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 .
Election/Restrictions
Applicant’s election without traverse of Group I, drawn to claims 1-10, in the reply filed on 09 Sept. 2026 is acknowledged.
Claims 11-15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 09 Sept. 2026.
Status of Claims
Claims 1-15 are pending.
Claims 11-15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Claims 1-10 are rejected.
Claims 2-6 and 8 are objected to
Priority
Applicant’s claim for the benefit of a prior-filed application, PCT/US2021/072199 filed 03 Nov. 2021 and U.S. Provisional App. No. 63/109,147 filed 03 Nov. 2020 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Accordingly, the effective filing date of the claimed invention is 03 Nov. 2020.
Drawings
The drawings filed 02 May 2023 are objected to because:
the drawings fail to comply with 37 CFR. 1.84(u)(1), which states view numbers must be preceded by the abbreviation "FIG.", and 37 CFR 1.84(u)(2), which states numbers and letters identifying the views must be simple and clear and must not be used in association with brackets, circles, or inverted commas. The view numbers should be changed to FIG. 1, FIG. 2, etc., without underlines used in association with the numbers.
FIG. 6 fails to comply with 37 CFR 1.84(p)(5) because it includes the following reference character(s) not mentioned in the description: #65.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) and/or 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 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 Objections
Claims 2-6 and 8 are objected to because of the following informalities:
Claims 2-5 and 9 recite “The method of claim 1 further comprises…”, which should be amended to recite “The method of claim 1, further comprising[[es]]…”.
Claim 6 recites “wherein the gene expression data for the sample cell is further defined as RNA-seq data”. To increase clarity and readability, claim 6 should be amended to recite “wherein the gene expression data for the sample cell is
Claim 8 recites “that minimize distance between…”, which is a grammatical error and should recite “that minimize a distance between”.
Appropriate correction is required.
Claim Interpretation
Claim 1 recites “constructing a graph for a genome, where each node in the graph represents a gene…”. Applicant’s specification at para. [0024]-[0025] discloses the constructed graph is restricted to protein-coding genes in the genome, and a corresponding A-matrix represents all possible protein-protein interactions, and at para. [0032] further disclose the adjacency matrix is formed from the A-matrix representing protein-protein interactions. Accordingly, “each node represents a gene” is interpreted encompass each node corresponding to a protein (which necessarily represents a gene) or a gene under the broadest reasonable interpretation of the claim, in light of Applicant’s specification.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “each node in the graph…and each edge in the graph…”. However, claim 1 previously recited “constructing a graph for a genome”, but did not require the graph has nodes and edges. As a result, it is not clear what set of nodes and edges “each node” and “each edge are referring to. For purpose of examination, the limitation is interpreted to mean “constructing a graph for a genome, wherein the graph includes nodes and edges, and wherein each node…and each edge…”.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “each edge…quantifies the relationship between two genes”. There is insufficient antecedent basis for “the relationship between two genes” because claim 1 does not previously recite a relationship between two genes. Furthermore, given there are many types of relationships that may exist between two genes, it is not clear what relationship is being referenced.
Claim 1, and claims dependent therefrom, are indefinite for recitation of “the regulatory set for the given transcription factor…and an input vector…represents the given transcription factor”. There is insufficient antecedent basis for “the regulatory set for the given transcription factor” because claim 1 previously recites “computing a regulatory set for a set of transcription factors”, such that the regulatory set is for multiple transcription factors and not “a given transcription factor”. As a result, it is not clear if Applicant intends to determine a regulatory set for each transcription factor for the set of transcription factors, or if there is one regulatory set for the set of transcription factors. Last, given claim 1 previously recites “a set of transcription factors”, it is not clear what transcription factor “the transcription factor” is referring to. It is noted that Applicant’s specification at para. [0033], suggests a matrix for a regulatory set is generated for each transcription factor, such that the input matrix is for a regulatory set for a given transcription factor. However, dependent claim 7 recites that the input matrix is actually a joined plurality of regulatory sets (i.e. one for each transcription factor). Therefore, this further raises confusion on whether the input matrix is for a “given transcription factor” or there is one input matrix for the set of transcription factors. Clarification is requested via claim amendment. In light of Applicant’s specification, and claim 7, claim 1 is interpreted to mean “computing a regulatory set for each transcription factor of a set of transcription factors, wherein each regulatory set quantifies influence of the respective transcription factor in the set of transcription factors”. The input matrix is interpreted to be the regulatory set for a given transcription factor of the set of transcription factors and the input vector represents the given transcription factor of the set of transcription factors. Later reference to “the given transcription factor” in claim 1 and dependent claims will be interpreted to this given transcription factor.
