DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3 November 2025 has been entered.
Response to Amendment
This communication is in response to the amendment filed on 3 November 2025.
Claims 3-4 and 14-15 are canceled.
Claims 1, 5, 7-9, and 17-18 are amended.
Claims 19-21 are newly added.
Claims 1-2, 5-12, and 16-21 have been examined.
Response to Arguments
In response to Applicant’s remarks filed on 3 November 2025:
a. Applicant's arguments with respect to the 35 U.S.C. 101 rejections of the pending claims have been fully considered but are not deemed persuasive.
On pages 9-20 of Applicant’s remarks, Applicant argues against the 35 U.S.C. 101 rejections of the pending claims. Applicant argues that claim 1 does not recite an abstract idea under Step 2A, Prong One; does recite a practical application under Step 2A, Prong Two; and/or does recite significantly more than an abstract idea under Step 2B.
The Office respectfully disagrees with the above remarks. With regards to the analysis at Step 2A, Prong One; Applicant cites claim 1’s recitation of a multi-layer perceptron (a special type of neural network1), a neural network, and a pre-trained predictor model as claim elements that cannot be practically performed in the human mind. The Office does not dispute this, and these claim elements have not been deemed abstract ideas under the Step 2A, Prong One analysis. Rather, these claims elements have been analyzed as additional elements (i.e. beyond the abstract idea) under Step 2A, Prong Two and Step 2B. As detailed below in the claim rejections under 35 U.S.C. 101, these high level, generic references to use of machine learning tools (i.e. use of a multi-layer perceptron, a neural network, and a pre-trained predictor model) amount to generally linking the abstract idea to a particular technological environment, which cannot be deemed a practical application nor an inventive concept. See MPEP 2106.05(e). As detailed below in the claim rejections under 35 U.S.C. 101, looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; generally linking to a particular technological environment; and/or generic computer implementation. Hence, the claims as a whole, looking at the additional elements individually and in combination, do not amount to a pracitcal application nor an inventive concept.
With regards to Applicant’s remarks about mathematical concepts (remarks, pages 12-13), Applicant is advised that “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.” MPEP 2106.04(a)(2)(I). Hence, even if the claims do not recite a specific formula or equation, the claims still recite mathematical concepts in the form of calculating an edge weight and generating an embedding representation. These limitations amount to no more than mathematical calculations, as detailed below in the claim rejections under 35 U.S.C. 101.
With regards to Applicant’s assertion of a purported improvement to the technology, the claims are entirely directed to operations for generating a graph embedding, as Applicant appears to acknowledge2. An “embedding” is defined as “any representation of data that captures its relevant qualities,” and it is well-known to those of ordinary skill in the art that humans can generate embeddings in a process known as manual feature engineering3. As described in the specification at [0003] and [0059], the embedding representation itself is merely a vector or matrix. One can mentally perform a task using a vector or matrix representing a specific target graph. Given that the BRI of the claims encompasses a simple case as detailed below in the claim rejections under 35 U.S.C. 101, a human could, with the aid of pencil and paper, mentally perform the claimed generating of an embedding representation. Hence, these limitations are an abstract idea under the “Mental Processes” grouping. Alternatively, these limitations may be deemed an abstract idea under the “Mathematical Concepts” grouping since the claimed generating of an embedding representation amounts to no more than mathematical calculation(s) and/or operation(s).
With regards to the assertion that the “performing a predefined graph task using an embedding representation of the target graph” represents an improvement to the technology (remarks, pages 15-19), the claimed “predefined graph task” is broadly claimed and it encompasses a mentally performable evaluation or judgement. As described in Applicant’s specification at [0003] and [0059] the embedding representation itself is merely a vector or matrix. Furthermore, as described in Applicant’s specification at [0064] graph “tasks” can include things like “classification”, “regression” or “graph distinction,” which as described therein can be as simple as “distinguishing between two graphs.” More specifically, the specification at [0147]-[0149] further describes the “graph distinction task” as determining whether the “embedding representation” is different for a pair of graphs, and an evaluation of whether two embeddings are the same/identical. A human can mentally perform a “graph distinction task” as described using a target embedding and comparing it to another embedding to determine if it is different or identical. The claimed performance of a graph task is recited at a high level of generality, and when interpreted under the BRI, this limitation encompasses a “graph distinction task” that is a mentally-performable evaluation/judgement. Hence, the claimed performance of a predefined graph task cannot be deemed an improvement to the technology because this limitation is itself a mentally performable abstract idea.
Claims 17 and 18 recite limitations similar to those of claim 1 and are ineligible under 35 U.S.C. 101 for the same reasons that claim 1 is ineligible, as set forth above.
