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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 21, 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign mentioned in the description: 220 (mentioned in paragraph [0034]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) 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. 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.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The disclosure is objected to because of the following informalities:
[0003]: "receiving, at a definition module an input" should read "receiving, at a definition module, an input"; "embedding of the nodes of the graph neural network, a multi-relational graph" should read "embedding by the nodes of the graph neural network, a multi-relational graph"; "a first updated multi-relation graph" should read "a first updated multi-relational graph"
[0008]: "a different semantics" should read "a different semantic"
[0013]: "a set of optional parameters/function" should read "a set of optional parameters/functions"
[0014]: "the hierarchy/ontology/taxonomy that r and R" should read "the hierarchy/ontology/taxonomy that r and R belong to"
[0019]: "Computing environment" should read "computing environment"
[0034]: "FIG. 2" should read "FIG. 2A"; "Shown in environment 211 is computer system 200" should read "Shown in environment 210 is computer system 211"; "Shown operational on multi-relational graph generation engine 210" should read "Shown operational on multi-relational graph generation engine 200"
[0036]: "definition module 212 identify" should read "definition module 212 identifies"
[0037]: "relation in learned" should read "relation is learned"; "GNN205" should read "GNN 205"
[0038]: "Attention score monitor 214" should read "Attention score monitor 214a"; "aggregation strategy Attention" should read "aggregation strategy. Attention"
[0040]: "relation generalization module 206" should read "relation generalization module 216"; "monitoring module 204" should read "monitor module 214"
[0041]: "relation generalization module 206" should read "relation generalization module 216"
[0042]: "At Step 304" should read "at step 304"; "generate embedding or embeddings. or vectorizing" should read "generate an embedding or embeddings, or vectorize"; "At step 310 generate" should read "At step 310, generate"; "At step 312 generating" should read "At step 312, generate"; "At step 314, Select (e.g., infers or determine)" should read "At step 314, select (e.g., infer or determine)"
Appropriate correction is required.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1: "receiving, at a definition module an input" should read "receiving, at a definition module, an input";
"model type of the graph neural network, a hierarchy/ontology/taxonomy of relations or entities" should read "model type of the graph neural network, and a hierarchy/ontology/taxonomy of relations or entities";
"identifying. by the definition module one" should read "identifying, by the definition module, one";
“monitoring, by the monitoring module” should read “monitoring, by a monitoring module”;
"a first updated multi-relation graph" should read "a first updated multi-relational graph"
Claim 2: "computer implemented" should read "computer-implemented"; “the updated multi-relational graph” should read “the first updated multi-relational graph”; “second updated multi-relation graph” should read “second updated multi-relational graph”
Claim 3: "computer implemented" should read "computer-implemented"
Claim 4: "computer implemented" should read "computer-implemented";
"claim 3., wherein" should read "claim 3, wherein"
Claim 5: "computer implemented" should read "computer-implemented"
Claim 7: "computer implemented" should read "computer-implemented";
"and function" should read "and functions"
Claim 8: "receiving, at a definition module an input" should read "receiving, at a definition module, an input";
"model type of the graph neural network, a hierarchy/ontology/taxonomy of relations or entities" should read "model type of the graph neural network, and a hierarchy/ontology/taxonomy of relations or entities";
"identifying. by the definition module one" should read "identifying, by the definition module, one";
“monitoring, by the monitoring module” should read “monitoring, by a monitoring module”;
"a first updated multi-relation graph" should read "a first updated multi-relational graph"
Claim 9: “functions comprising,” should read “functions comprising:”; “the updated multi-relational graph” should read “the first updated multi-relational graph”; “where the second” should read “wherein the second”; “second updated multi-relation graph” should read “second updated multi-relational graph”
Claim 14: "and function" should read "and functions"
Claim 15: "receiving, at a definition module an input" should read "receiving, at a definition module, an input";
"model type of the graph neural network, a hierarchy/ontology/taxonomy of relations or entities" should read "model type of the graph neural network, and a hierarchy/ontology/taxonomy of relations or entities";
"identifying. by the definition module one" should read "identifying, by the definition module, one";
“monitoring, by the monitoring module” should read “monitoring, by a monitoring module”;
"a first updated multi-relation graph" should read "a first updated multi-relational graph"
Claim 17: "The computer program product of claim 12" should read "The computer program product of claim 15"
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-20 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.
