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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/20/2026 has been entered.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application US 62/408,045 filed 13 October 2016.
Status of the Claims
Claims 1–5, 11, 13–20, 26 and 33 are canceled.
Claims 6-10, 12, 21-25, 27-32 and 34-37 are pending and are examined on the merits.
Withdrawn Rejections/Objections
The rejection of claims 6-10, 12, 21-25, 27-32 and 34-37 under 35 U.S.C. 112(a) in the Office action posted 10/22/2025 is withdrawn in view of claim amendments and persuasive arguments (Remarks: page 17, last para through page 19, 3rd para) both filed 1/20/2026.
However, a new 112(a) rejection is applied.
Claim Rejections - 35 USC § 112—First Paragraph
This rejection is newly installed, necessitated by claim amendments.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-10, 12, 21-25, 27-32 and 34-37 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 6, 21 and 28 all recite: “treating the patient with the combination of perturbagens based on the one or more master regulators, wherein the one or more master regulators are selected from the group consisting of BASP1, NKX6.2, MYCN, and ASCL1, and wherein the combination of perturbagens are selected from the group consisting of an RNA-interference agent and a small molecule” at their last steps, which suggest an embodiment of one RNA-interference agent + one small molecule against the same master regulator.
However, the disclosure of 62/506,413 (at[015]) says clearly combination (of perturbagens) against two or more out of the four master regulators:
“that ASCL1, BASP1, NKX6.2 and MYCN represented core master regulators of
GSCs in general, and that effective inhibition of any combination of 2 or more of these 4 core master regulators, either by genetic means (si/shRNA) or perhaps small molecule inhibitors, would have significant therapeutic potential as a GSC-specific treatment of GBM, and possibly.“
Claims 6, 21 and 28 thus all recite something not supported by disclosure. The claims can be amended to recite limitations provided in paragraph [015] of application 62/506,413.
Claim Rejections - 35 USC § 112—Second Paragraph
This rejection is newly installed, necessitated by claim amendments.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-10, 12, 21-25, 27-32 and 34-37 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6, 21 and 28 all recite (emphasis added): “(h) determining, by the one or more processors, the one or more master regulators based at least in part on one or more genes that are selected based at least in part on the importance score; and treating the patient with the combination of perturbagens based on the one or more master regulators” at step (h) and the next step. It becomes unclear, after three loose logical operations, how the perturbagens relate to the initial nodes are identified.
The claims can be amended to recite that the perturbagens are targeting the master regulators.
The term “a small molecule” in claims 6, 21 and 28 last line is a relative term which renders the claim indefinite. The term “a small molecule” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is not clear how small in order to qualify for “a small molecule”, and the claims are hence indefinite. In fact RNAi is a small molecule.
The claims can be amended to recite specifically a compound, a chemical drug, or something else.
Claim Rejections - 35 USC § 101
The instant rejection is maintained from the previous Office Action filed 10/22/2025 and modified in view of Applicant’s amendments filed 1/20/2026.
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 6-10, 12, 21-25, 27-32 and 34-37 are rejected under 35 USC § 101 because the claimed inventions are directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 6–10, 12 and 35 are directed to a process, here a “method”.
Claims 21–25, 27 and 36 are directed to another process, here another “method”.
Claims 28–32, 34 and 37 are directed to a third process, here a third “method”.
Each of these fall within one of the categories of statutory subject matter.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s)
Receiving, by one or more processors from a data filtering software via a computational processing pipeline, an initial dataset that describes a gene expression network, wherein the gene expression network comprises a regulatory network that is generated based on gene expression data.
--Receiving data from a data filtering software is interpreted as a process affected by human judgement and decision-making; To represent the gene expression data in the way of a regulatory network reads on a natural correlation between the gene expression data and the regulatory network, is a natural correlation and can be achieved in the human mind, so it is also an abstract idea; further, to represent the gene expression data in a graphic regulatory network reads on data re-organization, which can be achieved with the human mind. Therefore this step equates to an abstract idea of mental processes
Extracting, by the one or more processors, one or more subnetworks of the gene expression network.
--Extracting some data out of the existing data reads on data manipulation. Therefore this step equates to an abstract idea of mental processes.
