DETAILED ACTION
This action is in response to the Applicant Response filed 01 November 2023 for application 18/500,014 filed 01 November 2023.
Claim(s) 1-24 is/are pending.
Claim(s) 1-24 is/are rejected.
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 Objections
Claim(s) 3-4, 9-10, 15-16, 21-22 is/are objected to because of the following informalities:
Claim 3, line 2, based a relative node distance should read “based on a relative node distance”
Claim 9, line 2, based a relative node distance should read “based on a relative node distance”
Claim 15, line 2, based a relative node distance should read “based on a relative node distance”
Claim 21, line 2, based a relative node distance should read “based on a relative node distance”
Claims 4, 10, 16, 22 are objected to due to their dependence, either directly or indirectly, on claims 3, 9, 15, 21
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 6, 12, 18, 24 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.
Claim 6 recites … generate the position information … while failing to provide a proper antecedent basis for the phrase. Claim 1, from which claim 6 depends recites determining position information. It is suggested that the phrase in claim 6 be amended to recite “determine the position information.” Clarification or correction required.
Claim 12 recites … generating the position information … while failing to provide a proper antecedent basis for the phrase. Claim 7, from which claim 12 depends recites determining position information. It is suggested that the phrase in claim 12 be amended to recite “determining the position information.” Clarification or correction required.
Claim 18 recites … generate the position information … while failing to provide a proper antecedent basis for the phrase. Claim 13, from which claim 18 depends recites determining position information. It is suggested that the phrase in claim 18 be amended to recite “determine the position information.” Clarification or correction required.
Claim 24 recites … generating the position information … while failing to provide a proper antecedent basis for the phrase. Claim 19, from which claim 24 depends recites determining position information. It is suggested that the phrase in claim 24 be amended to recite “determining the position information.” Clarification or correction required.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-24 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 15, 22 of copending Application No. 18/330,253 in view of Paduraru et al. (US 2026/0178663 A1 – Generating Positional Encodings of Directed Graphs, hereinafter referred to as “Paduraru”) and/or Yan et al. (U.S. Pat. No. 12,572,793 B1 – Subroutine Neural Networks, hereinafter referred to as “Yan”) and/or Ren et al. (HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extractions, hereinafter referred to as “Ren”) and/or Huang et al. (GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation, hereinafter referred to as “Huang”). Although the claims at issue are not identical, they are not patentably distinct from each other, because, as noted in the table below, claims 1-24 of the instant application have similar limitations as recited in copending Application No. 18/330,253 (claims 1, 8, 15, 22).
This is a provisional nonstatutory double patenting rejection.
Application No. 18/500,014
Co-pending Appl. No 18/330,253
Claim 1
Claim 8
An apparatus of pre-processing for deep neural network compilation, comprising:
An apparatus, comprising:
at least one memory; and
a memory; and
at least one processor coupled to the at least one memory, the at least one processor configured to:
at least one processor coupled to the memory, the at least one processor configured:
receive a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges;
to receive a representation of an artificial neural network (ANN) model;
determine position information for each node of the ANN model;
… corresponding to the ANN model according to a learned distance metric function that maps ANN models to the embedding space based on structural similarity; and
generate an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node;
to generate an operator embedding to represent operators of the ANN model in an embedding space;
process, by a graph neural network (GNN), the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information; and
to process, by a graph neural network (GNN), the operator embedding, to generate a graph embedding corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
determine, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
to determine, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
Claim 2
in which the position information comprises a sinusoidal position embedding.
Claim 3
in which the sinusoidal position embedding is computed based a relative node distance to a root node.
Claim 4
in which the relative node distance comprises a shortest distance position or a longest distance position.
Claim 5
in which nodes having a same node type have different position information.
Claim 6
in which the at least one processor is further configured to generate the position information such that the position information is node permutation invariant.
Claim 7
Claim 1
A processor-implemented method of pre-processing for deep neural network compilation performed by at least one processor, the processor-implemented method comprising:
A processor-implemented method of pre-processing for deep neural network compilation, comprising:
receiving a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges;
receiving a representation of an artificial neural network (ANN) model;
determining position information for each node of the ANN model;
… corresponding to the ANN model according to a learned distance metric function that maps ANN models to the embedding space based on structural similarity; and
generating an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node;
generating an operator embedding to represent operators of the ANN model in an embedding space;
processing, by a graph neural network (GNN), the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information; and
processing, by a graph neural network (GNN), the operator embedding, to generate a graph embedding corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
determining, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
determining, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
Claim 8
in which the position information comprises a sinusoidal position embedding.