The term “more general” in claims 1, 2, 8, 9, 15, and 16 is a relative term which renders the claims indefinite. The term “more general” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear as to what the criterion for generality is, or what the relation is “more general” than. The limitations of “generating, by a relation generalization module, a more general relation for each relation below a ranking threshold” in claims 1, 8, and 15, and “generating, by a relation generalization module, a second more general relation for each relation below a ranking threshold” in claims 2, 9, and 16 are rendered indefinite by the use of the term “more general.” Claims 3-7, 10-14, and 17-20 are rejected due to dependency on a rejected base claim.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a computer program product comprising a computer readable storage device, which encompasses signals per se. Examiner recommends amending claim 15 to recite a computer program product comprising “a computer readable storage medium,” given that paragraph [0018] of the specification defines “computer readable storage medium” as excluding signals per se, in order to overcome this rejection.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim recites a method, and therefore is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
“identifying… one or more layers of interest in the graph neural network and a plurality of parameters to monitor, based on the model architecture and model type”; This limitation encompasses mentally identifying one or more layers of interest in the graph neural network and a plurality of parameters to monitor, based on the model architecture and model type.
“embedding… a multi-relational graph, wherein the multi-relational graph comprises a plurality of entities and relations between entities”; This limitation encompasses mentally embedding a multi-relational graph comprising a plurality of entities and relations between entities, such as by mentally generating a vector that represents the multi-relational graph.
“monitoring… the one or more layers of interest and the plurality of parameters of the graph neural network embedding a multi-relational graph”; This limitation encompasses mentally monitoring the one or more layers of interest and the plurality of parameters of the graph neural network embedding a multi-relational graph, such as by observing changes to the layers of interest and the plurality of parameters.
“generating… a comprehension score for each of a plurality of relations in the multi-relational graph, based on the monitoring”; This limitation encompasses mentally generating a comprehension score for each of a plurality of relations in the multi-relational graph, based on the monitoring, such as by mentally calculating a score for each relation based on monitored values of the layers of interest/parameters.
“generating… a ranked relation list, based on the comprehension score”; This limitation encompasses mentally generating a ranked relation list based on the comprehension score, such as by mentally sorting the relations from highest to lowest comprehension score.
“generating… a more general relation for each relation below a ranking threshold, wherein the more general relation is taken from the input hierarchy/ontology/taxonomy of relations or entities”; This limitation encompasses mentally generating a more general relation for each relation below a ranking threshold, such as by mentally selecting a more general relation from the input hierarchy/ontology/taxonomy corresponding to each relation.
“generating… a first updated multi-relation graph based on the generated generalized relation”; This limitation encompasses mentally generating a first updated multi-relation graph based on the generated generalized relation, such as by mentally replacing a relation from the original multi-relational graph with its corresponding generated generalized relation.
“embedding the first updated multi-relational graph…”; This limitation encompasses mentally embedding the first updated multi-relational graph based on the graph neural network, such as by mentally generating a vector that represents the first updated multi-relational graph
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving, at a definition module an input, wherein the input is a model architecture of a graph neural network, model type of the graph neural network, a hierarchy/ontology/taxonomy of relations or entities,” however, this limitation amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)). The claim further recites that the method is “computer-implemented,” the identifying step is performed by the “definition module,” the embedding steps are performed “by the graph neural network” or “based on the graph neural network,” the monitoring, generating a comprehension score, and generating a ranked relation list steps are performed by the “monitoring module,” and the generating a more general relation and generating a first updated multi-relation graph steps are performed by the “relation generalization module,” however these limitations all amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Otherwise, the analysis at this step mirrors that of Step 2A, Prong 2. As an ordered whole, the claim is directed to a mentally performable process of identifying layers of interest and parameters to monitor based on the model architecture and model type, embedding a multi-relational graph, monitoring the layers of interest and parameters, generating a comprehension score for each of a plurality of relations in the multi-relational graph based on the monitoring, generating a ranked relation list based on the comprehension score, generating a more general relation for each relation below a ranking threshold taken from the input hierarchy/ontology/taxonomy of relations, generating a first updated multi-relation graph based on the generated generalized relation, and embedding the first updated multi-relational graph based on the graph neural network. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“monitoring… the one or more layers of interest and the plurality of parameters of the graph neural network embedding the updated multi-relational graph”; This limitation encompasses mentally monitoring the one or more layers of interest and the plurality of parameters of the graph neural network embedding the updated multi-relational graph, such as by observing changes to the layers of interest and the plurality of parameters.
