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 .
Claim Status
Claims 1-20 are currently pending and examined on the merits.
Priority
The instant application claims foreign priority to European Application EP21306894.3 filed on 12/21/2021. At this point in examination, the effective filing date of claims 1-20 is 12/21/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/21/2022 are in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Specification
Pg. 15, para. 3, last line of the instant specification contains a hyperlink. The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
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 1-20 are patent eligible under 35 U.S.C. 101 because the claimed invention is not directed to an abstract idea without significantly more.
Eligibility Step 1: Claims 1-8 are directed to a method (process) for training a neural network for inferring a gene expression profile. Claims 9-14 are directed to a non-transitory computer-readable medium (machine). Claims 15-20 are directed to a non-transitory computer-readable storage medium (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-20 do not recite any steps that fall within the mental processes and/or mathematical concepts groups of abstract ideas, or law of nature/natural phenomenon. Independent claims 1, 9, and 15 recite additional elements such as obtaining a matrix of potential regulations between genes, obtaining a neural network having an input layer of nodes and an output layer of nodes, adding connections to the neural network from the nodes of the input layer to the nodes of the output layer, training the neural network, and removing connections of the trained neural network. Limitations such as adding and removing connections to the neural network affect the data structure of the neural network itself, therefore these steps cannot take place in the mind. Dependent claims 2-8, 10-14, and 16-20 recite information further limiting the additional elements indicated above.
[Step 2A, Prong One: NO]
Therefore, claims 1-20 are patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (Scientifica, 2016, 1-14), in view of Sun et al. [US10832123B2].
With respect to claims 1, 9, and 15:
Regarding the recited obtaining a matrix of potential regulations between genes of a set of genes of a sequence of reference genome, the matrix of potential regulations describing connections between regulator genes and regulated genes, a regulator gene encoding at least one transcription factor regulating at least one regulated gene, a connection representing at least one observed regulation of the regulated gene by the regulator gene in at least one time series of an observed biological process involving the genes of the set of genes of the sequence of the reference genome, Khan et al. discloses obtaining candidate gene regulatory networks (GRN) represented as a directed graph, where V denotes the set of all nodes as genes and E is the set of all edges as the interaction between a pair of genes. An edge
e
i
,
j
present in the set E demonstrates that gene j regulates gene i. This directed graph can also be represented by an adjacency matrix
G
=
g
i
,
j
N
×
N
, where N is the number of nodes or genes in the graph and the element
g
i
,
j
is 0 or 1 depending on the absence or presence of a directed edge from node j to node i, respectively (pg. 5, col. 1-2, para. 4). The proposed method for reverse engineering these GRNs from temporal genetic expression profiles was employed to identify causal relationships among genes from an in vivo dataset of eight genes involved in the SOS DNA repair mechanism of E. coli (pg. 8, col. 2, para. 2, lines 1-13, Fig. 5). This teaches obtaining a matrix of gene regulations between regulator genes that encode transcription factors and regulated genes from a set of genes, where the gene regulations are observed over time.
Regarding the recited obtaining a neural network having an input layer of nodes and an output layer of nodes, the input layer and the output layer having an equivalent node for representing each gene of the set of genes of the sequence of the reference genome, each node of the input layer representing a regulator gene and each node of the output layer representing a regulated gene, Khan et al. discloses taking the candidate gene regulatory networks, which can be represented as adjacency matrices, to formalize a recurrent neural network (RNN) (pg. 5, col. 2, para. 3, lines 1-6). Also, further discloses a representation of a GRN by an RNN model, where each node symbolizes a particular gene and the edges between the nodes represent the regulatory interactions among the genes (pg. 3, col. 2, para. 1, lines 1-12, Fig. 2). This teaches a neural network with an input layer of nodes representing a regulator gene and an output layer of nodes representing a regulated gene, as depicted in Figure 2.
Regarding the recited adding connections to the neural network from the nodes of the input layer to the nodes of the output layer, the added connections being extracted from the obtained matrix of potential regulations, Khan et al. discloses taking the candidate gene regulatory networks, which can be represented as adjacency matrices, to formalize a recurrent neural network (RNN) and initializing the weight matrix of the RNN formalism based on the GRNs (pg. 5, col. 2, para. 3, lines 1-6). Also, further discloses a representation of a GRN by an RNN model, where each node symbolizes a particular gene and the edges between the nodes represent the regulatory interactions among the genes (pg. 3, col. 2, para. 1, lines 1-12, Fig. 2). This teaches that initializing the weights for the RNN is adding the connections from the matrix of regulations to the neural network and forming them between the nodes of the input layer and the nodes of the output layer.
Regarding the recited training the neural network by using a set of gene expression profiles of the observed biological process, each connection of the trained the neural network being weighted, Khan et al. discloses training the RNN model parameters, such as the magnitude of the weight
w
i
j
indicating the strength of an interaction or regulatory effect between two nodes, using the statistical BAPSO methodology on in vivo time series genetic expression datasets (pg. 4, col. 1, para. 1, lines 19-21; pg. 5, col. 2, para. 3, lines 6-9; pg. 7, col. 2, para. 2, lines 1-11). This teaches training the neural network using gene expression profiles, where each connection in the neural network is being weighted.
Khan et al. does not disclose removing connections of the trained neural network having an insignificant weight value.
However, Sun et al. discloses pruning connections of an artificial neural network where if the weight of a connection is smaller than a threshold, said connection is insignificant (pg. 21, col. 5, lines 54-62). This teaches removing connections of a neural network having an insignificant weight value.