Claim 9
in which the sinusoidal position embedding is computed based a relative node distance to a root node.
Claim 10
in which the relative node distance comprises a shortest distance position or a longest distance position.
Claim 11
in which nodes having a same node type have different position information.
Claim 12
further comprising generating the position information such that the position information is node permutation invariant.
Claim 13
Claim 15
A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising:
A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising:
program code to receive a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges;
program code to receive a representation of an artificial neural network (ANN) model;
program code to determine position information for each node of the ANN model;
… corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
program code to generate an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node;
program code to generate an operator embedding to represent operators of the ANN model in an embedding space;
program code to process, by a graph neural network (GNN), the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information; and
program code to process, by a graph neural network (GNN), the operator embedding, to generate a graph embedding corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
program code to determine, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
program code to determine, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
Claim 14
in which the position information comprises a sinusoidal position embedding.
Claim 15
in which the sinusoidal position embedding is computed based a relative node distance to a root node.
Claim 16
in which the relative node distance comprises a shortest distance position or a longest distance position.
Claim 17
in which nodes having a same node type have different position information.
Claim 18
in which the program code further comprises program code to generate the position information such that the position information is node permutation invariant.
Claim 19
Claim 22
An apparatus of pre-processing for deep neural network compilation, comprising:
An apparatus, comprising:
means for receiving a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges;
means for receiving a representation of an artificial neural network (ANN) model;
means for determining position information for each node of the ANN model;
… corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
means for generating an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node;
means for generating an operator embedding to represent operators of the ANN model in an embedding space;
means for processing, by a graph neural network (GNN), the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information; and
means for processing, by a graph neural network (GNN), the operator embedding, to generate a graph embedding corresponding to the ANN model according to a learned distance function that maps ANN models to the embedding space based on structural similarity; and
means for determining, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
means for determining, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding.
Claim 20
in which the position information comprises a sinusoidal position embedding.
Claim 21
in which the sinusoidal position embedding is computed based a relative node distance to a root node.
Claim 22
in which the relative node distance comprises a shortest distance position or a longest distance position.
Claim 23
in which nodes having a same node type have different position information.
Claim 24
further comprising means for generating the position information such that the position information is node permutation invariant.
Regarding claim 2 (similarly 8, 14, 20), Application No. 18/330,253 teaches all of the limitations of claim 1 (7, 13, 19, respectively), as stated in the chart. However, Application 18/330,253 does not explicitly teach in which the position information comprises a sinusoidal position embedding.
Paduraru teaches in which the position information comprises a sinusoidal position embedding (Paduraru, [0086] – teaches positional encoding including sinusoidal position encoding; see also Paduraru, [0034]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Application No. 18/330,253 with the teachings of Paduraru in order to capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, as well as capture some notion of distance between nodes in the field of graph positional embeddings (Paduraru, [0044] – “A positional encoding system ... within the system ... processes at least a portion of the graph data ... to generate a respective positional encoding for each of the nodes in the graph. Generally, a positional encoding is an embedding that, e.g., has the same dimensionality as the node features in the graph data..., and that represents the position of each of the nodes within the graph... As will be described in more detail below, the system ... generates the positional encodings such that the positional encodings capture directedness in the graph. That is, modifying the direction of any given edge within the graph ... will generally result in a modification to at least one of the positional encodings for at least one of the nodes in the graph... In general the positional encodings will also capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, and may also capture some notion of distance between nodes.”).
Regarding claim 3 (similarly 9, 15, 21), Application No. 18/330,253 in view of Paduraru teaches all of the limitations of claim 2 (8, 14, 20, respectively), as stated above. Paduraru further teaches in which the sinusoidal position embedding is computed based a relative node distance to a root node (Paduraru, [0086] – teaches computing sinusoidal position encoding for a node based on the input position corresponding to the node; see also Paduraru, [0034]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Application No. 18/330,253 and Paduraru in order to compute positional embeddings to capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, as well as capture some notion of distance between nodes (Paduraru, [0044]).
