DETAILED ACTION
1. This office action is in response to Application No. 18142897 filed on 06/18/2026. Claims 2, 3 and 6 have been cancelled. Claims 1, 4, 5, 7-17 are presented for examination and are currently pending.
Response to Arguments
2. The Applicant’s argument on pages 14-15 that “… the accuracy of various tasks associated with graphs (e.g., downstream tasks such as classification or regression) may be considerably improved by using the integrated embedding representation, which prevents loss of information during generation of integrated embedding representations at paragraphs [0023], [0024], and [0027]” is persuasive because it improves the functioning of the technological field. As a result, the 101 rejection is withdrawn.
On page16-17 of the remarks, the Applicant argued that “Without conceding to the appropriateness of the rejection, claim 1 is amended based on features recited in at least dependent claims 2 and 6. For example, amended claim 1 recites:
A graph embedding method performed by at least one computing device,
the graph embedding method comprising: acquiring a first embedding representation and a second embedding representation of a target graph, the first embedding representation and the second embedding representation being generated by embedding the target graph via different graph neural networks (GNNs); changing the second embedding representation by reflecting a specific value into the second embedding representation based on a multiplication operation; and generating an integrated embedding representation by aggregating the first embedding representation and the changed second embedding representation based on an addition operation, wherein one of the first embedding representation and the second embedding representation is generated by aggregating information of neighbor nodes that form the target graph, and wherein the other one of the first embedding representation and the second embedding representation is generated by reflecting topology information of the target graph.
Applicant respectfully submits that the cited references do not teach or suggest such subject matter.
The argument above is not persuasive because the instant claims are still obvious over the teachings of Mao modified by Manolache as detailed in the rejections of this Office Actions even though the previous claims 2, 3 and 6 have now been incorporated into the independent claims.
On page 18 of the remarks, the Applicant argued that “Mao does not teach that the different embeddings are generated by different GNNs.”
The argument above is not persuasive because Mao teaches we stack multiple layers of GNNs…The embeddings from different layers … pg. 1101, right col., second para. The Examiner notes that in Fig. 4b, he1 is the first embedding representation and he2 is the second embedding representation). This means that since Mao teaches stack multiple layers of GNNs, and embedding representations are from different layers, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention that each embedding representation of Mao is generated by a different GNN. Furthermore, Mao’s Many GNNs, pg. 1098, right col., first para. reads on different graph neural networks (GNNs).
On page 18 of the remarks, the Applicant argued that “Additionally, Mao does not teach that one of the different embeddings (e.g., he1) is generated by aggregating information of neighbor nodes that form the target graph and that the other one of the different embeddings (e.g., hₑ₂) is generated by reflecting topology information of the target graph”.
The argument is not persuasive because Applicant’s broadly claimed “wherein one of the first embedding representation and the second embedding representation is generated by aggregating information of neighbor nodes” would have been obvious a person having ordinary skill in the art as Mao’s equations:
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where N𝑒𝑒𝑖 represents the set of neighboring nodes around 𝑒𝑖 , 𝑾𝑙 is the transformation matrix of layer 𝑙. Equation 2 is responsible for aggregating information from the neighboring nodes while Equation 3 transforms the node embeddings into better ones, pg. 1098, right col., first para.).
Furthermore, the broadly claimed limitation of “wherein the other one of the first embedding representation and the second embedding representation is generated by reflecting topology information” would also have been obvious to a person having ordinary skill in the art as Mao’s new transformation operation, Relational Reflection Transformation. Let relation embedding 𝒉𝑟 be a normal vector, there is one and only one hyperplane 𝑷𝑟 and only one corresponding reflection matrix 𝑴r, pg. 1101, left col., section 5.1).
On page 18 of the remarks, the Applicant argued that “Manolache does not cure the deficiencies of Mao as it was cited to allegedly teach the claimed "at least one computing device." Therefore, Applicant respectfully submits that the cited references, either individually or in combination, do not teach or suggest claim 1”.