Clai 1 recites “manipulating…wherein…the at least one transcription factor is in the input vector”. Claim 1 previously recites “an input vector…represents the given transcription factor”. As a result, it is not clear if the input vector represents one transcription factor, or if the input vector may represent multiple transcription factors, as encompassed by “the at least one transcription factor is in the input vector”. For purpose of examination, the manipulation step is interpreted to mean at least one transcription factor that includes the given transcription factor are manipulated.
Claim 4 is indefinite for recitation of “The method of claim 3 further comprises identifying a subset of vertices in the graph using one of degree centrality, closeness centrality…”. Claim 3 recites “identifying a subset of vertices in the graph based on centrality”. It is unclear if claim 4 intends to identify a second subset of vertices that is also based on centrality (e.g. degree centrality, closeness centrality, etc.), or if claim 4 intends to further limit the identification of the subset of vertices in claim 3 such that only one subset based on centrality is determined. For purpose of examination, claim 4 is interpreted to further limit the centrality of claim 3.
Claim 7 is indefinite for recitation of “computing a regulatory set for a set of transcription factors comprises computing a regulatory set for each of a plurality of transcription factors and joining the plurality of regulatory sets to form the input matrix”. Claim 1 recites “the regulatory set for the given transcription factor serves as an input matrix”, which was indefinite as discussed above. Claim 7 further raises confusion regarding whether the input matrix is a regulatory set for a single transcription factor (i.e. “the given transcription factor”) or if the input matrix is for the set of transcription factors. Clarification is requested via claim amendment.
Claim 10 is indefinite for recitation of “the given transcription factor” Claim 1, from which claim 10 depends, recites “the given transcription factor” and claim 10 also recites “a given transcription factor” (which is not required to be the same transcription factor as in claim 1). Therefore, it is not clear which given transcription factor is being referenced, or if these are intended to be the same transcription factor. For purpose of examination, the given transcription factor in claims 1 and 10 are interpreted to be the same.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 7 is rejected under 35 U.S.C. 112(d), as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 7 recites “joining the plurality of regulatory sets to form the input matrix”, wherein there is a regulatory set for each transcription factor. However, claim 1 already recites “the regulatory set for the given transcription factor serves as an input matrix”. Therefore, claim 7 does not include all the limitations of claim 1, from which it depends, given it replaces the input matrix in claim 1 with a different input matrix as recited in claim 7. See MPEP 608.01(n) III, which states, for example, if claim 1 recites the combination of elements A, B, C, and D, a claim reciting the structure of claim 1 in which D was omitted or replaced by E would not be a proper dependent claim
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim 1 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim 1 recites the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
constructing a graph for a genome, where each node in the graph represents a gene in the genome and each edge in the graph quantifies the relationship between two genes (mental process and mathematical concept);
forming an adjacency matrix from the graph (mental process and mathematical concept).
computing a regulatory set for a set of transcription factors, where the regulatory set quantifies influence of the transcription factors in the set of transcription factors on a genome (mental process and mathematical concept).
expressing reprogramming of the sample cell to the target cell with a state- space representation of a linear system, where the gene expression data for the target cell serves as an output vector in the state-space representation, the adjacency matrix serves as a state transition matrix, the gene expression data for the sample cell serves as a state vector in the state-space representation, the regulatory set for the given transcription factor serves as an input matrix in the state-space representation, and an input vector in the state-space representation represents the given transcription factor (mathematical concept);
solving for the input vector in the state-space representation (mathematical concept).
The identified claim limitations falls into one of the groups of abstract ideas of mathematical concepts and/or mental processes for the following reasons. First, constructing a node-edge graph for a genome with nodes representing genes and edges quantifying a relationship between genes can be practically performed in the mind aided with pen and paper by analyzing expression information for a set of genes and drawing edges between genes with correlated expression, which amounts to a mere analysis of data. Forming an adjacency matrix from the graph can be practically performed in the mind, aided with pen and paper, by drawing a matrix with a row and column for each gene, and writing either 0 or 1 in each cell of the matrix depending on whether the graph includes an edge between the two respective genes corresponding to the cell. Furthermore, the constructing and forming limitations further recite a mathematical relationship analogous to organizing information and manipulating information through mathematical correlations in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). Computing a regulatory set for a set of transcription factors can be practically performed in the mind by analyzing the influence of transcription factors on gene expression of genes in a genome (e.g. analyzing correlations). Furthermore, the only discloses embodiments in Applicant’s specification for computing a regulatory set that quantify an effect of a transcription factor on a genome encompasses generating a matrix that encodes where a control signal can influence an existing network with the matrix representing regulating weight of TF on TAD k, and therefore this limitation further recites a mathematical concept. Expressing reprogramming of the sample cell with a state-space representation of a linear system and solving for the input vector recite a mathematical calculation, as the limitations require mathematically solving for an input vector from gene expression data for the target as an output vector, the adjacency matrix as state transition matrix, the gene expression data as a state vector, and the regulatory set as an input matrix (i.e. mathematical calculations). The expression of the reprogramming with a state-space representation further recites a mathematical equation.