It would have been prima facie obvious to one of ordinary skill in the art to combine the neural network of gene regulations disclosed by Khan et al. with removing insignificant edges disclosed by Sun et al. One would be motivated to combine the neural network with the removal step because the compression method for deep neural networks proposed by Sun et al. improves accuracy of the compressed neural networks by a modified fine-tuning process and an additional step of retraining the network without mask (pg. 26, col. 15-16, lines 61-67). This means that pruning insignificant connections will be highly accurate in the neural network of gene regulations. There is a likelihood of success, since these teachings utilize neural networks for either reconstructing gene regulatory networks or compressing dense neural networks into sparse neural networks, which are well known techniques in the field of computer science.
Claim 9 recites a non-transitory computer readable medium. Claim 15 recites a non-transitory computer readable storage medium.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
With respect to claims 2, 10, and 16:
Khan et al. does not disclose wherein the removing the connections of the trained neural network having an insignificant weight includes performing for each connection of the trained neural network: obtaining a value of a threshold of insignificance representing a modification of an expression of the regulated gene in a range of an experimental error.
However, Sun et al. discloses pruning connections of artificial neural networks (ANNs) where if the weight of a connection is smaller than a threshold, said connection is insignificant (pg. 21, col. 5, lines 54-62). Also, further discloses that ANNs have been widely applied in various fields, such as gene expression (pg. 19, col. 1, lines 31-35). This teaches a threshold for removing connections of a neural network having an insignificant weight.
Khan et al. does not disclose wherein the removing the connections of the trained neural network having an insignificant weight includes performing for each connection of the trained neural network: removing the connection to the regulated gene if the weight value is smaller than the threshold of insignificance.
However, Sun et al. discloses pruning connections of an artificial neural network where if the weight of a connection is smaller than a threshold, said connection is insignificant (pg. 21, col. 5, lines 54-62). Also, further discloses that ANNs have been widely applied in various fields, such as gene expression (pg. 19, col. 1, lines 31-35). This teaches removing connections of a neural network if the weight of the connection is smaller than a threshold.
With respect to claims 3-8, 11-14, and 17-20:
Claims 3, 11, and 17 recite wherein the obtained matrix of potential regulations between genes of a set of genes of a sequence of reference genome has been computed by: identifying, for each gene of the set of genes of the sequence of the reference genome, one or more transcription factor binding sites and the respective transcription factor or factors bound on the one or more transcription factor binding sites; and for each identified bound transcription factor: identifying one or more potentially regulated genes; identifying a potentially regulator gene encoding the bound transcription factor; and connecting the regulator gene and the one or more regulated genes. Claims 4, 12, and 18 recite wherein the identifying one or more potentially regulated genes further comprises: determining, from a gene location map of the genes of the set of genes of the sequence of the reference genome, if one or more genes are in a frame of a predetermined number of base pairs around the identified bound transcription factor; and identifying the one or more genes are in the frame of a predetermined number of base pairs around the identified bound transcription factor as potentially regulated genes. Claim 5 recites wherein the predetermined number of base pairs is smaller than 15000. Claims 6 and 13 recite wherein the identifying, for each gene of the set of genes of the sequence of the reference genome, one or more transcription factor binding sites further comprises: performing a peak calling operation on chromatin accessibility data of the set of genes of the sequence of the reference genome, thereby identifying peaks; identifying one or more hollows for each identified peak, thereby obtaining footprints of a past presence of transcription factor on the chromatin accessibility data of the set of genes of the sequence of the reference genome; comparing the obtained footprints to motifs of known transcription factors; and identifying, as a result of the comparing, which transcriptions factor has been bound to each footprint. Claims 7, 14, and 19 recite wherein the obtained matrix of potential regulations between genes of a set of genes of a sequence of reference genome has been computed by: obtaining a matrix of potential regulations for each time series of the observed biological process, thereby obtaining a set of matrices of potential regulations; and merging the matrix of potential regulations of the set of matrices of potential regulations. Claims 8 and 20 recite wherein a connection described for each time series of the observed biological process is equivalent to a connection described for one of the time series of the observed biological process.
Khan et al. discloses obtaining candidate gene regulatory networks (GRN) represented as a directed graph, where V denotes the set of all nodes as genes and E is the set of all edges as the interaction between a pair of genes. An edge
e
i
,
j
present in the set E demonstrates that gene j regulates gene i. This directed graph can also be represented by an adjacency matrix
G
=
g
i
,
j
N
×
N
, where N is the number of nodes or genes in the graph and the element
g
i
,
j
is 0 or 1 depending on the absence or presence of a directed edge from node j to node i, respectively (pg. 5, col. 1-2, para. 4). The proposed method for reverse engineering these GRNs from temporal genetic expression profiles was employed to identify causal relationships among genes from an in vivo dataset of eight genes involved in the SOS DNA repair mechanism of E. coli (pg. 8, col. 2, para. 2, lines 1-13, Fig. 5). This teaches obtaining a matrix of gene regulations between regulator genes that encode transcription factors and regulated genes from a set of genes, where the gene regulations are observed over time. Claims 3-8, 11-14, and 17-20 are product-by-process claims. Because the claims recite steps on obtaining the matrix of potential regulations that have been previously computed, only the resulting matrix product is eligible for patentability. Therefore, the matrix recited in these claims are taught by Khan et al. because Khan et al. discloses obtaining a matrix of gene regulations between genes of a set of genes. "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985). See MPEP 2113.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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