig. 1 shows the graphs where nodes are genes, edges are regulatory relationships between genes, see below. EnGRaiN sec. 2.5 “It includes a total of 1359 non-redundant regulatory interactions between 388 transcription factors and target genes.”)
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uGLAD and EnGRaiN teach claim 29. (New) The computer-readable storage media of claim 27, wherein the first generator model is one of GLAD, uGLAD, Neural Graph Revealers (NGR), or GRNUlar (uGLAD abs “uGLAD1 , builds upon and extends the state-of the-art model GLAD [42] to the unsupervised setting.”) and wherein the second generator model is one of GENIE3 or GRNBoost2. (EnGRaiN sec. 2.2.1 “all parallel methods… GENIE3, GRNBoost…”)
Conclusion
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
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