Dependent claims 2-5 and 7-9 further recite an abstract idea and/or are part of the abstract idea identified above. Dependent claim 2 further recites the mental process of constructing a graph by representing various interactions with a series of matrices. Dependent claims 3-4 further recite the mental process of identifying a subset of vertices by analyzing a type of centrality. Dependent claim 5 further recites the mental process of extracting a subgraph from the graph, and further limits the mental process and mathematical concept of forming the adjacency matrix to be from the subgraph. Dependent claim 7 further limits the mathematical concept and mental process of computing a regulatory set to be for each transcription factor and further recites the mental process and mathematical concept of joining the plurality of regulatory sets to form a single matrix. Dependent claims 8-9 further limit the mathematical concept of solving the input vector to involve minimizing a distance and performing a least squares method. Therefore, claims 1-10 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Dependent claims 2-5 and 7-9 do not recite any elements in addition to the judicial exception and therefore are part of the judicial exception.
The additional elements of claim 1 include:
receiving a biological sample of a sample cell from the subject, where the sample cell has a given cell type;
determining gene expression data for the sample cell from the biological sample;
receiving gene expression data for a target cell having a target cell type, where the target cell type differs from the given cell type; and
manipulating at least one transcription factor in a particular cell of the subject, where the particular cell has the given cell type and the at least one transcription factor is in the input vector.
The additional element of claim 6 includes:
wherein the gene expression data for the sample cell is further defined as RNA-seq data.
The additional element of claim 9 includes:
wherein manipulating at least one transcription factor includes at least one of introducing a given transcription factor into the particular cell or removing the given transcription factor from the particular cell.
The additional elements of receiving a sample of a sample cell from the subject, determining RNA-seq gene expression data for the sample cell, and receiving gene expression data for the target cell are not sufficient to integrate the recited judicial exception into a practical application because the limitations only serve to collect gene expression data for use by the abstract idea, which is not sufficient to provide integration. See MPEP 2106.05(g).
Regarding the step of manipulating at least one transcription factor that includes the given transcription factor in a particular cell of the subject by introducing or removing a given transcription factor into or from the particular cell, the limitation is not sufficient to integrate the recited judicial exception into a practical application because it amounts to mere instructions to apply the exception. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743. See MPEP 2106.05(f). In the instant case, the claims only recite the idea of a solution of reprogramming a cell, but fail to recite details regarding how the at least one transcription factor is manipulated using the solved input vector representing the given transcription factor. The claims merely solve the input vector for the given transcription factor, but provides no details regarding how the solved input vector is used to inform the manipulating. Therefore, the additional elements amount to mere instructions to apply the exception in a generic way.
Therefore, the additionally recited elements amount to insignificant extra-solution activity and/or mere instructions to apply the exception, and as such, the claims as a whole do not integrate the abstract idea into practical application. Thus, claims 1-10 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
Dependent claims 2-5 and 7-9 do not recite any elements in addition to the judicial exception and therefore are part of the judicial exception.
The additional elements of claim 1 include:
receiving a biological sample of a sample cell from the subject, where the sample cell has a given cell type;
determining gene expression data for the sample cell from the biological sample;
receiving gene expression data for a target cell having a target cell type, where the target cell type differs from the given cell type; and
manipulating at least one transcription factor in a particular cell of the subject, where the particular cell has the given cell type and the at least one transcription factor is in the input vector.
The additional element of claim 6 includes:
wherein the gene expression data for the sample cell is further defined as RNA-seq data.
The additional element of claim 9 includes:
wherein manipulating at least one transcription factor includes at least one of introducing a given transcription factor into the particular cell or removing the given transcription factor from the particular cell.
