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 .
DETAILED ACTION
2. This office action is in response to the original filing of 06/01/2023. Claim 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 112
3. 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 2, 9, and 15 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 2, 9, and 15 recite “generate the second graph based on information from the protein ontology.”
However, the term “the protein ontology” lacks proper antecedent basis in the claim. The claim does not introduce or define a separate element identified as a “protein ontology.” The claim therefore introduces a new term, “protein ontology,” without previously establishing an antecedent relationship between the claimed second graph and a protein ontology.
It is unclear whether “the protein ontology” in claims 2, 9, and 15 refer to:
1. the previously recited “protein function ontology” of the claims;
2. a separate protein ontology that was not previously introduced; or
3. another ontology containing information related to protein functions.
Because the claim does not provide a clear antecedent basis for “the protein ontology,” the scope of claims 2, 9, and 15 is uncertain. A person of ordinary skill in the art would not be able to determine with reasonable certainty whether the claimed second graph is generated from the same protein function ontology previously recited in the respective independent claims 1, 8 and 14 or from a different ontology.
Claim Rejections - 35 USC § 101
4. 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Claim 1:
Step 1: The claim is directed to a method, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “ identifying associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology”; generating, a composite graph by merging the first graph and the second graph using the associations of nodes”; “adding node embeddings for nodes of the composite graph”; “determining at least one new association of unassociated nodes of the composite graph using the node embeddings” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements:
“by the processor set” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
“saving the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Step 2B: The claim does not contain significantly more than the judicial exception.
“by the processor set” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
“saving the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 8:
Step 1: The claim is directed to a product, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “identify associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology”; “generate a composite graph by merging the first graph and the second graph using the associations of nodes”; “perform node embedding including node features of biological sequences for nodes of the composite graph”; “determine at least one new association of unassociated nodes of the composite graph using the node embeddings” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements:
“one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Step 2B: The claim does not contain significantly more than the judicial exception.
“one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 14:
Step 1: The claim is directed to a system, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “identify associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology”; “generate a composite graph by merging the first graph and the second graph using the associations of nodes”; “perform node embedding for nodes of the composite graph”; “build a graph neural network from the composite graph with the node embeddings”; “perform link prediction using the graph neural network to identify at least one new association of unassociated nodes of the composite graph” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements:
“a processor”, “a computer readable memory” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Step 2B: The claim does not contain significantly more than the judicial exception.
“a processor”, “a computer readable memory” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 2 recites “generating the first graph based on information from the gene ontology; and generating the second graph based on information from the protein ontology” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 3 recites “generating the node embeddings for the nodes of the composite graph that preserves a network neighborhood of the nodes of the composite graph” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 4 recites “wherein the generating comprises applying a shallow network embedding technique that employs a skip-gram model on generated random walks of the composite graph.” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 5 recites “comprising ranking associations of the at least one new association of unassociated nodes of the composite graph using the node embeddings” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 6 recites “wherein the ranking comprises determining a distance measure between the nodes of the at least one new association based on the node embeddings” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 7 recites “wherein the node embeddings comprise latent multi-dimensional embeddings generated for the nodes of the composite graph by applying a shallow network embedding technique that preserves a network neighborhood of the nodes of the composite graph” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 9 recites “wherein the program instructions are further executable to: generate the first graph based on information from the gene ontology; and generate the second graph based on information from the protein ontology” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 10 recites “wherein the program instructions are further executable to select the biological sequences for the nodes of the composite graph” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 11 recites “wherein the selecting comprises: mining annotated sequence ontologies for the biological sequences associated with terms from the nodes of the composite graph; and performing multiple sequence alignment to identify the biological sequences for the nodes of the composite graph” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 12 recites “wherein the node embeddings comprise: low-dimensional vectors that represent the graph nodes and edges in a vectorial space; and context vectors that are latent representations of DNA sequences” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 13 recites “wherein the program instructions are further executable to rank associations of the at least one new association of unassociated nodes of the composite graph using the node embeddings” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 15 recites “wherein the program instructions are further executable to: generate the first graph based on information from the gene ontology; and generate the second graph based on information from the protein ontology” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 16 recites “wherein the node embeddings comprise: low-dimensional vectors that represent the graph nodes and edges in a vectorial space; and context vectors that are latent representations of DNA sequences” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 17 recites “obtain annotated biological sequences belonging to nodes of the composite graph; perform multiple sequence alignment to identify the biological sequences for the nodes of the composite graph; and select representative biological sequences for nodes of the composite graph” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 18 recites “applying a sequence-to-sequence technique that encodes a DNA sequence as a context vector; and including the context vector in a node embedding for a node of the composite graph” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 19 recites “translating the composite graph to an adjacency matrix; translating the node embeddings to a node attribute matrix; and outputting a matrix of probabilities that two nodes of the composite graph are associated” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 20 recites “wherein the program instructions are further executable to train the graph neural network using the composite graph and the associations of nodes to perform the link prediction” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim Rejections - 35 USC § 103
5. 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.