Determining, by the one or more processors, an individual score for each node in the one or more subnetworks;
--Under a broadest reasonable interpretation (BRI), this step is conducted by calculating a score for a node based on topology in the sub-network graph. Therefore this step equates to an abstract idea of mathematical concepts.
Determining, by the one or more processors, a neighborhood score for each node in the one or more subnetworks;
--Under a BRI, this step is conducted by calculating a score for a node based on topology in the sub-network graph. Therefore this step equates to an abstract idea of mathematical concepts.
Generating, by the one or more processors, a combined node score for each node in the one or more subnetworks based at least in part on a combination of the individual score and the neighborhood score.
--Under a BRI, this step is conducted by adding up the two scores acquired in the previous two steps. Therefore this step equates to an abstract idea of mathematical concepts.
Determining, by the one or more processors and based at least in part on one or more performance predictions that are generated by a machine learning model that is trained based at least in part on one or more differential expression datasets, a given parameter set from a plurality of parameter sets that comprises at least the one or more input parameters;
--Under a BRI, the “machine learning model” can be a linear regression model. Hence the “given parameter” is acquired by calculating. Therefore this step equates to an abstract idea of mathematical concepts.
Determining, by the one or more processors, an importance score for each node in the one or more subnetworks based at least in part on the given parameter set and the combined node score;
--Under a BRI, this step is conducted by mathematical calculations. Therefore this step equates to an abstract idea of mathematical concepts.
Determining, by the one or more processors, the one or more master regulators based at least in part on one or more genes that are selected based at least in part on the importance score.
--Under a BRI, this step is conducted by comparing the individual “importance score” to a threshold which is a mathematical operation. Therefore this step equates to an abstract idea of mathematical concepts.
Hence, the claims do recite elements that, individually and in combination, constitute abstract ideas. Dependent claims, such as claims 7-9, 22-24 and 29-31 recite mathematical equations explicitly; claims 10, 12, 25, 27, 32 and 34 recite mathematical operations to calculate different scores. These claims are all directed to abstract ideas of mathematical concepts. The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)).
Step 2A Prong Two: Consideration of Practical Application
The claims result in a treatment for the glioblastoma patients using perturbagens targeting one or more master regulators. The claims do recite additional elements that effect a particular treatment. However, the claims do not integrate the abstract idea into a practical application, because the judicial exceptions (the data analytical processes that leads to the identification of master regulators) is unrelated to the treatment of glioblastoma. The analysis steps (a) to (h) are generic with respect to the disease. The steps do not recite any process steps or data related to glioblastoma. If we switch “glioblastoma” in the preamble in claims 6, 21 and 28 to another disease, there would be no change to the 101 analysis does far. The problem is rooted in the disconnection between the first step (“determining a treatment that targets one or more master regulators, wherein the treatment comprises a combination of perturbagens that are determined by”) and the first step sub-step (a) (“receiving, by one or more processors from a data filtering software via a computational processing pipeline, an initial dataset that describes a gene expression network, wherein the gene expression network comprises a regulatory network that is generated based on gene expression data”). The received data has nothing to do with the glioblastoma. Consequently, we don’t know if the identified master regulators have anything to do with glioblastoma.
The claim also does not recite any specific treatment. A perturbagen being an RNA-interference agent and a small molecule is not a particular treatment.
Furthermore, the claim results in determining an importance score for each node in a subnetwork (step (g)). Then the claim recites determining a master regulator based on the gene selected based on the importance score (step (h)). However the claim never recites how the importance score is used to select one or more genes. Then, the claim never recites how the master regulator is determined “based on” the one or more selected genes. Finally, the claim never recites the relationship between a perturbagen and the determined master regulator. Thus, there is not and integration between the final step of treating and the analytical steps of determining an importance score for each node, determining one or more genes, determining master regulators and finally treating with a perturbagen based on a master regulator.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
“One or more processors" (claim 6).
"A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to" (claim 21).
"One or more non-transitory computer-readable storage media including instructions" (claim 28).
--These limitations do not improve the functions of the computer or integrate the abstract idea into a practical application. The limitations are drawn to a generic computer that performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, as described in MPEP 2106.05(f)
Claims 6, 21 and 28 all recite:
“Treating the patient with the combination of perturbagens based on the one or more master regulators
wherein the one or more master regulators are selected from the group consisting of BASP1, NKX6.2, MYCN, and ASCL1, and
wherein the combination of perturbagens are selected from the group consisting of an RNA-interference agent and a small molecule.”