First, regarding “receiving gene expression data for a target cell…”, the limitation encompasses a computer receiving already generated gene expression data. There is no requirement that the gene expression data is determined from a sample of the target cell. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of receiving a sample of a sample cell from the subject, determining RNA-seq gene expression data for the sample cell, and manipulating at least one transcription factor in a particular cell having the given cell type of the subject by introducing or removing the given transcription factor to or from the particular cell are well-understood, routine, and conventional, even considered in combination. This position is supported by Camp et al. (Single-cell genomics to guide human stem cell and tissue engineering, 2018, Nature Methods, 15, pg. 661-667). Camp reviews the application of single-cell genomics (SCG), including RNA-seq, to engineering cells and tissues (Abstract; pg. 661, col. 2, para. 2), which includes using scRNA-seq data to quantify the accuracy, precision, and efficiency of cell and organ engineering (pg. 661, col. 2, para. 3). Camp discloses using scRNA-seq to computationally reconstruct cell differentiation trajectories, which can be analyzed to detect differences in gene expression between different cell types, and then guide strategies to improve the reverse engineering organoid culture protocol (i.e. scRNA-seq is analyzed to inform cell reprogramming) (pg. 662, col. 2, para. 2 to pg. 663, col. 1, para. 2). Camp further discloses that scRNA-seq data can be used to identify the transcription factors (TFs) expressed in a specific cel subtype and at branching points along a differentiation path, predict TF combinations to differentiate input cells to a target cel subtype, and performed pooled screens to test the transcription factor combinations for the reverse engineering of cell types (pg. 663, col. 1, para. 3-4; Fig. 2), which involves expressing TFs in cels (pg. 661, col. 1, para. 2).
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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.
Claims 1 and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Rajapakse (2018) in view of Wang (2015).
Cited references:
Rajapakse et al. US 2018/0129775 A1 (cited in IDS filed 02 May 2023); and
Wang et al., WO 2015/054266 A1
Regarding claim 1, Rajapakse discloses a method for cell reprogramming (Abstract), comprising the following steps:
Rajapakse discloses receiving a biological sample of a sample cell from the subject, where the sample cell has a given cell type (claim 1; [0055]; FIG. 2)
Rajapakse discloses determining gene expression data for the sample cell from the biological sample (claim 1; [0056]; FIG. 2).
Rajapakse discloses forming a state transition matrix which models cell dynamics ([0009]; [0058]), wherein the model is determined based on measured mRNA activity associated with genes (i.e. expression data) ([0045]).
Rajapakse discloses receiving gene expression data for a target cell having a target cell type, where the target cell type differs from the given cell type (claim 1; [0057]; FIG. 2).
Rajapakse discloses computing a regulatory set for one or more transcription factors (i.e. a set of transcription factors), where a regulatory set quantifies influence of a given transcription factor on a genome ([0058]).
Rajapakse discloses expressing reprogramming of the sample cell with a state-space representation of a linear system, where the gene expression data for the target cell serves as an output vector in the state-space representation, the gene expression data for the sample cell serves as a state vector in the state-space representation, the regulatory set for the given transcription factor serves as an input matrix in the state-space representation, and an input vector in the state-space representation represents the given transcription factor ([0059]).
Rajapakse discloses solving for the input vector in the state-space representation ([0060]).
Rajapakse discloses introducing the given transcription factor (i.e. at least one transcription factor in the input vector) into a particular cell of the subject, where the particular cell has the given cell type (claim 11).
Further regarding claim 1, Rajapakse does not disclose the following limitations:
Rajapakse does not disclose constructing a graph for a genome, where each node in the graph represents a gene in the genome and each edge in the graph quantifies the relationship between two genes, forming an adjacency matrix from the graph, and wherein the adjacency matrix serves as the state transition matrix.
However, Wang discloses method for predictive optimization of gene network responses (Abstract), which comprises creating a system response model comprising nodes and edges within a network graph, wherein the graph network is associated with predictors of the system response ([0005]). Wang discloses the network graph may be a gene regulatory network or protein-protein interaction (PPI) network (i.e. nodes are genes/proteins), with edges including confidence scores relating to the evidence of the interaction (i.e. each edge quantifies the relationship between nodes) ([0005]; [0008]-[0009]; [0042]), and further discloses the system response may be morphology, behavior, or cell type labels and the predictors of system response (e.g. cell type/behavior) include gene expression data ([0005]; [0009]). Wang discloses the network can be represented by an adjacency matrix A ([0042]) and then used as a state transition matrix ([0046], e.g. state transition matrix is normalized adjacency matrix).