Claims 2, 5-12, 16, and 19-21 are ineligible under 35 U.S.C. 101 for the same reasons that claims 1 is ineligible, as set forth above, and for the additional reasons detailed below in the claim rejections under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-2, 5-12, and 16-21 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
As to claims 1-2, 5-12, and 16-21, the following limitations are newly-added:
“wherein the calculating the edge weight comprises:
generating, by using a multi-layer perceptron-based color value mapping module, a line graph corresponding to the colored graph, node color values of the line graph being determined based on color values of a node tuple connected by a corresponding edge in the colored graph, and
determining an edge weight for a first edge of the colored graph corresponding to a first node in the line graph based on an output value of a predictor that is obtained by inputting a color value of the first node;” and
“wherein the predictor is a model pre-trained using the line graph, the predictor being configured to output a score indicating whether an edge of the colored graph corresponding to an input node is an actual edge or a virtual edge, based on a color value of the input node.”
Claim 1, and similar limitations of claims 17 and 18.
Applicant has not pointed out where the new (or amended) claim(s) is supported, nor does there appear to be a written description of these claim limitations in the application as filed. Hence, these newly-added limitations are deemed to introduce new matter. See MPEP 2163.04(I)(B).
As to claims 2, 5-12, 16, and 19-21, they depend from claim 1, and these dependent claims inherit the deficiencies of their parent claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5-12, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 17, and 18, these claims recite “a colored graph” and “a target graph.” These claims do not place any limits or specifications upon the claimed graphs. The broadest reasonable interpretation (BRI) of the claimed graphs encompasses simple graphs having just a few nodes and edges. These claims recite “calculating an edge weight for the colored graph based on node color values of the colored graph.” This amounts to no more than mathematical calculation(s). Hence, this limitation is an abstract idea under the “Mathematical Concepts” grouping. Alternatively, this limitation may be deemed an abstract idea under the “Mental Processes” grouping because a human can, with the aid of pencil and paper, mentally perform the claimed “calculating” for the simple graphs encompassed by the BRI of the claims. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with a pencil and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
These claims also recite generating a line graph corresponding to the colored graph, node color values of the line graph being determined based on color values of a node tuple connected by a corresponding edge in the colored graph. For the simple graphs encompassed by the BRI of the claims, a human could draw out on a piece of paper a line graph that is as described in the claims. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite “determining an edge weight for a first edge of the colored graph corresponding to a first node in the line graph based on an output value of a predictor that is obtained by inputting a color value of the first node.” Read in light of the instant specification, the claimed “edge weight” and “output value” are understood to be numerical values. Hence, the claimed “determining” in this limitation amounts to no more than mathematical calculation(s), i.e. calculating the claimed edge weight based on the claimed output value. Hence, this limitation is an abstract idea under the “Mathematical Concepts” grouping. Alternatively, this limitation may be deemed an abstract idea under the “Mental Processes” grouping, since a human can mentally performs these calculations with the aid of pencil and paper.
These claims also recite “generating an edge filtration for the colored graph using the edge weight as a connectivity metric between nodes.” The claimed “edge filtration” is defined in the instant specification as follows: “the term ‘filtration’ refers to a collection or sequence of subgraphs that represent an evolutionary process of a graph (particularly, a simplicial complex), where the subgraphs have an inclusion relationship in one direction (i.e., an increasing direction) (see FIGS. 7 and 8 for reference)” (see para. 0076 of Applicant’s published specification). Applicant’s Figure 8, reproduced below, depicts an edge filtration:
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Given that the BRI of the claims encompasses a simple case, as set forth above, a human could mentally perform the claimed generating of an edge filtration with the aid of pencil and paper. For example, with the aid of pencil and paper, a human could mentally generate the sequence of subgraphs (claimed “edge filtration”) depicted in Applicant’s Figure 8. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite the following: “generating an embedding representation of the target graph based on topology information extracted from the edge filtration.” An “embedding” is defined as “any representation of data that captures its relevant qualities,” and it is well-known to those of ordinary skill in the art that humans can generate embeddings in a process known as manual feature engineering4. As described in the specification at [0003] and [0059], the embedding representation itself is merely a vector or matrix. One can mentally perform a task using a vector or matrix representing a specific target graph. Given that the BRI of the claims encompasses a simple case as set forth above, a human could, with the aid of pencil and paper, mentally perform the claimed generating of an embedding representation. Hence, these limitations are also an abstract idea under the “Mental Processes” grouping. Alternatively, these limitations may be deemed an abstract idea under the “Mathematical Concepts” grouping since the claimed generating of an embedding representation amounts to no more than mathematical calculation(s) and/or operation(s).