No other additional elements are recited in dependent claims.
Treating glioblastoma patients using perturbagens and small molecules are known. For example, Cohen et al. ("Localized RNAi therapeutics of chemoresistant grade IV glioma using hyaluronan-grafted lipid-based nanoparticles." ACS nano 9.2 (2015): 1581-1591. Newly cited) disclosed (page 1581, Title and Section “Abstract”) treating glioblastoma patients using RNAi.
Nara et al. ("Silencing of MYCN by RNA interference induces growth inhibition, apoptotic activity and cell differentiation in a neuroblastoma cell line with MYCN amplification." International journal of oncology 30.5 (2007): 1189-1196. Newly cited) disclosed (page 1189, Title and Section “Abstract”) treating glioblastoma cells using RNAi and hyaluronan-grafted lipid-based nanoparticles (which reads on a small molecule) to treat glioblastoma.
Berthold et al. ("Neuroblastoma: current drug therapy recommendations as part of the total treatment approach." Drugs 59.6 (2000): 1261-1277. Newly cited) disclosed (page 1267, Table V) 13 drugs used in glioblastoma therapy.
Ng et al. ("A small interference RNA screen revealed proteasome inhibition as strategy for glioblastoma therapy." Clin Neurosurg 56 (2009): 107-118. Newly cited). demonstrated (page 110, Figure 19.1) that siRNA targeting the PSMA1 gene (a RNA-interference agent targeting PSMA1) sensitize the U87MG cells (a known glioblastoma cell line) to TMZ (a small molecule) treatment, which indicate the combination therapy of siRNA targeting the PSMA1 gene and a small molecule (TMZ) is effective against the U87MG glioblastoma cells.
Targeting the master regulators (such as MYCN) is also known. However, the data and process steps by which the master regulators are identified is generic and unrelated to glioblastoma. Therefore the claim does not integrate abstract ideas into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments - Claim Rejections Under 35 USC § 101
In the Remarks filed 1/20/2026, Applicant argues (page19, last para through page 20, 2nd para) that amended claims now “recite a particular disease (glioblastoma), a particular set of master regulators (BASP1, NKX6.2, MYCN, and ASCL1), and a particular treatment (an RNA-interference agent and a small molecule). Amended claims 35-37 recite a particular treatment (RNA-interference agent that is an siRNA or an shRNA.”
Applicant’s argument refers to Step 2A/Prong two in the 101 analysis, relating to whether claims are integrate into a practical application or not due to an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. In response, Applicant’s argument is not persuasive. There is a particular treatment (against a specific disease using particular agents against specific targets). As discussed above (regarding Step 2A/Prong two), the claim also does not recite any specific treatment. A perturbagen being an RNA-interference agent and a small molecule is not a particular treatment.
Furthermore, the claim results in determining an importance score for each node in a subnetwork (step (g)). Then the claim recites determining a master regulator based on the gene selected based on the importance score (step (h)). However the claim never recites how the importance score is used to select one or more genes. Then, the claim never recites how the master regulator is determined “based on” the one or more selected genes. Finally, the claim never recites the relationship between a perturbagen and the determined master regulator. Thus, there is no integration between the final step of treating and the analytical steps of determining an importance score for each node, determining one or more genes, determining master regulators and finally treating with a perturbagen based on a master regulator.
In the Remarks, Applicant argues (page20, 2nd para) that amended claim elements "wherein the combination of perturbagens are selected from the group consisting of an RNA-interference agent and a small molecule" add significantly more to the recited abstract idea.”
Applicant’s argument refers to Step 2B in the 101 analysis, relating to whether claims are 101 eligible due to significant more. In response, Applicant’s argument is not persuasive. The claim results in determining an importance score for each node in a subnetwork (step (g)). Then the claim recites determining a master regulator based on the gene selected based on the importance score (step (h)). However the claim never recites how the importance score is used to select one or more genes. Then, the claim never recites how the master regulator is determined “based on” the one or more selected genes. Finally, the claim never recites the relationship between a perturbagen and the determined master regulator. Thus, there is no integration between the final step of treating and the analytical steps of determining an importance score for each node, determining one or more genes, determining master regulators and finally treating with a perturbagen based on a master regulator.