It would have been prima obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Rajapakse to have further constructed the graph for a genome, form an adjacency matrix from the graph, and then use the adjacency matrix as a state transition matrix, as shown by Wang above. One of ordinary skill in the art would have been motivated to combine the methods of based on the simple substitution of the transition state matrix modeling cell dynamics based of Rajapakse ([0009]) with the transition state matrix corresponding to the adjacency matrix of the graph network, which also models cell dynamics (i.e. gene regulation or protein/protein interaction) of Wang, given the function of both state transition matrices are to represent cell dynamics and ultimately predict transitions regarding cell types (Rajapakse: claim 1, e.g. used for cell reprogramming; Wang: [0005]; [0009]; e.g. predicted system response includes cell type labels). One of ordinary skill in the art would have substituted the transition matrix of Wang with the transition matrix of Rajapakse, given both Rajapakse and Wang use the transition matrices to model a system response of a cell type or cell reprogramming, and base the modeling of cell dynamics data on gene expression data.
Regarding claim 6, Rajapakse further discloses the gene expression data of the sample cel is RNA-seq data (claim 14; [0056]).
Regarding claim 7, Rajapakse further discloses computing a regulator set for each of a plurality of transcription factors and joining the plurality of regulatory sets to form the input matrix (claim 6; [0013]).
Regarding claims 8-9, Rajapakse further discloses solving for the input vector includes determining values for the input vector that minimize distance between the sample cell and the target cell using a least squares method ([0014]).
Regarding claim 10, Rajapakse further discloses the manipulating comprises introducing the given transcription factor into the particular cell of the subject (claims 11 and 19).
Therefore, the invention is prima facie obvious.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Rajapakse in view of Wang, as applied to claim 1 above, and further in view of Lu (2013).
Cited reference: Lu et al., Using graph models to find transcription factor modules: the hitting set problem and an exact algorithm, 2013, Algorithms for Molecular Biology, 8(2), pg. 1-19 (cited in IDS filed 02 May 2023).
Regarding claim 2, Rajapakse in view of Wang disclose the method of claim 1 as applied above.
Further regarding claim 2, Rajapakse in view of Wang, as applied to claim 1 above, do not disclose constructing a graph for a genome by representing protein-protein interactions, transcription-DNA interactions, and transcription factor-transcription factor interactions with a series of matrices.
However, Lu discloses a method of using graph models to find transcription factor modules (Abstract), which comprises constructing a graph for a genome comprising interactions between proteins, interactions between transcription factors and genes (i.e. DNA), and interactions between transcription factors (Figure 1, e.g. nodes ; pg. 1, col. 1, para. 2). Lu further discloses determining TF modules (i.e. subgraphs) from the graph (Figure 3; pg. 7, col. 1, para. 1-2). Lu further discloses analysis of the graph allows for the identification of optimal combinations of transcription factors that explain gene expression patterns in a context-specific setting (pg. 1, para. 2, col. 1).
I would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Rajapakse in view of Wang, as applied to claim 1 above, to have constructed a graph model according to Lu, as discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Rajapakse in view of Wang with Lu, in order to facilitate the identification of optimal combinations of transcription factors that explain gene expression patterns, as shown by Lu (pg. 1, para. 2, col 1, para. 1), given gene expression patterns are also predictive of cell reprogramming, as shown by Rajapakse (claim 1; [0009]). This modification would have a had a reasonable expectation of success given the graph constructed in claim 2 is not used in any way in the method of claim 1, and one of ordinary skill in the art could have constructed a graph in accordance with Lu.
Regarding representing the various interactions with a series of matrices, Wang discloses graph networks may be represented as an adjacency matrix ([0042]), as applied to claim 1 above. Therefore, the graph and subgraphs of Lu may similarly be represented as adjacency matrices.
It would have been further prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Rajapakse in view of Wang and Lu, to have further represented the graph and subgraphs of Lu as adjacency matrices according to the teaching of Wang ([0042]) in order to facilitate mathematical analysis of the network, as shown by Wang ([0046]-[0048]). This modification would have had a reasonable expectation of success given Wang discloses graph networks may be represented by a matrix ([0042]).
Therefore, the invention is prima facie obvious.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Rajapakse in view of Wang, as applied to claim 1 above, and further in view of Liu (2018).
Cited reference: Liu et al., Genome Architecture Mediates Transcriptional Control of Human Myogenic Reprogramming, 2018, iScience, 6, pg. 232-246.
Regarding claims 3-4, Rajapakse in view of Wang disclose the method of claim 1 as applied above.