Regarding claim 4 (similarly 10, 16, 22), Application No. 18/330,253 in view of Paduraru teaches all of the limitations of claim 3 (9, 15, 21, respectively), as stated above. However, Application No. 18/330,253 in view of Paduraru does not explicitly teach in which the relative node distance comprises a shortest distance position or a longest distance position.
Yan teaches in which the relative node distance comprises a shortest distance position or a longest distance position (Yan, col. 13:58-col. 14:39 - teaches a shortest path algorithm the determine position distance).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Application No. 18/330,253 in view of Paduraru with the teachings of Yan in order to enable the model to make full use of the order of the input sequence without relying on recurrence or convolutions in the field of graph positional embeddings (Yan, col. 6:1-18 – “In some implementations, the bitwise embedding layer ... combines, for each subroutine input element, the embedded representation of the subroutine input element with a positional embedding of the input position of the subroutine input element in the input order to generate a combined embedded representation of the subroutine input element. That is, each position in the input sequence has a corresponding embedding, and for each subroutine input element the bitwise embedding layer ... combines the embedded representation of the subroutine input element with the embedding of the subroutine input element's position in the input sequence... For example, the bitwise embedding layer ... can concatenates or determine a sum or an average of the embedded representation of the subroutine input element and the positional embedding. Such positional embeddings can enable the model to make full use of the order of the input sequence without relying on recurrence or convolutions.”).
Regarding claim 5 (similarly 11, 17, 23), Application No. 18/330,253 teaches all of the limitations of claim 1 (7, 13, 19, respectively), as stated in the chart. However, Application 18/330,253 does not explicitly teach in which nodes having a same node type have different position information.
Ren teaches in which nodes having a same node type have different position information (Ren, Fig. 1 – teaches an example graph to sequence that shows nodes of a same type with different position information).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Application No. 18/330,253 with the teachings of Ren in order to invertibly map between a graph and an alternating sequence of nodes and edges representing position information in the field of graph positional embeddings (Ren, section 1 – “We propose a general technique to invertibly map between an information graph and an alternating sequence (assuming a given graph traversal algorithm). Generating an alternating sequence is equivalent to generating the original information graph.”).
Regarding claim 6 (similarly 12, 18, 24), Application No. 18/330,253 teaches all of the limitations of claim 1 (7, 13, 19, respectively), as stated in the chart. However, Application 18/330,253 does not explicitly teach generate the position information such that the position information is node permutation invariant.
Huang teaches generate the position information such that the position information is node permutation invariant (Huang, section 1 – teaches permutation invariant graph generation [Meaning that the position information is permutation invariant]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Application No. 18/330,253 with the teachings of Huang in order to capture the important permutation invariant properties of graphs in the field of graph positional embeddings (Huang, section I – “Learning the distribution of discrete and combinatorial graph structures is a challenging task, which is also a necessary and fundamental step for further jointly modelling attributes ... and labels ... in semantic abundant graphs. Traditional methods for graph generation date back to random graph models..., which rely on hand-crafted stochastic generation processes and capture limited graph statistic properties. Recent deep graph generative models utilize the capacity of neural networks to learn graph structure distribution effectively. The prominent paradigms include variational autoencoder (VAE) based models [14]–[16], generative adversarial network (GAN) based models [17], [18], flow-based models..., and autoregressive models... Among them, autoregressive models achieve the most impressive generation quality on discrete graph structures. However, they rely on node generation orderings with high time complexity and fail to capture the important permutation invariant properties of graphs. The desired likelihood-based graph generative models should estimate invariant likelihood to all possible equivalent adjacency matrices of the same graph. To reach this goal, Niu et al. ... creatively integrate score-based generative models ... with graph neural networks to implicitly represent permutation-invariant distributions, but still suffer from generation quality and sampling speed.”).
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.
Claim(s) 1-24 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of determine position information for each node of the ANN model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of process ... the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determine ... a set of hyperparameters for the ANN model based on the graph embedding, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – apparatus, at least one memory, at least one processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – deep neural network, artificial neural network (ANN) model, graph neural network (GNN). The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
apparatus, at least one memory, at least one processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
deep neural network, artificial neural network (ANN) model, graph neural network (GNN) amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the apparatus of claim 1 but for the recitation of additional element(s) of in which the position information comprises a sinusoidal position embedding.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of in which the sinusoidal position embedding is computed based a relative node distance to a root node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating embedding values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 3 is applicable here since claim 4 carries out the apparatus of claim 3 but for the recitation of additional element(s) of in which the relative node distance comprises a shortest distance position or a longest distance position.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 5 carries out the apparatus of claim 1 but for the recitation of additional element(s) of in which nodes having a same node type have different position information.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of generate the position information such that the position information is node permutation invariant, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation.