Claim(s) 1-9 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ashburner et al. Gene Ontology: tool for the unification of biology (2000) in view of Grover et al. Node2Vec: Scalable Feature Learning for Networks (2016).
Claim 1. Ashburner discloses a method, comprising:
identifying, by a processor set, associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology (Gene Ontology as comprising three ontologies: biological process, molecular function and cellular component p. 25 and fig. 1 illustrates ontology terms connected by relationships) [protein function ontologies were well known and compatible with GO's molecular-function branch. Employing a second ontology describing protein functions would have been an obvious extension of the GO framework for integrating complementary biological knowledge];
generating, by the processor set, a composite graph by merging the first graph and the second graph using the associations of nodes (GO was developed to unify biology through shared ontology structures) (p. 25);
Ashburner does not explicitly disclose adding, by the processor set, node embeddings for nodes of the composite graph; determining, by the processor set, at least one new association of unassociated nodes of the composite graph using the node embeddings; and saving, by the processor set, the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage.
However, Grover discloses
adding, by the processor set, node embeddings for nodes of the composite graph (Section 3 introduces feature learning for graph nodes…Section 3.1 defines node embeddings. Equation (1) optimization objective. Section 3.2 biased random walks generate node contexts.. Figure 2 embedding workflow);
determining, by the processor set, at least one new association of unassociated nodes of the composite graph using the node embeddings (Section 4.3 uses node embeddings to predict missing graph edges…Section 4.7 Link prediction evaluation…Table 3 reports link prediction performance); and
saving, by the processor set, the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage (Section 3 embeddings are stored after optimization…Section 4: Predicted links are output for downstream analysis). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ashburner further in view of Grover to incorporate the above cited feature. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 2. Ashburner and Grover disclose the method of claim 1, Ashburner further discloses comprising: generating the first graph based on information from the gene ontology (biological-process ontology); and generating the second graph based on information from the protein ontology (molecular-function (protein function) ontology) (abstract p. 25).
Claim 3. Ashburner and Grover disclose the method of claim 1, Grover further discloses comprising generating the node embeddings for the nodes of the composite graph that preserves a network neighborhood of the nodes of the composite graph (Section 3.1 defines the objective of node2vec as learning feature representations that preserve network neighborhoods…Equation 1 defines the optimization objective maximizing the probability of preserving neighboring nodes...Section 3.2 describes biased random walks that preserve network neighborhoods). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 4. Ashburner and Grover disclose the method of claim 3, Grover further discloses wherein the generating comprises applying a shallow network embedding technique that employs a skip-gram model on generated random walks of the composite graph (Section 3.2 describes performing biased random walks over graph nodes… Algorithm 1 generates random walks…. Section 3.3 states that the Skip-Gram architecture is applied to the generated walks. Equation (2) defines the Skip-Gram optimization). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 5. Ashburner and Grover disclose the method of claim 1, Grover further discloses comprising ranking associations of the at least one new association of unassociated nodes of the composite graph using the node embeddings (Section 4.3 evaluates link prediction and Predicted node pairs are ranked according to embedding similarity….Table 3 reports ranking performance). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 6. Ashburner and Grover disclose the method of claim 5, Grover further discloses wherein the ranking comprises determining a distance measure between the nodes of the at least one new association based on the node embeddings (Section 4.3 computes similarity between learned embeddings and discusses applying operators to embedding vectors for edge prediction…Table 3 evaluates prediction accuracy based upon embedding similarity) [vector inner product between embedding vectors represents well-known equivalent approaches for ranking graph relationships]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 7. Ashburner and Grover disclose the method of claim 1, Grover further discloses wherein the node embeddings comprise latent multi-dimensional embeddings generated for the nodes of the composite graph by applying a shallow network embedding technique that preserves a network neighborhood of the nodes of the composite graph (Section 3.1 defines latent embedding vectors…Equation (1) optimization objective…Section 3.2 network neighborhood preservation…Section 3.3 Skip-Gram optimization). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claims 8-9 are similar in scope as claims 1-2, respectively; therefore, they are rejected under the same rationale.
Claim 12. Ashburner and Grover disclose the computer program product of claim 8, Grover further discloses wherein the node embeddings comprise: low-dimensional vectors that represent the graph nodes and edges in a vectorial space; and context vectors that are latent representations of DNA sequences (Section 3 node2vec teaches learning low-dimensional feature representations…Section 3.1 The node embeddings represent graph structure in a continuous vector space…Equation (1) Defines embedding optimization). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 13. Ashburner and Grover disclose the computer program product of claim 8, Grover further discloses wherein the program instructions are further executable to rank associations of the at least one new association of unassociated nodes of the composite graph using the node embeddings (Section 4.3 Grover uses learned embeddings to rank candidate links and Node pairs are evaluated based on embedding-based similarity…Table 3 Reports link prediction ranking performance). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner
6. Claim(s) 10-11 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ashburner et al. Gene Ontology: tool for the unification of biology (2000) in view of Grover et al. Node2Vec: Scalable Feature Learning for Networks (2016) and further in view of Hamilton et al. Inductive Representation Learning on Large Graphs (2017).