Manolache is used to teach a computing device (computing appliance is a personal computer[0058]; Exemplary embedding transformations include nudging a vector by a small amount ε along one of the axes or along a transformation-specific predetermined direction. Other exemplary transformations may comprise a rotation and a reflection about a pre-determined plane [0040]. The Examiner notes small amount ε is a specific value reflected into an embedding), then,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao to incorporate the teachings of Manolache for the benefit of increasing the number of distinct transformations of a point ([0069], Fig. 6) in graph neural networks (GNN) configured such that each hidden unit receives an input (e.g., embedding vector) characterizing a respective token (Manolache [0047])
On page 18 of the remarks, the Applicant argued that “Based on at least the above reasons, Applicant respectfully submits that independent claim 1 is patentable over the cited references. Independent claims 16 and 17 recite similar features as noted above with respect to claim 1. Therefore, Applicant respectfully submits that claims 16 and 17 are patentable for similar reasons as noted above with respect to claim 1. Dependent claims 4-5 and 7-15 each depend from claim 1 described above. Accordingly, Applicant respectfully submits that claims 4-5 and 7-15 are patentable at least due to their respective dependencies and the additional features recited therein. Withdrawal of the pending rejections of these claims is, therefore, respectfully requested”.
As argued above, independent 1 is not patentable because the claims are still obvious over the prior art of record.
Similarly, independent claims 16 and 17 are not patentable for same reasons as claim 1.
The dependent claims 4, 5 and 7-15 which depend directly or indirectly from independent claim 1 are not patentable because the instant claims are still
obvious over the prior art of record.
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.
3. Claims 1, 4, 6, 10, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021)
Regarding claim 1, Mao teaches a graph embedding method performed by at least one computing device, the graph embedding method comprising (Furthermore, we propose a novel GNNs-based method, Relational Reflection Entity Alignment (RREA). RREA leverages Relational Reflection Transformation to obtain relation specific embeddings for each entity in a more efficient way, abstract):
acquiring a first embedding representation and a second embedding representation of a target graph (To create a global-aware graph representation, we stack multiple layers of GNNs to capture multi-hop neighborhood information. The embeddings from different layers …
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pg. 1101, right col., second para. The Examiner notes that in Fig. 4b, he1 is the first embedding representation and he2 is the second embedding representation),
the first embedding representation and the second embedding representation being generated by embedding the target graph via different graph neural networks (GNNs) (Furthermore, we propose a novel GNNs-based method, Relational Reflection Entity Alignment (RREA). RREA leverages Relational Reflection Transformation to obtain relation specific embeddings for each entity in a more efficient way, abstract. The Examiner notes GNNs indicates a plurality of Graph Neural Network);
changing the second embedding representation (we design a new transformation operation, Relational Reflection Transformation, which fulfills these two criteria. This new operation is able to reflect entity embeddings along different relational hyperplanes to construct relation specific embeddings, pg. 1096, left col., second para.)
by reflecting a specific value into the second embedding representation based on a multiplication operation (It is easy to derive that the reflection of entity embedding 𝒉𝑒 along the relational hyperplane 𝑷𝑟 can be computed by 𝑴𝑟𝒉𝑒, pg. 1101, left col., section 5.1. The Examiner notes that in Fig. 4b, 𝑴𝑟𝒉𝑒2 is the changed second embedding representation, and he2 Fig. 4b has been reflected across the y-axis, and this indicates the x-coordinate value of the point he2 as the specific value has been multiplied by -1); and
generating an integrated embedding representation by aggregating the first embedding representation and the changed second embedding representation based on an addition operation (… concatenate the summation of the relation embeddings with entity embeddings to get dual-aspect embeddings. In this paper, we adopt dual-aspect embeddings, pg. 1101, right col., third para. The Examiner notes summation indicates addition operation is applied to the aggregation),
wherein one of the first embedding representation and the second embedding representation is generated by aggregating information of neighbor nodes that form the target graph (Many GNNs in entity alignment task contains the following equations:
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where N𝑒𝑒𝑖 represents the set of neighboring nodes around 𝑒𝑖 , 𝑾𝑙 is the transformation matrix of layer 𝑙. Equation 2 is responsible for aggregating information from the neighboring nodes while Equation 3 transforms the node embeddings into better ones, pg. 1098, right col., first para.), and
wherein the other one of the first embedding representation and the second embedding representation is generated by reflecting topology information of the target graph (we design a new transformation operation, Relational Reflection Transformation. Let relation embedding 𝒉𝑟 be a normal vector, there is one and only one hyperplane 𝑷𝑟 and only one corresponding reflection matrix 𝑴r, pg. 1101, left col., section 5.1)
Mao does not explicitly teach a computing device.