The limitation of determining position information for each node of the ANN model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of processing ... the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining ... a set of hyperparameters for the ANN model based on the graph embedding, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – processor-implemented, at least one processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – deep neural network, artificial neural network (ANN) model, graph neural network (GNN). The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
processor-implemented, at least one processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
deep neural network, artificial neural network (ANN) model, graph neural network (GNN) amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 7 is applicable here since claim 8 carries out the method of claim 7 but for the recitation of additional element(s) of in which the position information comprises a sinusoidal position embedding.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation.
The limitation of in which the sinusoidal position embedding is computed based a relative node distance to a root node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating embedding values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 10 carries out the method of claim 9 but for the recitation of additional element(s) of in which the relative node distance comprises a shortest distance position or a longest distance position.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 7 is applicable here since claim 11 carries out the method of claim 7 but for the recitation of additional element(s) of in which nodes having a same node type have different position information.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method of pre-processing for deep neural network compilation.
The limitation of generating the position information such that the position information is node permutation invariant, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of determine position information for each node of the ANN model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of process ... the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determine ... a set of hyperparameters for the ANN model based on the graph embedding, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-readable medium, program code, processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – deep neural network, artificial neural network (ANN) model, graph neural network (GNN). The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-readable medium, program code, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
deep neural network, artificial neural network (ANN) model, graph neural network (GNN) amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 13 is applicable here since claim 14 carries out the computer-readable medium of claim 13 but for the recitation of additional element(s) of in which the position information comprises a sinusoidal position embedding.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of in which the sinusoidal position embedding is computed based a relative node distance to a root node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating embedding values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 16 carries out the computer-readable medium of claim 15 but for the recitation of additional element(s) of in which the relative node distance comprises a shortest distance position or a longest distance position.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 13 is applicable here since claim 17 carries out the computer-readable medium of claim 13 but for the recitation of additional element(s) of in which nodes having a same node type have different position information.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of generate the position information such that the position information is node permutation invariant, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of determining position information for each node of the ANN model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating an operator embedding to represent operators of the ANN model in an embedding space based on the position information for each node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of processing ... the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining ... a set of hyperparameters for the ANN model based on the graph embedding, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – apparatus. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – deep neural network, artificial neural network (ANN) model, graph neural network (GNN). The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving a representation of an artificial neural network (ANN) model, the ANN including multiple nodes coupled by edges, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
apparatus amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
deep neural network, artificial neural network (ANN) model, graph neural network (GNN) amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 20 carries out the apparatus of claim 19 but for the recitation of additional element(s) of in which the position information comprises a sinusoidal position embedding.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 21, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of in which the sinusoidal position embedding is computed based a relative node distance to a root node, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating embedding values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 22, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 21 is applicable here since claim 22 carries out the apparatus of claim 21 but for the recitation of additional element(s) of in which the relative node distance comprises a shortest distance position or a longest distance position.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 23, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 23 carries out the apparatus of claim 19 but for the recitation of additional element(s) of in which nodes having a same node type have different position information.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 24, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 24 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus of pre-processing for deep neural network compilation.
The limitation of generating the position information such that the position information is node permutation invariant, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 7, 13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2023/0176840 A1 – Learned Graph Optimizations for Compilers, hereinafter referred to as “Zhou”).