Therefore, although the combination of perturbagens are selected from the group consisting of an RNA-interference agent and a small molecule, they have nothing to do with the master regulators under a BRI. Therefore, they don’t add significantly more to the recited abstract idea.
In a summary, for both the Step 2A/Prong two and the Step 2B, the data, the analytical results, and the perturbagens have disconnections in regarding to the glioblastoma disease. The conclusion is there is no integration into a 101 eligible application.
Therefore, the 101 rejection is maintained.
Claim Rejections - 35 USC § 103
The instant rejection is maintained from the previous Office Action filed 10/22/2025 and modified in view of Applicant’s amendments filed 1/20/2026.
The instant rejection is maintained from the previous Office Action filed 3/26/2025 and modified in view of Applicant’s amendments filed 6/25/2025.
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.
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.
Claims 6, 21 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. ("Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory." BMC bioinformatics. Vol. 17. BioMed Central, 2016. Previously cited), in view of Acencio et al. ("Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information." BMC bioinformatics 10 (2009): 1-18. Previously cited), and Ng et al. ("A small interference RNA screen revealed proteasome inhibition as strategy for glioblastoma therapy." Clin Neurosurg 56 (2009): 107-118. Newly cited).
Claim 6 is interpreted as a method of treating glioblastoma patients. Huang teaches Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory, which is similar to claim 6 data analysis section. Particularly,
Huang provide “The input data herein were taken from previous work [6] to enable this study to be compared with [6]. The microarray data for lung cancer were downloaded from GEO [24] and summarized in Table 1. The microarray datasets consist of data from experiments GSE7670 [25] and GSE10072 [26], which were conducted using the HG-U133A array; and data from experiments GSE19804 [27] and GSE27262 [28], which were performed using HGU133 plus 2.0 chip (Huang: 3rd para., col 2, page 14), which teaches (a) receiving gene expression data, because the GEO microarray datasets are about gene expression.
Huang further provides “In the machine learning method, every microarray dataset is processed individually. Before conducting the machine learning algorithms, the DEGs lacking of domain data or PPI data were excluded from the candidate DEGs. The input data concerned only the remaining DEGs. After the machine learning approach was implemented, only DEGs that were identified as cancer proteins by all five topological parameter based classifiers were considered as key genes. Table 2 presents the statistical results in this stage” (Huang: page 18, last para, col 1 through 1st para., col 2), which teaches (a) filtering input data and acquiring expression network data, with subnetworks (Huang: page 23, Fig. 2a and 2b).
Huang provides “In this work, this stage yielded 104 and 123 key genes for the early- and late-stage NSCLC, respectively. Focusing on the top 10 % rather than 20 % yields only 41 and 56 key genes for the early- and late-stage NSCLC” (Huang: page 18, col 2, 2nd para), which teaches (b) extracting the subnetwork data of DEGs from the gene expression data.
Huang provides “To identify key genes in the up- and down-regulated networks respectively the following process was implemented. For each group of DEGs that is classified by a topological parameter, a DEG that ranks in the top 20 % in that parameter will receive a score (S) of one. Clearly, a higher score for a DEG indicates greater importance in the network. DEGs with the highest scores in each group are selected for key genes. The key genes are the union of the two sets with the highest-scoring DEGs in the up- and down-regulated networks. In this work, this stage yielded 104 and 123 key genes for the early- and late-stage NSCLC, respectively. Focusing on the top 10 % rather than 20 % yields only 41 and 56 key genes for the early- and late-stage NSCLC. Relaxing the threshold to 30 % yields 170 and 200 key genes, respectively, which are too many; therefore, top 20 % of classified genes were chosen for key genes” (Huang: 2nd para, col 2, page 18), which teaches implicitly (c) a DEG ranking score for each node (gene), which also teaches implicitly (a) a regulatory network based on gene expression.
Huang provides “These selected DEGs are separately filtered using machine learning classifiers and graph theory, and two corresponding sets of key genes are then derived. Gene set enrichment analysis and pathway analysis were then conducted on the two sets of key genes, and drug-gene interaction databases and the Connectivity Map (cMap) were used to identify potential drugs (with cMap p-value <0.1 and enrichment score <0) for treating NSCLC. The common enriched pathways and drugs that were returned by both machine learning algorithms and the classification of topological parameters were further investigated” (page 14, col 2, 2nd para), which teaches identifying gene target and the drug that targeting the gene.