Manolache a computing device (computing appliance is a personal computer[0058]; Exemplary embedding transformations include nudging a vector by a small amount ε along one of the axes or along a transformation-specific predetermined direction. Other exemplary transformations may comprise a rotation and a reflection about a pre-determined plane [0040]. The Examiner notes small amount ε is a specific value reflected into an embedding)
Since Mao as primary reference teaches reflection operation about a plane in Fig. 4, and Manolache as secondary reference discloses transformation may comprise a reflection about a pre-determined plane [0040], then,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao to incorporate the teachings of Manolache for the benefit of increasing the number of distinct transformations of a point ([0069], Fig. 6) in graph neural networks (GNN) configured such that each hidden unit receives an input (e.g., embedding vector) characterizing a respective token (Manolache [0047])
Regarding claim 4, Mao and Manolache graph embedding method of claim 1, Manolache teaches wherein the specific value is an irrational number (Exemplary embedding transformations include nudging a vector by a small amount ε along one of the axes or along a transformation-specific predetermined direction. Other exemplary transformations may comprise a rotation and a reflection about a pre-determined plane [0040]; The Examiner notes small amount ε is a specific value reflected into an embedding, and instant specification of the Applicant discloses that “Here, the specific value of + ε may be reflected into the second embedding representation” [0087]).
The same motivation to combine independent claim 1 applies here.
Regarding claim 10, Mao and Manolache teaches the graph embedding method of claim 1, Mao teaches wherein the acquiring the first embedding representation and the second embedding representation comprises: acquiring the first embedding representation via a neighbor node information aggregation scheme-based GNN (Many GNNs in entity alignment task contains the following equations:
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where N𝑒𝑒𝑖 represents the set of neighboring nodes around 𝑒𝑖 , 𝑾𝑙 is the transformation matrix of layer 𝑙. Equation 2 is responsible for aggregating information from the neighboring nodes while Equation 3 transforms the node embeddings into better ones, pg. 1098, right col., first para.); and
acquiring the second embedding representation by extracting the topology information of the target graph using the first embedding representation (Dual-Aspect Embedding: … entity embeddings generated by GNNs only contain the topological information … Therefore, they concatenate the summation of the relation embeddings with entity embeddings to get dual-aspect embeddings. In this paper, we adopt dual-aspect embeddings, pg.1101, right col., third para.).