Regarding claim 1, Zhou teaches an apparatus of pre-processing for deep neural network compilation (Zhou, [0067] – teaches compiler optimization network to generate an optimization plan for an input program), comprising:
at least one memory (Zhou, [0079] – teaches executing instructions stored in memory on a processor); and
at least one processor coupled to the at least one memory, the at least one processor (Zhou, [0079] – teaches executing instructions stored in memory on a processor) configured to:
receive a representation of an artificial neural network (ANN) model (Zhou, [0068] – teaches receiving an operational graph; see also Zhou, [0007] – teaches that machine learning algorithms are represented by computational graphs), the ANN including multiple nodes coupled by edges (Zhou, [0053] – teaches nodes and edges of computational graphs);
determine position information for each node of the ANN model (Zhou, [0052] – teaches capturing topological information in the computations graph);
generate an operator embedding to represent operators of the ANN model in an embedding space (Zhou, [0053] – teaches representing the computational graph [ML algorithm] as an encoding of meta features for nodes and edges, including operation type) based on the position information for each node (Zhou, [0052]-[0054] – teaches generating graph embeddings based on neighbor nodes and topological information);
process, by a graph neural network (GNN) (Zhou, [0052] – teaches the graph embedding network is a graph neural network), the operator embedding to generate a graph embedding corresponding to the ANN model (Zhou, [0070] – teaches generating a graph embedding using a graph embedding network for the input program [ML algorithm]) according to a learned distance metric and based on the position information (Zhou, [0052]-[0054] – teaches generating graph embeddings based on neighbor nodes and topological information; see also, Zhou, [0051] – proximal policy optimization); and
determine, by the GNN, a set of hyperparameters for the ANN model based on the graph embedding (Zhou, [0071]-[0072] – teaches using the graph embedding to output an optimization plan for the input program).
Regarding claim 7, it is the method embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Zhou further teaches a processor-implemented (Zhou, [0067] – teaches using multiple computers) method of pre-processing for deep neural network compilation (Zhou, [0067] – teaches compiler optimization network to generate an optimization plan for an input program) performed by at least one processor (Zhou, [0079] – teaches executing instructions stored in memory on a processor) …
Regarding claim 13, it is the computer-readable medium embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1.
Zhou further teaches a non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor (Zhou, [0079] – teaches executing instructions stored in memory on a processor) …
Regarding claim 19, it is the apparatus embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Zhou further teaches an apparatus of pre-processing for deep neural network compilation (Zhou, [0067] – teaches compiler optimization network to generate an optimization plan for an input program) …
Claim(s) 2-3, 8-9, 14-15, 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Paduraru et al. (US 2026/0178663 A1 – Generating Positional Encodings of Directed Graphs, hereinafter referred to as “Paduraru”).
Regarding claim 2, Zhou teaches all of the limitations of the apparatus of claim 1 as noted above. However, Zhou does not explicitly teach in which the position information comprises a sinusoidal position embedding.
Paduraru teaches in which the position information comprises a sinusoidal position embedding (Paduraru, [0086] – teaches positional encoding including sinusoidal position encoding; see also Paduraru, [0034]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zhou with the teachings of Paduraru in order to capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, as well as capture some notion of distance between nodes in the field of graph positional embeddings (Paduraru, [0044] – “A positional encoding system ... within the system ... processes at least a portion of the graph data ... to generate a respective positional encoding for each of the nodes in the graph. Generally, a positional encoding is an embedding that, e.g., has the same dimensionality as the node features in the graph data..., and that represents the position of each of the nodes within the graph... As will be described in more detail below, the system ... generates the positional encodings such that the positional encodings capture directedness in the graph. That is, modifying the direction of any given edge within the graph ... will generally result in a modification to at least one of the positional encodings for at least one of the nodes in the graph... In general the positional encodings will also capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, and may also capture some notion of distance between nodes.”).
Regarding claim 3, Zhou in view of Paduraru teaches all of the limitations of the apparatus of claim 2 as noted above. Paduraru further teaches in which the sinusoidal position embedding is computed based a relative node distance to a root node (Paduraru, [0086] – teaches computing sinusoidal position encoding for a node based on the input position corresponding to the node; see also Paduraru, [0034]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Zhou and Paduraru in order to compute positional embeddings to capture information relating to the position (connectedness) of a node in the graph in relation to other nodes of the graph, as well as capture some notion of distance between nodes (Paduraru, [0044]).
Regarding claim 8, the rejection of claim 7 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 2.
Regarding claim 9, the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 3.
Regarding claim 14, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 2.
Regarding claim 15, the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 3.
Regarding claim 20, the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 2.
Regarding claim 21, the rejection of claim 20 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru for the reasons set forth in the rejection of claim 3.
Claim(s) 4, 10, 16, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Paduraru and further in view of Yan et al. (U.S. Pat. No. 12,572,793 B1 – Subroutine Neural Networks, hereinafter referred to as “Yan”).
Regarding claim 4, Zhou in view of Paduraru teaches all of the limitations of the apparatus of claim 3 as noted above. However, Zhou in view of Paduraru does not explicitly teach in which the relative node distance comprises a shortest distance position or a longest distance position.