Huang does not teach performing treatment explicitly. However, Huang teaches clinical trials (page 22, col. 1, par. 2) for identified drugs in NSCLC treatment, multiple targets and multiple drugs (page 22, col 2, Table 10) and FDA approved drugs (page 13, col, 2), which suggests providing a treatment based on drug combination. It would therefore be obvious to one of ordinary skill to combing the teachings of Huang for determining drugs with the suggestions of administering the drugs to arrive at claimed last step (after step h). This is a combination of known elements that would yield a predictable result.
Huang does not teach a neighborhood score.
Acencio teaches prediction of essential genes by integration of network topology, cellular localization and biological process information, which includes the steps of:
Acencio provides “Clustering coefficient (c) of a node (in our case, a gene) quantifies how close the node and its neighbors are to being a clique, i.e., all nodes connected to all nodes” (Acencio: 3rd para., lines 6-9, col 2, page 14), which teaches (d) calculating a neighborhood score for each node, here the clustering coefficient.
Acencio provides “we started the analyzes by assessing the predictability of essential genes by each of the 12 network topological features (computed as described in " Methods") and by all 12 network topological features integrated in a single predictor. For this purpose, we trained our classifier on a balanced dataset with all network topological features as training data and on a dataset containing only one of the network topological features as training data (see "Methods" for detailed information on construction of the balanced datasets). The ROC plot shown in Figure 2 indicates that integration of all networks topological features in a single predictor outperforms the predictability of essential genes by the individual network topological features. By comparing the AUC values of grouped and individual network topological features, we verified that the AUC value of grouped network topological features (AUC = 0.773) is statistically significantly higher (P < 0.002) than AUC value of any individual network topological feature (Figure 2 and Additional file 2)” (Acencio: pages 3-4, connection para) and “we used our best classifier, that is, the one that containing all network topological features, cellular components and biological processes information as training attributes. For each gene, the predictor output the probability of classifying it as essential and non-essential, which we called, respectively, "essentiality score" and "non-essentiality score" (Acencio: page 8, col 1, 1st para last seven lines), which teaches (e) a combined node score, which reflects the node in the subnetwork neighborhood.
Acencio does not teach differentially expressed genes. Huang provides “Microarray data for lung cancer were firstly separated into the early- and late-stage data. Two-pair tests (based on normal and cancer tissues from the same patient) were performed to identify differentially expressed genes (DEGs). A Robust Multi-array Average (RMA) was utilized to normalize gene expression, and eBayes analysis was then performed on the results thereof. DEGs were predicted using an adjusted p-value of 0.05. The selected DEGs were divided into two groups - an up-regulated group and a down-regulated group - based on the fold-changes (FC) in gene expression” (Huang: 2nd para line 1-11, col 2, page 14), which teaches (f) the differential expressed genes and a set of parameters voted by machine learning voting methods (Huang: Fig. 1, page 15).
Acencio provides “For each gene, the predictor output the probability of classifying it as essential and non-essential, which we called, respectively, ‘essentiality score and ‘non-essentiality score’” (Acencio: last para., col 1, page 8), which teaches (g) a combined importance node score for each node in the network.
Acencio provides “To predict a gene as essential, we defined an essentiality score of 0.654 as the cutoff value, i.e., genes with essentiality score above 0.654 were considered to be essential. This cutoff value was based on the optimal threshold, which is the score value that leads to the maximal accuracy of classification, calculated by the software StAR [24]” (Acencio: last para. Lines 1-6, col 2, page 8), which teaches (h) identify master regulators based on importance node scores.
Neither Huang nor Acencio teaches treating glioblastoma patients. Ng demonstrated (page 110, Figure 19.1) that siRNA targeting the PSMA1 gene (a RNA-interference agent targeting PSMA1) sensitize the U87MG cells (a known glioblastoma cell line) to TMZ (a small molecule) treatment, which indicate the combination therapy of siRNA targeting the PSMA1 gene and a small molecule (TMZ) is effective against the U87MG glioblastoma cells.
Both Huang and Acencio mention computing explicitly and both Huang and Acencio used analytical pipeline or software, hence they also teach computer storage medium and processors implicitly. Therefore, the art applied to claim 6 also teaches claims 21 and 28. Because claims 21 and 28 are the computer “system” version and the computer “storage medium” version of claim 6.