Further regarding claims 3-4, Rajapakse in view of Wang as applied to claim 1 above do not disclose identifying a subset of vertices in the graph based on one of degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality.
However, Liu discloses a method for evaluating topological features of genome architecture based on network centrality (Abstract), which comprises determining measures of centrality, including degree centrality, eigenvector centrality, and betweenness centrality, to identify nodes that play influential topical roles and reflect importance genomic loci within in a gene interaction network (pg. 233, para. 2 and 7-8; Figure 2, e.g. nodes represent FPKM RNA-seq value of gene and edges quantify Hi-C contact; pg. 234, para. 4 and pg. 236, para. 1). Liu discloses the method can be used to understand regulatory dynamics during reprogramming, including early-stage gene expression dynamics (pg. 241, para. 6; pg. 244, para. 5 to pg. 245, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Rajapakse in view of Wang, as applied to claim 1 above, to have further identified a subset of nodes (i.e. vertices) in the graph using one of degree centrality, betweenness centrality, and eigenvector centrality as shown by Liu above. One of ordinary skill in the art would have been motivated to combine the methods of Rajapakse in view of Wang with Liu in order to better understand regulatory dynamics during reprogramming, including early-stage gene expression dynamics, as shown by Liu (pg. 241, para. 6; pg. 244, para. 5 to pg. 245, para. 1), given Rajapakse also models cell dynamics in cell reprogramming (Abstract). This modification would have had a reasonable expectation of success given one of ordinary skill in the art could apply the method of Liu to explore regulatory dynamics in the gene regulatory or protein interaction network of Rajapakse in view of Wang.
Therefore, the invention is prima facie obvious.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Rajapakse in view of Wang, as applied to claim 1 above, and further in view of Mohammadi (2019).
Cited reference: Mohammadi et al., Reconstruction of cell-type specific interactomes at single-cell resolution, 2019, Cell Syst. 9(6), pg. 559-568.
Regarding claim 5, Rajapakse in view of Wang disclose the method of claim 1 as applied above.
Further regarding claim 5, Rajapakse in view of Wang as applied to claim 1 above do not disclose extracting a subgraph from the graph and forming the adjacency matrix from the subgraph, where the subgraph represents a specific cell type.
However, Mohammadi discloses a method of constructing cell-type specific interactomes of genes (Abstract; pg. 8, para. 4, e.g. nodes are genes), which comprises obtaining single-cell RNA sequencing data of a given cell type, using the single-cell RNA sequencing data to estimate the distribution and variability of interaction strengths within the given cell types, and constructing a subnetwork based on the estimated strengths (Figure 1 A-C; pg. 4, para. 3 to pg. 5, para. 1), wherein the resulting cell-type specific network have between 34.4% to 54.6% of the genes of the original reference network (i.e. the cell-type specific network is a subnetwork) (pg. 5, para. 3). Mohammadi further discloses representing the cell-type specific network as an adjacency matrix, and using the adjacency matrix as a transition matrix (pg. 14, para. 3). Mohammadi discloses these cell-specific networks enable the distinction of topologically-specific genes whose interactions are highly cell-type-specific, and can be used to study the relationship between cell-type specific hubness and the context-specific role of corresponding genes (pg. 3, para. 3; pg. 4, para. 2).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Rajapakse in view of Wang as applied to claim 1 to have extracted a subgraph representing a specific cell type from the graph and form the adjacency matrix from the subgraph, as shown by Mohammadi above. One of ordinary skill in the art would have been motivated to combine the methods of Rajapakse in view of Wang with Mohammadi in order to provide an adjacency matrix that represents topologically-specific genes with highly cell-type specific interactions, (pg. 3, para. 3; pg. 4, para. 2), given Rajapakse in view of Wang use networks of cell dynamics to model a particular cell type or cell reprogramming. This modification would have had a reasonable expectation of success given both Rajapakse in view of Wang and Mohammadi represent an interaction network by an adjacency matrix and use the adjacency matrix as a state-transition matrix.
Therefore, the invention is prima facie obvious.
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 and 6-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 10,672,501 in view of Wang (2015).
Cited reference: Wang et al., WO 2015054266 A1
Reference claim 1 discloses the limitations of instant claim 1, except the reference claims do not disclose constructing a graph for a genome, where each node in the graph represents a gene in the genome and each edge in the graph quantifies the relationship between two genes, forming an adjacency matrix from the graph, and wherein the adjacency matrix serves as the state transition matrix.
