Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This office action is in response to the original filing of 11/21/2023. Claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Claim 1:
Step 1: The claim is directed to a medium, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “assembling, from the data set, a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. and “identifying the one or more locations by determining similarities between the generated location embeddings and a description embedding representative of the description” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2)
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 1 recites the additional elements:
“receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“a memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not contain significantly more than the judicial exception.
“receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“a memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 11:
Step 1: The claim is directed to a system, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “identifying the one or more locations by determining similarities between the description embedding and location embeddings determined using a graph neural network algorithm applied to a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2)
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 1 recites the additional elements:
“receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying a machine learning language model to the description to determine a description embedding” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“one or more processors and a memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not contain significantly more than the judicial exception.
“receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying a machine learning language model to the description to determine a description embedding” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“one or more processors and a memory” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 16:
Step 1: The claim is directed to a method, falling under one of the four statutory categories of invention.
Step 2A Prong 1: The claim recites following abstract ideas:
The limitations “assembling, and from the data set, a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. Claim 1 recites the additional elements:
“receiving, by a computing system, a data set having a hierarchical structure and descriptions associated with identified locations in the data set” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying, , a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes”; and “training, the graph neural network algorithm based on the generated location embeddings, description embedding representative of the descriptions, and the identified locations” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“a computing system” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: The claim does not contain significantly more than the judicial exception.
“receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
“applying, , a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes”; and “training, the graph neural network algorithm based on the generated location embeddings, description embedding representative of the descriptions, and the identified locations” amount to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“a computing system” merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f)). These computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 2 recites “wherein the data set is source code of an application; wherein the nodes in the graph data structure represent functions defined in the source code; and wherein the edges represent function calls made between the functions” amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim 3 recites “wherein the description includes text identifying one or more attributes associated with the application; and wherein the identified one or more locations correspond to portions of the source code determined to be relevant to the one or more attributes” amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim 4 recites “wherein applying the graph neural network algorithm includes: determining node embeddings for nodes in the graph data structure; and assigning nodes in the graph data structure to a plurality of pooling layers, wherein the assigning further includes determining, for a given one of the pooling layers, clusters for ones of the assigned nodes” amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim 5 recites “wherein applying the graph neural network algorithm includes: performing, for a given cluster, message passing between nodes within the given cluster, wherein the message passing includes applying one or more linear transformations and one or more non-linearities based on embeddings determined for the nodes in the given cluster” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 6 recites “wherein applying the graph neural network algorithm includes: calculating a location embedding for a given node assigned to a first of the plurality of pooling layers by combining the given node's embedding with an embedding determined by pooling nodes assigned to a given one of the clusters in a second of the plurality of pooling layers” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 7 recites “wherein the operations further comprise: applying a machine learning language model to the description to determine the description embedding, wherein the applying includes: tokenizing the description to produce a plurality of tokens; and supplying the plurality of tokens to an encoder to produce the description embedding” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 8 recites “wherein the encoder includes one or more self-attention layers and one or more feed-forward layers” amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claim 9 recites “wherein determining a given one of the similarities includes: calculating a cosine similarity between a given one of the generated location embeddings and a description embedding representative of the description” constitute mathematical calculations and an evaluation/judgment. Mathematical relationships and mathematical calculations, See MPEP §2106.04(a)(2).
Claim 10 recites “wherein the identifying includes ranking the one or more locations based on the determined similarities” amount to insignificant extra solution activity like mere data gathering, MPEP 2106.05(g)).
Claims 12-15 and 17-20 recite subject matter similar to claims 2-10, respectively, and are therefore rejected for the same reasons and based on the same rationale set forth with respect to claims 2-9.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. Claims 1-3, 9-12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240).