Claim 10. Ashburner and Grover disclose the computer program product of claim 8, but fails to explicitly disclose wherein the program instructions are further executable to select the biological sequences for the nodes of the composite graph.
However, Hamilton discloses wherein the program instructions are further executable to select the biological sequences for the nodes of the composite graph (Section 3: GraphSAGE incorporates node attributes/features into graph embeddings…Node features may represent characteristics associated with each node) [Selecting biological sequences as node attributes represents applying known biological information as node features. Biological sequences are conventional node attributes in biological knowledge graphs]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 11. Ashburner Grover and Hamilton disclose the computer program product of claim 10, Grover further discloses wherein the selecting comprises: mining annotated sequence ontologies for the biological sequences associated with terms from the nodes of the composite graph; and performing multiple sequence alignment to identify the biological sequences for the nodes of the composite graph (associating biological annotations with ontology terms. Gene products are linked to ontology concepts through annotation relationships) (pp. 26-27) [Once ontology terms are associated with biological entities, retrieving annotated biological sequences corresponding to those entities is an expected implementation]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner
Claim 14. Supra Claim1 and Hamilton further discloses build a graph neural network from the composite graph with the node embeddings (Section 3 — GraphSAGE teaches a graph neural network framework that: receives graph structure; aggregates neighboring node information, and generates node embeddings…Figure 1 Illustrates the GraphSAGE architecture…Equation (1) Defines the aggregation process). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.; and Grover perform link prediction using the graph neural network to identify at least one new association of unassociated nodes of the composite graph (Section 4.3 - Link Prediction node2vec evaluates the ability of embeddings to predict missing graph edges). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 15. Ashburner Grover and Hamilton disclose the system of claim 14, Ashburner further discloses wherein the program instructions are further executable to: generate the first graph based on information from the gene ontology (biological-process ontology); and generate the second graph based on information from the protein ontology (molecular-function (protein function) ontology) (abstract p. 25).
Claim 16. Ashburner Grover and Hamilton disclose the system of claim 14, Grover further discloses wherein the node embeddings comprise: low-dimensional vectors that represent the graph nodes and edges in a vectorial space; and context vectors that are latent representations of DNA sequences (Section 3 node2vec teaches learning low-dimensional feature representations…Section 3.1 The node embeddings represent graph structure in a continuous vector space…Equation (1) Defines embedding optimization). One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 17. Ashburner Grover and Hamilton disclose the system of claim 14, Grover further discloses wherein the program instructions are further executable to: obtain annotated biological sequences belonging to nodes of the composite graph; perform multiple sequence alignment to identify the biological sequences for the nodes of the composite graph (associating biological annotations with ontology terms. Gene products are linked to ontology concepts through annotation relationships) (pp. 26-27) [Once ontology terms are associated with biological entities, retrieving annotated biological sequences corresponding to those entities is an expected implementation]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner; and Hamilton further discloses select representative biological sequences for nodes of the composite graph (Section 3: GraphSAGE incorporates node attributes/features into graph embeddings…Node features may represent characteristics associated with each node) [Selecting biological sequences as node attributes represents applying known biological information as node features. Biological sequences are conventional node attributes in biological knowledge graphs]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 18. Ashburner Grover and Hamilton disclose the system of claim 14, Hamilton further discloses wherein the performing comprises: applying a sequence-to-sequence technique that encodes a DNA sequence as a context vector; and including the context vector in a node embedding for a node of the composite graph (Section 3 GraphSAGE teaches combining node attributes with graph structural information to generate node embeddings). Grover Section 3 node2vec teaches embedding input information into vector representations. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 19. Ashburner Grover and Hamilton disclose the system of claim 14, Hamilton further discloses wherein the building comprises: translating the composite graph to an adjacency matrix; translating the node embeddings to a node attribute matrix; and outputting a matrix of probabilities that two nodes of the composite graph are associated (Section 3 GraphSAGE operates on graph structures represented by node relationships and attributes…The graph structure corresponds to adjacency information…Equations (1)-(3) Use: graph neighborhoods; node features; and learned representations) [Representing graphs as adjacency matrices and node embeddings as feature matrices is a standard mathematical representation of graph neural network input. Generating probabilities for node relationships corresponds to graph neural network link prediction]. One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Claim 20. Ashburner Grover and Hamilton disclose the system of claim 14, Hamilton further discloses wherein the program instructions are further executable to train the graph neural network using the composite graph and the associations of nodes to perform the link prediction (Section 3 GraphSAGE trains aggregation functions using graph structures and node information. Section 4 Evaluates trained representations for prediction tasks.) One would have been motivated to do so in order to improve automated biological knowledge discovery by enabling prediction of previously unknown semantic relationships while preserving the graph structure described by Ashburner.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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