However, Wang discloses method for predictive optimization of gene network responses (Abstract), which comprises creating a system response model comprising nodes and edges within a network graph, wherein the graph network is associated with predictors of the system response ([0005]). Wang discloses the network graph may be a gene regulatory network or protein-protein interaction (PPI) network (i.e. nodes are genes/proteins), with edges including confidence scores relating to the evidence of the interaction (i.e. each edge quantifies the relationship between nodes) ([0005]; [0008]-[0009]; [0042]), and further discloses the system response may be morphology, behavior, or cell type labels and the predictors of system response (e.g. cell type/behavior) include gene expression data ([0005]; [0009]). Wang discloses the network can be represented by an adjacency matrix A ([0042]) and then used as a state transition matrix ([0046], e.g. state transition matrix is normalized adjacency matrix).
It would have been prima obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of the reference claims to have further constructed the graph for a genome, form an adjacency matrix from the graph, and then use the adjacency matrix as a state transition matrix, as shown by Wang above. One of ordinary skill in the art would have been motivated to combine the methods of based on the simple substitution of the transition state matrix modeling cell dynamics based of the reference claims (reference claim 1) with the transition state matrix corresponding to the adjacency matrix of the graph network, which also models cell dynamics (i.e. gene regulation or protein/protein interaction) of Wang, given the function of both state transition matrices are to represent cell dynamics and ultimately predict transitions regarding cell types (Wang: [0005]; [0009]; e.g. predicted system response includes cell type labels). One of ordinary skill in the art would have substituted the transition matrix of Wang with the transition matrix of the reference claims, given both the reference claims and Wang use the transition matrices to model a system response of a cell type or cell reprogramming, and base the modeling of cell dynamics data on gene expression data.
Reference claim 4 discloses the limitation of instant claim 6.
Reference claim 5 discloses the limitations of instant claim 7.
Reference claim 6 discloses the limitation of instant claim 8.
Reference claim 7 discloses the limitation of instant claim 9.
Reference claim 1 discloses the limitation of instant claim 10.
Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 10,672,501 in view of Wang (2015), as applied to claim 1 above, and further in view of Lu (2013).
Cited reference: Lu et al., Using graph models to find transcription factor modules: the hitting set problem and an exact algorithm, 2013, Algorithms for Molecular Biology, 8(2), pg. 1-19 (cited in IDS filed 02 May 2023).
Regarding instant claim 2, the reference claims in view of Wang disclose instant claim 1 above.
Further regarding instant claim 2, the reference claims in view of Wang, as applied to claim 1 above, do not disclose constructing a graph for a genome by representing protein-protein interactions, transcription-DNA interactions, and transcription factor-transcription factor interactions with a series of matrices.
However, Lu discloses a method of using graph models to find transcription factor modules (Abstract), which comprises constructing a graph for a genome comprising interactions between proteins, interactions between transcription factors and genes (i.e. DNA), and interactions between transcription factors (Figure 1, e.g. nodes ; pg. 1, col. 1, para. 2). Lu further discloses determining TF modules (i.e. subgraphs) from the graph (Figure 3; pg. 7, col. 1, para. 1-2). Lu further discloses analysis of the graph allows for the identification of optimal combinations of transcription factors that explain gene expression patterns in a context-specific setting (pg. 1, para. 2, col. 1).
I would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of the reference claims in view of Wang, as applied to claim 1 above, to have constructed a graph model according to Lu, as discussed above. One of ordinary skill in the art would have been motivated to combine the methods of the reference claims in view of Wang with Lu, in order to facilitate the identification of optimal combinations of transcription factors that explain gene expression patterns, as shown by Lu (pg. 1, para. 2, col 1, para. 1), given gene expression patterns are also predictive of cell reprogramming, as shown by Rajapakse (claim 1; [0009]). This modification would have a had a reasonable expectation of success given the graph constructed in instant claim 2 is not used in any way in the method of instant claim 1, and one of ordinary skill in the art could have constructed a graph in accordance with Lu.
Regarding representing the various interactions with a series of matrices, Wang discloses graph networks may be represented as an adjacency matrix ([0042]), as applied to instant claim 1 above. Therefore, the graph and subgraphs of Lu may similarly be represented as adjacency matrices.
It would have been further prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of the reference claims in view of Wang and Lu, to have further represented the graph and subgraphs of Lu as adjacency matrices according to the teaching of Wang ([0042]) in order to facilitate mathematical analysis of the network, as shown by Wang ([0046]-[0048]). This modification would have had a reasonable expectation of success given Wang discloses graph networks may be represented by a matrix ([0042]).