Claim 1. WU discloses a non-transitory computer readable medium having program instructions stored therein that are executable by a computing system to perform operations comprising:
assembling, from the data set, a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure ([0021]-[0022], [0053] claim 2) [generating a function-call graph corresponding to a program based on static analysis of the program's source code.. function-call graph includes functions of the program and function-call relationships];
applying a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes ([0053], claim 3) [generating feature embeddings corresponding to program functions using a trained GNN]; and
WU does not explicitly disclose receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request;
identifying the one or more locations by determining similarities between the generated location embeddings and a description embedding representative of the description.
However, BAHRAMI discloses receiving a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request ([0027]-[0029], fig. 2, claim 1); identifying the one or more locations by determining similarities between the generated location embeddings and a description embedding representative of the description ([0030]-[0031], fig. 2). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of BAHRAMI to incorporate the above cited features. One would have been motivated to do so to improve the predictability use of known embedding and similarity-search techniques according to their established functions.
Claim 2. WU and BAHRAMI disclose the computer readable medium of claim 1, WU further discloses wherein the data set is source code of an application; wherein the nodes in the graph data structure represent functions defined in the source code; and wherein the edges represent function calls made between the functions ([0053], [0060], claims 2-3)[ generating a function-call graph corresponding to a program based on static analysis of source code… function embeddings using a GNN operating with the function-call graph].
Claim 3. WU and BAHRAMI disclose the computer readable medium of claim 2, BAHRAMI further discloses wherein the description includes text identifying one or more attributes associated with the application; and wherein the identified one or more locations correspond to portions of the source code determined to be relevant to the one or more attributes (fig. 2 blocks 250-280, claim 1)[ receiving a natural-language search query for source-code suggestions and returning source code responsive to the query… the example query “plot a line” as a request for source code that implements the described functionality]. One would have been motivated to do so to improve the predictability use of known embedding and similarity-search techniques according to their established functions.
Claim 9. WU and BAHRAMI disclose the computer readable medium of claim 1, BAHRAMI further discloses wherein determining a given one of the similarities includes: calculating a cosine similarity between a given one of the generated location embeddings and a description embedding representative of the description (comparison of the natural-language search vector and natural-language code vectors may be based on cosine similarity). (FIG. 2, block 270). One would have been motivated to do so to improve the predictability use of known embedding and similarity-search techniques according to their established functions.
Claim 10. WU and BAHRAMI disclose the computer readable medium of claim 1, BAHRAMI further discloses wherein the identifying includes ranking the one or more locations based on the determined similarities (…comparing a query vector with code vectors, determining a similarity score, and returning source code based on that comparison) (FIG. 2, blocks 270 and 280) [Once similarity scores are generated for multiple candidate code representations, ordering the candidates according to those scores would have been an obvious implementation of the disclosed similarity-based source-code retrieval process]. One would have been motivated to do so to improve the predictability use of known embedding and similarity-search techniques according to their established functions.
Claim 11. Supra claim 1 and BAHRAMI further discloses performing the natural-language-query, vector-generation, vector-comparison, and source-code-response operations (claim 15).
Claim 12. WU and BAHRAMI disclose the computing system of claim 11, WU further discloses wherein the data set is source code, the nodes in the graph data structure represent functions defined in the source code, and the description includes text identifying one or more attributes associated with the source code (..source code, function-call graphs, and function embeddings) (claims 1-3).
Claim 15 represents the system of claim 10 and is rejected under the same rationale.
6. Claims 4-6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240) and further in view of Ying et al. “Hierarchical Graph Representation Learning with Differentiable Pooling” 2018.
Claim 4. WU and BAHRAMI disclose the computer readable medium of claim 1, but fail to explicitly disclose wherein applying the graph neural network algorithm includes: determining node embeddings for nodes in the graph data structure; and assigning nodes in the graph data structure to a plurality of pooling layers, wherein the assigning further includes determining, for a given one of the pooling layers, clusters for ones of the assigned nodes.