Yan teaches in which the relative node distance comprises a shortest distance position or a longest distance position (Yan, col. 13:58-col. 14:39 - teaches a shortest path algorithm the determine position distance).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zhou in view of Paduraru with the teachings of Yan in order to enable the model to make full use of the order of the input sequence without relying on recurrence or convolutions in the field of graph positional embeddings (Yan, col. 6:1-18 – “In some implementations, the bitwise embedding layer ... combines, for each subroutine input element, the embedded representation of the subroutine input element with a positional embedding of the input position of the subroutine input element in the input order to generate a combined embedded representation of the subroutine input element. That is, each position in the input sequence has a corresponding embedding, and for each subroutine input element the bitwise embedding layer ... combines the embedded representation of the subroutine input element with the embedding of the subroutine input element's position in the input sequence... For example, the bitwise embedding layer ... can concatenates or determine a sum or an average of the embedded representation of the subroutine input element and the positional embedding. Such positional embeddings can enable the model to make full use of the order of the input sequence without relying on recurrence or convolutions.”).
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru and further in view of Yan for the reasons set forth in the rejection of claim 4.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru and further in view of Yan for the reasons set forth in the rejection of claim 4.
Regarding claim 22, the rejection of claim 21 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Paduraru and further in view of Yan for the reasons set forth in the rejection of claim 4.
Claim(s) 5, 11, 17, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Ren et al. (HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extractions, hereinafter referred to as “Ren”).
Regarding claim 5, Zhou teaches all of the limitations of the apparatus of claim 1 as noted above. However, Zhou does not explicitly teach in which nodes having a same node type have different position information.
Ren teaches in which nodes having a same node type have different position information (Ren, Fig. 1 – teaches an example graph to sequence that shows nodes of a same type with different position information).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zhou with the teachings of Ren in order to invertibly map between a graph and an alternating sequence of nodes and edges representing position information in the field of graph positional embeddings (Ren, section 1 – “We propose a general technique to invertibly map between an information graph and an alternating sequence (assuming a given graph traversal algorithm). Generating an alternating sequence is equivalent to generating the original information graph.”).
Regarding claim 11, the rejection of claim 7 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Ren for the reasons set forth in the rejection of claim 5.
Regarding claim 17, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Ren for the reasons set forth in the rejection of claim 5.
Regarding claim 23, the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Ren for the reasons set forth in the rejection of claim 5.
Claim(s) 6, 12, 18, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Huang et al. (GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation, hereinafter referred to as “Huang”).
Regarding claim 6, Zhou teaches all of the limitations of the apparatus of claim 1 as noted above. However, Zhou does not explicitly teach generate the position information such that the position information is node permutation invariant.
Huang teaches generate the position information such that the position information is node permutation invariant (Huang, section 1 – teaches permutation invariant graph generation [Meaning that the position information is permutation invariant]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Zhou with the teachings of Huang in order to capture the important permutation invariant properties of graphs in the field of graph positional embeddings (Huang, section I – “Learning the distribution of discrete and combinatorial graph structures is a challenging task, which is also a necessary and fundamental step for further jointly modelling attributes ... and labels ... in semantic abundant graphs. Traditional methods for graph generation date back to random graph models..., which rely on hand-crafted stochastic generation processes and capture limited graph statistic properties. Recent deep graph generative models utilize the capacity of neural networks to learn graph structure distribution effectively. The prominent paradigms include variational autoencoder (VAE) based models [14]–[16], generative adversarial network (GAN) based models [17], [18], flow-based models..., and autoregressive models... Among them, autoregressive models achieve the most impressive generation quality on discrete graph structures. However, they rely on node generation orderings with high time complexity and fail to capture the important permutation invariant properties of graphs. The desired likelihood-based graph generative models should estimate invariant likelihood to all possible equivalent adjacency matrices of the same graph. To reach this goal, Niu et al. ... creatively integrate score-based generative models ... with graph neural networks to implicitly represent permutation-invariant distributions, but still suffer from generation quality and sampling speed.”).
Regarding claim 12, the rejection of claim 7 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Huang for the reasons set forth in the rejection of claim 6.
Regarding claim 18, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Huang for the reasons set forth in the rejection of claim 6.
Regarding claim 24, the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Zhou in view of Huang for the reasons set forth in the rejection of claim 6.
Conclusion
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
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 number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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