Regarding claim 16, claim 16 is similar to claim 1. It is rejected in the same manner and reasoning applying. Further, Manolache teaches at least one processor; and a memory configured to store program code executable by the at least one processor, the program code comprising: acquiring code configured to cause the at least one processor to (Processor(s) 72 comprise a physical device (e.g. microprocessor, multi-core integrated circuit formed on a semiconductor substrate) configured to execute computational and/or logical operations with a set of signals and/or data. Such signals or data may be encoded and delivered to processor(s) 72 in the form of processor instructions, e.g., machine code [0058])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao to incorporate the teachings of Manolache for the benefit of increasing the number of distinct transformations of a point ([0069], Fig. 6) in graph neural networks (GNN) configured such that each hidden unit receives an input (e.g., embedding vector) characterizing a respective token (Manolache [0047])
Regarding claim 17, claim 17 is similar to claim 1. It is rejected in the same manner and reasoning applying. Further, Manolache teaches a non-transitory computer-readable recording medium storing program code executable by at least one processor, the program code comprising: acquiring code configured to cause the at least one processor to (a non-transitory computer-readable medium stores instructions which, when executed by at least one hardware processor of a computer system, cause the computer system [0006])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao to incorporate the teachings of Manolache for the benefit of increasing the number of distinct transformations of a point ([0069], Fig. 6) in graph neural networks (GNN) configured such that each hidden unit receives an input (e.g., embedding vector) characterizing a respective token (Manolache [0047])
4. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) and further in view of Yu et al. ("Knowledge embedding based graph convolutional network." Proceedings of the web conference 2021, April 19 - 23, 2021)
Regarding claim 5, Mao and Manolache teaches the graph embedding method of claim 1, Mao and Manolache does not explicitly teach the limitations of claim 5.
Yu teaches wherein the specific value is a value based on a learnable parameter (Wl the matrix of model parameters to be learned by the GCN, pg. 1621, left col., section 3.1), and
wherein the graph embedding method further comprises: predicting a label for a predefined task based on the integrated embedding representation (Entity Classification is the task of predicting the labels of entities in a given knowledge graph (pg. 1624, left col., last para.); … both entity embeddings and relation embeddings in our model are used to enforce optimization of each other in a recursive aggregation process, pg. 1620, left col. third para. ); and
updating a value of the learnable parameter based on a result of the predicting (Analogous to Equation 3, if we denote hlv the embedding of entity v at layer l, the entity updating rules are:
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… The relation updating rules can be defined in a similar manner:).
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pg. 1621, section 3.2)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache to incorporate the teachings of Yu for the benefit of capturing the rich semantics of heterogeneous relations and learning better context-based relation embeddings (Yu, pg. 1622, right col., first para.)
5. Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) and further in view of Huang et al. ("Long-short graph memory network for skeleton-based action recognition." proceedings of the IEEE/CVF winter conference on applications of computer vision. 2020).
Regarding claim 7, Mao and Manolache teaches the graph embedding method of claim 1, Mao and Manolache do not teach the limitations of claim 7.
Huang teaches wherein the acquiring the first embedding representation and the second embedding representation comprises (We build our model based on Bi-LSGM, then combine the forward hidden states → S and the backward hidden states ← S together, pg. 648, right col., third para. The Examiner notes the forward hidden states is the first embedding representation and backward hidden states is the second embedding representation):
acquiring a first embedding matrix and a second embedding matrix of the target graph (It is worth noticing that the hidden states and memory cells in the LSTM store data in the form of graph matrices (pg. 646, right col., first para.); we aim to improve the ability of LSTM to extract spatial information by embedding the GCN layer in the LSTM cell, pg. 647, right co., section 3. Methodology),
the first embedding matrix and the second embedding matrix having different sizes; and acquiring the first embedding representation and the second embedding representation by performing a resizing operation on at least one of the first embedding matrix and the second embedding matrix (To calculate the feature map of LSGM, we compress the feature dimension and resize S via 2 FC layers (pg. 648, right col., fifth para.); The Examiner notes resizing S indicates there are different sizes for both the forward hidden states which is the first embedding representation and backward hidden states which is the second embedding representation).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache to incorporate the teachings of Huang for the benefit of extracting temporal and spatial features and capturing high-level nodes representations (Huang, pg. 651, conclusion)
Regarding claim 8, Mao, Manolache and Huang teaches the graph embedding method of claim 7, Huang teaches wherein the resizing operation is implemented by a multilayer perceptron (Therefore, we embed the graph convolution layer into the LSTM cell to capacitate it to extract spatial features, which is our LSGM cell (pg. 648, left col., second to the last para.); To calculate the feature map of LSGM, we compress the feature dimension and resize S via 2 FC layers (pg. 648, right col., fifth para. The Examiner notes the fully connected (FC) layers is multilayer perceptron).