Claims 3-4 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 10,672,501 in view of Wang (2015), as applied to claim 1 above, and further in view of Liu (2018).
Cited reference: Liu et al., Genome Architecture Mediates Transcriptional Control of Human Myogenic Reprogramming, 2018, iScience, 6, pg. 232-246.
Regarding instant claims 3-4, the reference claims in view of Wang disclose the method of claim 1 as applied above.
Further regarding instant claims 3-4, the reference claims in view of Wang as applied to claim 1 above do not disclose identifying a subset of vertices in the graph based on one of degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality.
However, Liu discloses a method for evaluating topological features of genome architecture based on network centrality (Abstract), which comprises determining measures of centrality, including degree centrality, eigenvector centrality, and betweenness centrality, to identify nodes that play influential topical roles and reflect importance genomic loci within in a gene interaction network (pg. 233, para. 2 and 7-8; Figure 2, e.g. nodes represent FPKM RNA-seq value of gene and edges quantify Hi-C contact; pg. 234, para. 4 and pg. 236, para. 1). Liu discloses the method can be used to understand regulatory dynamics during reprogramming, including early-stage gene expression dynamics (pg. 241, para. 6; pg. 244, para. 5 to pg. 245, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of the reference claims in view of Wang, as applied to instant claim 1 above, to have further identified a subset of nodes (i.e. vertices) in the graph using one of degree centrality, betweenness centrality, and eigenvector centrality as shown by Liu above. One of ordinary skill in the art would have been motivated to combine the methods of the reference claims in view of Wang with Liu in order to better understand regulatory dynamics during reprogramming, including early-stage gene expression dynamics, as shown by Liu (pg. 241, para. 6; pg. 244, para. 5 to pg. 245, para. 1), given the reference claims also models cell dynamics in cell reprogramming (reference claim 1). This modification would have had a reasonable expectation of success given one of ordinary skill in the art could apply the method of Liu to explore regulatory dynamics in the gene regulatory or protein interaction network of the reference claims in view of Wang.
Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 10,672,501 in view of Wang (2015), as applied to claim 1 above, and further in view of Mohammadi (2019).
Cited reference: Mohammadi et al., Reconstruction of cell-type specific interactomes at single-cell resolution, 2019, Cell Syst. 9(6), pg. 559-568.
Regarding instant claim 5, the reference claims in view of Wang disclose the method of claim 1 as applied above.
Further regarding instant claim 5, the reference claims in view of Wang as applied to claim 1 above do not disclose extracting a subgraph from the graph and forming the adjacency matrix from the subgraph, where the subgraph represents a specific cell type.
However, Mohammadi discloses a method of constructing cell-type specific interactomes of genes (Abstract; pg. 8, para. 4, e.g. nodes are genes), which comprises obtaining single-cell RNA sequencing data of a given cell type, using the single-cell RNA sequencing data to estimate the distribution and variability of interaction strengths within the given cell types, and constructing a subnetwork based on the estimated strengths (Figure 1 A-C; pg. 4, para. 3 to pg. 5, para. 1), wherein the resulting cell-type specific network have between 34.4% to 54.6% of the genes of the original reference network (i.e. the cell-type specific network is a subnetwork) (pg. 5, para. 3). Mohammadi further discloses representing the cell-type specific network as an adjacency matrix, and using the adjacency matrix as a transition matrix (pg. 14, para. 3). Mohammadi discloses these cell-specific networks enable the distinction of topologically-specific genes whose interactions are highly cell-type-specific, and can be used to study the relationship between cell-type specific hubness and the context-specific role of corresponding genes (pg. 3, para. 3; pg. 4, para. 2).
It would have been prima facie obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of the reference claims in view of Wang as applied to claim 1 to have extracted a subgraph representing a specific cell type from the graph and form the adjacency matrix from the subgraph, as shown by Mohammadi above. One of ordinary skill in the art would have been motivated to combine the methods of the reference claims in view of Wang with Mohammadi in order to provide an adjacency matrix that represents topologically-specific genes with highly cell-type specific interactions, (pg. 3, para. 3; pg. 4, para. 2), given the reference claims in view of Wang use networks of cell dynamics to model a particular cell type or cell reprogramming. This modification would have had a reasonable expectation of success given both the reference claims in view of Wang and Mohammadi represent an interaction network by an adjacency matrix and use the adjacency matrix as a state-transition matrix.
Conclusion
No claims are allowed.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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