“generating… a second comprehension score for each of a plurality of relations in the updated multi-relational graph, based on the monitoring of the updated multi-relational graph”; This limitation encompasses mentally generating a second comprehension score for each of a plurality of relations in the updated multi-relational graph, based on the monitoring of the updated multi-relational graph, such as by mentally calculating a score for each relation based on monitored values of the layers of interest/parameters.
“generating… a second ranked relation list, based on the second comprehension score”; This limitation encompasses mentally generating a second ranked relation list based on the second comprehension score, such as by mentally sorting the relations from highest to lowest second comprehension score.
“generating… a second more general relation for each relation below a ranking threshold, where the second more general relation is taken from the input hierarchy/ontology/taxonomy of relations or entities”; This limitation encompasses mentally generating a second more general relation for each relation below a ranking threshold, such as by mentally selecting a second more general relation from the input hierarchy/ontology/taxonomy corresponding to each relation.
“generating… a second updated multi-relation graph based on the generated generalized relation from the second ranked relation list”; This limitation encompasses mentally generating a second updated multi-relation graph based on the generated generalized relation from the second ranked relation list, such as by mentally replacing a relation from the first updated multi-relational graph with its corresponding generated generalized relation from the second ranked relation list.
“embedding the second updated multi-relational graph…”; This limitation encompasses mentally embedding the second updated multi-relational graph based on the graph neural network, such as by mentally performing mathematical operations specified by the graph neural network to convert the second updated multi-relational graph into a vector representation.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the monitoring, generating a second comprehension score, and generating a second ranked relation list steps are performed by the “monitoring module,” the generating a second more general relation and generating a second updated multi-relation graph steps are performed by the “relation generalization module,” and the embedding step is “based on the graph neural network,” however these limitations all amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the graph neural network is a graph attention network,” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“wherein the monitored parameters are attention scores”; This limitation merely further limits the monitoring step of claim 1, which is still mentally performable when the monitored parameters are attention scores, as one can mentally monitor attention scores such as by observing changes to attention scores.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claims 1 and 3.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claims 1 and 3.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the graph neural network is a graph convolutional network,” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of Step 2A Prong 2 above.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
“wherein the monitored parameters are convolutional weights”; This limitation merely further limits the monitoring step of claim 1, which is still mentally performable when the monitored parameters are convolutional weights, as one can mentally monitor convolutional weights such as by observing changes to convolutional weights.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. No further additional elements are recited, see analysis of claims 1 and 5.
Step 2B: The claim does not contain significantly more than the judicial exception. No further additional elements are recited, see analysis of claims 1 and 5.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the input further comprises a set of optional parameters and function to configure the ranking and generalization of relations,” however this merely further limits the receiving an input limitation of claim 1, and still amounts to the insignificant extra-solution activity of mere data gathering (MPEP 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “input further comprises a set of optional parameters and function…” limitation, in addition to reciting insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
Claims 8-14
Step 1: The claims recite a computer system, and therefore are directed to the statutory category of machines.
Step 2A Prong 1: Claims 8-14 are computer system claims corresponding to method claims 1-7, thus claims 8-14 recite the same judicial exception as claims 1-7, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-7, respectively, except insofar as claims 8-14 further recite “one or more computer processors; one or more computer readable storage devices; and computer program instructions stored on the one or more computer readable storage devices, executable by the one or more computer processors to perform functions comprising: [the method],” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-7, respectively, except insofar as claims 8-14 further recite “one or more computer processors; one or more computer readable storage devices; and computer program instructions stored on the one or more computer readable storage devices, executable by the one or more computer processors to perform functions comprising: [the method],” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Claims 15-20
Step 1: The claims are directed to non-statutory subject matter, however for the purpose of the abstract idea rejection, Examiner will assume they are directed to the statutory category of articles of manufacture.
Step 2A Prong 1: Claims 15-20 are computer program product claims corresponding to method claims 1-6, thus claims 15-20 recite the same judicial exception as claims 1-6, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-6, respectively, except insofar as claims 15-20 further recite “a computer readable storage device having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a function, the function comprising: [the method],” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-6, respectively, except insofar as claims 15-20 further recite “a computer readable storage device having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a function, the function comprising: [the method],” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Improving Knowledge Graph Embeddings with Ontological Reasoning (Jain et al.) discloses predicting triples by a knowledge graph embedding model, determining whether the predicted triples are consistent with an ontology, and generating generalized triples of the inconsistent predicted triples to use as negative samples in the embedding model training.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GWYNEVERE A DETERDING whose telephone number is (571)272-7657. The examiner can normally be reached Mon-Fri. 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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