The same motivation to combine dependent claim 7 applies here.
6. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) in view of Huang et al. ("Long-short graph memory network for skeleton-based action recognition." proceedings of the IEEE/CVF winter conference on applications of computer vision. 2020) and further in view of Gong et al. (US20240028631 PCT filed 10/05/2021)
Regarding claim 9, Mao, Manolache and Huang teaches the graph embedding method of claim 7, Mao, Manolache and Huang does not explicitly teach the limitations of claim 9.
Gong teaches wherein the generating the integrated embedding representation comprises: generating a first embedding vector by performing a pooling operation on the first embedding representation (After getting the sentence embedding 210 and the entity embedding 216 … as described above, two additional pooling layers 218, 220 can be applied, respectively, to compress the sentence and entity embedding lengths further [0053]. The Examiner notes sentence embedding 210 is the first embedding representation);
generating a second embedding vector, which has the same dimension quantity as the first embedding vector (different entity types have the same embedding [0015]), by performing a pooling operation on the changed second embedding representation (After getting the sentence embedding 210 and the entity embedding 216 … as described above, two additional pooling layers 218, 220 can be applied, respectively, to compress the sentence and entity embedding lengths further [0053]. The Examiner notes sentence embedding 216 is the second embedding representation); and
generating a vector-type integrated embedding representation based on the first embedding vector and the second embedding vector (In a next step, it may be provided that both the pooled sentence embedding 222 and the pooled entity embedding 224 are given as input to a text aggregator 226 to aggregate the textual embedding [0054]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao, Manolache and Huang to incorporate the teachings of Gong for the benefit of improving and further developing a system that similar documents are identified in a highly efficient and objective way (Gong [0009])
7. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021 and further in view of Gong et al. (US20240028631 PCT filed 10/05/2021)
Regarding claim 13, Mao and Manolache teaches the graph embedding method of claim 1, Mao and Manolache does not explicitly teach the limitations of claim 13.
Gong teaches the wherein the generating the integrated embedding representation comprises: performing a pooling operation on the first embedding representation and the changed second embedding representation (After getting the sentence embedding 210 and the entity embedding 216 … as described above, two additional pooling layers 218, 220 can be applied, respectively, to compress the sentence and entity embedding lengths further [0053]); and
generating the integrated embedding representation by aggregating results of the pooling operation (n a next step, it may be provided that both the pooled sentence embedding 222 and the pooled entity embedding 224 are given as input to a text aggregator 226 to aggregate the textual embedding [0054]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache to incorporate the teachings of Gong for the benefit of improving and further developing a system that similar documents are identified in a highly efficient and objective way (Gong [0009])
8. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) and further in view of Dalli et al. (US20210232915)
Regarding claim 11, Mao and Manolache teaches the graph embedding method of claim 10, Mao, Manolache and Huang does not explicitly teach the limitations of claim 11.
Dalli teaches wherein the first embedding representation is a three-dimensional (3D) embedding matrix generated by aggregating a feature matrix for a node tuple (An exemplary embodiment of a CNN-XNN reconstruction application in medical imaging may be used to denoise MRI or PET scans and additionally reconstruct a 3D model from one or more 2D image slices [0072]), and
wherein the generating the second embedding representation comprises: generating a two-dimensional (2D) embedding matrix by extracting diagonal elements of the 3D embedding matrix (diagonal elements are extracted matrix 317, Fig. 3B); and
extracting the topology information of the target graph by analyzing the 2D embedding matrix (extract the most pertinent information and track multiple events and objects across time and space for the whole corpus being analyzed [0120]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao, Manolache and Huang to incorporate the teachings of Dalli in order to implement a practical and resource efficient transfer to multi-dimensional data (Dalli [0047])
9. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) and further in view of Neill (US20210374143)
Regarding claim 12, Mao and Manolache teaches the graph embedding method of claim 10, Mao and Manolache does not explicitly teach the limitations of claim 12.