It would have been prima facie obvious to combine Huang’s DEG-filtered subnetworks to identify key genes based on machine learning and topological analysis, with additional methods in network neighbor analysis offered by Acencio. Given that Acencio teaches integration of multiple datasets (hence multiple parameters for the network topology analysis) that a large number of pathway analysis procedures are useful for topological analysis.
One would reasonably expect success for the combination, as both Huang and Acencio are about essential (or key) gene identification, and Huang’s machine learning and DEG data can benefit from Acencio’s elegant topology analyzing methods and parameters.
It would have been prima facie obvious to combine the pipeline of Huang and Acenio, specialized in key regulator discoveries, through topological data analysis, with Ng’s method in combination therapy of glioblastoma patients using RNA-interference molecule and small molecule compound against therapeutic targets. Because the glioblastoma patients need effective treatments.
One would reasonably expect success for the combination because therapeutic target identifications and therapeutic agent discoveries are two important aspects in the medical industry. Therapeutic target identifications and therapeutic agent discoveries do not interfere with each other but rely on each other. For the benefit of glioblastoma patients, the combination of combined Huang and Acenio, and Ng would be a classic example of:
Combining prior art elements according to known methods to yield predictable results (MPEP §2141.III.(A));
Claims 12, 27 and 34-37 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, Acencio and Ng as applied to claims 6, 21 and 28 above, and further in view of Huang, et al. (“Method for discovering potential drugs”, US 2011/0287953, previously cited. Hereafter referred as Huang_b. Previously cited).
.
The combination of Huang, Acencio and Ng teach a method of treating glioblastoma patients through scoring nodes in a biological network by importance, but does not teach the steps recited in these claims.
Claim 12 is directed to the method of claim 6, further comprising
(a) "generating a gene expression network"
(b) "determining importance scores for the genes in the gene expression network"
(c) "identifying a predetermined number of genes having the highest determined importance scores"
(d) "selecting a set of core master regulators …"
(e) "testing candidate perturbagens …"
(f) "developing a predictive test …"
Huang_b teaches
(a) generating a biological network using gene expression data (0036–0037)
(b) scoring gene nodes in the network by centrality (0043)
(c) identifying clique (“bottleneck”) genes having high centrality (0048)
(d) selecting bottleneck genes from among the clique genes (0048)
(e) identifying drugs that target the “bottleneck” genes (0080)
(f) selecting drugs likely to have be effective for treatment based on their targets, and planning functional assays to verify efficacy (0084)
Claims 35-37 are directed to a subset the method of claim 6, system claim of claim 21, and the storage medium claim 28 respectively, further comprising
(a) "selecting a set of master regulators …"
(b) "testing candidate perturbagens or master regulator with drug …"
Huang_b teaches
(a) selecting bottleneck genes from among the clique genes (0048)
(b) identifying drugs that target the “bottleneck” genes (0080).
Huang_b’s bottleneck gene is the master regulator gene.
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention. Prior to the time of invention, said practitioner would have been motivated to use the topological analysis and centrality measures taught by Huang, Acencio and Ng to identify important genes in the gene network taught by Huang_b, because Huang, Acencio and Ng teach that these procedures are advantageous for identifying important nodes in a biological network. Given that Huang_b teaches a number of compatible centrality measures, and requires only that the nodes in the network be ranked somehow, said practitioner would have readily predicted that the modification would successfully result in a method of identifying and validating important, druggable genes in a biological network as claimed. The inventions are therefore prima facie obvious.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the Remarks filed 20 January 2026 (pages 20-21), Applicant argued against art applied. Specifically, Applicant argues the newly amended claims 6, 21 and 28 elements “treating the patient with the combination of perturbagens” is not taught by art. Applicant’s argument is not persuasive, for the following reasons:
As discussed above over the 112(b) rejection, the claims 6, 21 and 25 are not interpreted as if targeting the four master regulators listed. The three consecutive “based on” render the claim indefinite. A perturbagen “based on” a master regulator does not necessarily mean A perturbagen “targeting” a master regulator.
Ng teaches a combination (siRNA against PSMA1 gene + TMZ) therapy for the U87MG glioblastoma cells.
Therefore, the 103 rejection is maintained.
Conclusion
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635