However, Ying discloses determining node embeddings for nodes in the graph data structure; and assigning nodes in the graph data structure to a plurality of pooling layers, wherein the assigning further includes determining, for a given one of the pooling layers, clusters for ones of the assigned nodes (Introduction, discussing generation of individual node embeddings; see also the GNN formulation in § 3.1; Differentiable Pooling, assignment GNN and soft cluster assignment matrix, including the pooled feature and adjacency operations especially § 3.2; Hierarchical representation Figure 1 and § 1) [additionally describes GNN propagation using message passing and explicitly provides an example implementation using linear transformations and ReLU nonlinearities. The relevant GNN propagation disclosure appears in § 3.1, including the message-passing formulation and Equation (2)]. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of Ying to incorporate the above cited features. One would have been motivated to do so to facilitate production of hierarchical graph representations.
Claim 5. WU BAHRAMI and Ying disclose the computer readable medium of claim 4, Ying further discloses wherein applying the graph neural network algorithm includes: performing, for a given cluster, message passing between nodes within the given cluster, wherein the message passing includes applying one or more linear transformations and one or more non-linearities based on embeddings determined for the nodes in the given cluster. (§ 3.1, Equations 1-2). One would have been motivated to do so to facilitate production of hierarchical graph representations.
Claim 6. WU BAHRAMI and Ying disclose the computer readable medium of claim 4, Ying further discloses wherein applying the graph neural network algorithm includes: calculating a location embedding for a given node assigned to a first of the plurality of pooling layers by combining the given node's embedding with an embedding determined by pooling nodes assigned to a given one of the clusters in a second of the plurality of pooling layers (constructing representations of coarsened nodes/clusters from node embeddings and learned assignment information. The principal disclosure is in § 3.2, Equations (3) and (4), where node embeddings are combined through the assignment matrix to produce representations for the pooled graph) [However, combining a local node representation with a higher-level pooled representation would have been an obvious design choice for preserving both local and hierarchical information, particularly in view of Ying's express teaching that the coarsened representations are produced from lower-level node embeddings]. One would have been motivated to do so to facilitate production of hierarchical graph representations.
Claim 13. WU BAHRAMI and Ying disclose the computing system of claim 11, Ying further discloses wherein the graph neural network algorithm assigns nodes in the graph data structure to a plurality of pooling layers and determines, for a given one of the pooling layers, clusters for ones of the assigned nodes for message passing § 3.1 and § 3.2, and Figure 1. One would have been motivated to do so to facilitate production of hierarchical graph representations.
7. Claims 7-8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240) and further in view of Feng et al. CodeBERT: A Pre-Trained Model for Programming and Natural Languages” 2020.
Claim 7. WU and BAHRAMI disclose the computer readable medium of claim 1, BAHRAMI further discloses wherein the operations further comprise: applying a machine learning language model to the description to determine the description embedding, wherein the applying includes: tokenizing the description to produce a plurality of tokens (tokenizing the natural-language search query before mapping the query to a natural-language search vector. FIG. 2, block 260); However, Feng discloses supplying the plurality of tokens to an encoder to produce the description embedding (a bimodal model for programming language and natural language and a Transformer-based neural architecture supporting natural-language code search) (abstract). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of Feng to incorporate the above cited features. One would have been motivated to do so to facilitate generating learned representations for natural-language/code search.
Claim 8. WU BAHRAMI and Feng disclose the computer readable medium of claim 7, wherein the encoder includes one or more self-attention layers and one or more feed-forward layers (expressly states that it uses a multi-layer bidirectional Transformer as its model backbone § 4.1, Model Architecture) [Transformer encoders conventionally employ self-attention and position-wise feed-forward sublayers, the claimed encoder architecture would have been an obvious implementation of the language-model encoder]. One would have been motivated to do so to facilitate generating learned representations for natural-language/code search.