Neill teaches the wherein the extracting the topology information of the target graph is performed by calculating a persistence diagram (The compute module 114 subsequently stores sub-graphs of the CPS-G dataset (in-memory 118 or persistent storage database 128) [0115]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache to incorporate the teachings of Neill for the benefit of performing real-time data analytics and analysis including efficient implementation and execution of Graph Neural Network (GNN) algorithms (Neill [0007])
10. Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mao et al. (Relational reflection entity alignment. InProceedings of the 29th ACM international conference on information & knowledge management 2020 Oct 19 (pp. 1095-1104)) in view of Manolache et al. (US20220327108 filed 04/09/2021) and further in view of Iyer at al. (US20210073632 filed 11/18/2020)
Regarding claim 14, Mao, Manolache and Huang teaches the graph embedding method of claim 1, Mao and Manolache does not explicitly teach the limitations of claim 14.
Iyer teaches wherein the generating the integrated embedding representation (aggregates the block embedding(s) to generate semantic embedding(s) [0076]) comprises: acquiring a third embedding representation of the target graph (generate a third block embedding based on the third code block [0111]); changing the third embedding representation by reflecting another specific value into the third embedding representation (The third semantic embedding 528 can additionally or alternatively be associated with code snippets of other semantic concepts of computation, such as multiplication, exponents, arithmetic transformations, etc [0053]); and
generating the integrated embedding representation by aggregating the first embedding representation, the changed second embedding representation, and the changed third embedding representation (first block embedding corresponding to the first code block and the second block embedding corresponding to the second code block [0101]; The third semantic embedding 528 can additionally or alternatively be associated with code snippets of other semantic concepts of computation, such as multiplication, exponents, arithmetic transformations, etc [0053]; The semantic embedding of the sum operation can be an aggregation of one or more block embeddings corresponding to code blocks of the sum operation [0038]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache to incorporate the teachings of Iyer for the benefit of using graphs as effective representations for graph neural networks (GNNs) used to learn latent features and/or semantic information [0022] and improving the efficiency of using a computing device by generating code semantics for input in using self-supervised learning techniques (Iyer [0092])
Regarding claim 15, Mao and Manolache teaches the graph embedding method of claim 1, Mao and Manolache does not explicitly teach the limitations of claim 15.
Iyer teaches wherein the generating the integrated embedding representation (aggregates the block embedding(s) to generate semantic embedding(s) [0076])
comprises: acquiring a third embedding representation through a k-th embedding representation (k being a natural number of 3 or greater) (For example, the third semantic embedding 528 is associated with code snippets of a third semantic concept [0053]; For example, the fourth semantic concept may be arrays [0056]; semantic concept representations (e.g., semantic embeddings) [0045]);
changing the third embedding representation through the k-th embedding representation by reflecting another specific value into the third embedding representation through the k-th embedding representation (The third semantic embedding 528 can additionally or alternatively be associated with code snippets of other semantic concepts of computation, such as … arithmetic transformations, etc. [0053]); and
generating the integrated embedding representation by aggregating the first embedding representation, the changed second embedding representation, and the changed third embedding representation through k-th embedding representation (first block embedding corresponding to the first code block and the second block embedding corresponding to the second code block [0101]; The third semantic embedding 528 can additionally or alternatively be associated with code snippets of other semantic concepts of computation, such as … arithmetic transformations, etc. [0053]; The semantic embedding of the sum operation can be an aggregation of one or more block embeddings corresponding to code blocks of the sum operation [0038]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mao and Manolache and Huang to incorporate the teachings of Iyer for the benefit of using graphs as effective representations for graph neural networks (GNNs) used to learn latent features and/or semantic information [0022] and improving the efficiency of using a computing device by generating code semantics for input in using self-supervised learning techniques (Iyer [0092])
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/M.G./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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