Claim 14. WU BAHRAMI and Feng disclose the computing system of claim 11, wherein the applying includes: tokenizing the description to produce a plurality of tokens (tokenizing the natural-language search query before mapping the query to a natural-language search vector. FIG. 2, block 260); However, Feng discloses; supplying the plurality of tokens to an encoder that includes one or more self-attention layers and one or more feed-forward layers (a bimodal model for programming language and natural language and a Transformer-based neural architecture supporting natural-language code search) (abstract). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of Feng to incorporate the above cited features. One would have been motivated to do so to facilitate generating learned representations for natural-language/code search.
8. Claims 16, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240) and further in view of Guo et al. “GRAPHCODEBERT: “GRAPHCODEBERT: Pre-Training Code Representations with Data Flow” 2021.
Claim 16. Supra claim 1 and Guo discloses training, by the computing system, the graph neural network algorithm based on the generated location embeddings, description embedding representative of the descriptions, and the identified locations (..learning code representations using both source code and code structure and expressly introduces structure-aware pre-training tasks, including aligning representations between source code and code structure) (abstract; § 1, Introduction; and § 4, particularly § 4.1)...( further states that it is pre-trained using source code paired with comments and data flow, and that the model supports natural-language code search). (§ 4 and Figure 2). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of Guo to incorporate the above cited features. One would have been motivated to do so to make corresponding description and location representations useful for similarity-based identification.
Claim 18. WU BAHRAMI and Guo disclose the method of claim 16, Wu further discloses comprising: receiving, by the computing system, a request to identify, in the data set, one or more locations corresponding to a description specified by the request; and identifying, by the computing system, the one or more locations by determining similarities between generated location embeddings and a description embedding representative of the description (GNN-generated function embeddings) (claims 1–3 and 8).
Claim 19. WU BAHRAMI and Guo disclose the method of claim 16, Wu further discloses wherein the data set is source code of an application; wherein the edges in the graph data structure include edges that represent function calls in the source code; and wherein the descriptions include texts identifying attributes associated with the application (claims 2–3 and 16–17).
9. Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240) in view of Guo et al. “GRAPHCODEBERT: Pre-Training Code Representations with Data Flow” 2021 and further in view of You et al. “Graph Contrastive Learning with Augmentations” 2021.
Claim 17. WU BAHRAMI and Guo disclose the method of claim 16, fail to explicitly disclose wherein the training includes applying a contrastive learning algorithm using the location embeddings, the description embeddings, and the identified locations.
However, You discloses framework learns unsupervised graph representations using graph augmentations (abstract)..( The GraphCL framework uses graph-based representations and a contrastive objective to increase agreement between representations of related graph views) (§ 3, “The GraphCL Framework”;§ 3.1, Graph Data Augmentation; § 3.2, Contrastive Learning; and Figure 1). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of You to incorporate the above cited features. One would have been motivated to do so to make training graph embeddings so that representations associated with corresponding training examples are brought closer together and representations associated with non-corresponding examples are distinguished.
10. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (US 2023/0185568) in view of BAHRAMI et al. (US 2022/0138240) in view of Guo et al. “GRAPHCODEBERT: “GRAPHCODEBERT: Pre-Training Code Representations with Data Flow” 2021 and further in view of Ying et al. “Hierarchical Graph Representation Learning with Differentiable Pooling” 2018.
Claim 20. WU BAHRAMI and Guo disclose the method of claim 16, but fail to explicitly disclose comprising: further determining, by the computing system, node embeddings for nodes in the graph data structure; assigning, by the computing system, nodes in the graph data structure to a plurality of pooling layers; and performing, by the computing system, message passing between nodes within a given pooling layer.
However, Ying discloses further determining, by the computing system, node embeddings for nodes in the graph data structure; assigning, by the computing system, nodes in the graph data structure to a plurality of pooling layers; and performing, by the computing system, message passing between nodes within a given pooling layer (§ 3.1, § 3.2, Equations (1)–(4), and Figure 1). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WU further in view of Ying to incorporate the above cited features. One would have been motivated to do so to facilitate production of hierarchical graph representations.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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