These claims also recite “performing a predefined graph task using an embedding representation of the target graph.” As described in Applicant’s specification at [0003] and [0059] the embedding representation itself is merely a vector or matrix. Furthermore, as described in Applicant’s specification at [0064] graph “tasks” can include things like “classification”, “regression” or “graph distinction,” which as described therein can be as simple as “distinguishing between two graphs.” More specifically, the specification at [0147]-[0149] further describes the “graph distinction task” as determining whether the “embedding representation” is different for a pair of graphs, and an evaluation of whether two embeddings are the same/identical. A human can mentally perform a “graph distinction task” as described using a target embedding and comparing it to another embedding to determine if it is different or identical. The claimed performance of a graph task is recited at a high level of generality, and when interpreted under the BRI, this limitation encompasses a “graph distinction task” that is a mentally-performable evaluation/judgement. Hence, the claimed performance of a graph task is also an abstract idea under the “Mental Processes” grouping. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. Other than the abstract idea, the claims recite the following:
a) “acquiring a colored graph for a target graph”;
b) use of a multi-layer perceptron5-based color value mapping module;
c) use of a neural network;
d) “wherein the predictor is a model pre-trained using the line graph, the predictor being configured to output a score indicating whether an edge of the colored graph corresponding to an input node is an actual edge or a virtual edge, based on a color value of the input node;”
e) “at least one processor”;
f) “a memory configured to store a computer program that is executed by the at least one processor”; and
g) “A non-transitory computer-readable recording medium having stored therein a
computer program.”
Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). Limitation (b) through (d) are high level, generic references to use of machine learning tools (i.e. use of a multi-layer perceptron, a neural network, and a pre-trained predictor model) and amount to generally linking the abstract idea to a particular technological environment, which cannot be deemed a practical application. See MPEP 2106.05(e). Limitations (e) through (g) are recited at a high level of generality, i.e. as generic computer components performing generic computing functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; generally linking to a technological environment; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Limitation (a) amounts to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). In addition, the courts have deemed receiving data to be well-understood, routine, and conventional activity, as in the following cases: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory). See MPEP 2106.05(d)(II). Limitation (b) through (d) are high level, generic references to use of machine learning tools (i.e. use of a multi-layer perceptron, a neural network, and a pre-trained predictor model) and amount to generally linking the abstract idea to a particular technological environment, which cannot be deemed significantly more. See MPEP 2106.05(e). As discussed above with respect to integration of the abstract idea into a practical application, additional elements (e) through (g) amount to no more than mere field of use limitations and instructions to apply the exception using generic computer components. Mere instructions to apply an exception using conventional computer components and functions cannot provide an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; generally linking to a particular technological environment; and/or generic computer implementation. Hence, the claims as a whole, looking at the additional elements individually and in combination, do not amount to significantly more than the abstract idea. These claims are not patent eligible.
As to dependent claim 2, the following is recited: “wherein the colored graph is generated by updating a color value of each node that forms the target graph, in a manner that aggregates color values of neighboring nodes.” Given that the BRI of the claims encompasses simple graphs having just a few nodes and edges, as set forth above in the discussion of the parent claims, a human can mentally perform this limitation with the aid of pencil and paper. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
As to dependent claims 5, this claim recites certain details of the training processor for the predictor model. As detailed above in the discussion of the parent claims, the use of trained predictor model is a high level, generic reference to use of a machine learning tool, and it amounts to generally linking the abstract idea to a particular technological environment. Generally linking the abstract idea to a particular technological environment cannot be deemed a practical application nor an inventive concept. See MPEP 2106.05(e).
As to dependent claims 6-9, these claims recite further details of “generating the line graph” and/or “calculating the edge weight.” Given that the BRI of the claims encompasses simple graphs having just a few nodes and edges, as set forth above in the discussion of the parent claims, a human can mentally perform the limitations of these claims with the aid of pencil and paper. Hence, the limitations of these claims are also an abstract idea under the “Mental Processes” grouping. Alternatively, these limitations may be deemed an abstract idea under the “Mathematical Concepts” grouping because they amount to no more than mathematical calculations and/or operations.
As to dependent claim 10, the following is recited: “wherein the generating the edge filtration comprises generating the edge filtration using a Vietoris-Rips filtration technique.” Given that the BRI of the claims encompasses simple graphs having just a few nodes and edges, as set forth above in the discussion of the parent claims, a human can mentally perform this limitation with the aid of pencil and paper. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping. Alternatively, this limitation may be deemed an abstract idea under the “Mathematical Concepts” grouping because it amounts to no more than mathematical calculation(s) and/or operation(s).
As to dependent claims 11-12, and 19-21, these claims recite further details of “generating the embedding representation.” Given that the BRI of the claims encompasses simple graphs having just a few nodes and edges, as set forth above in the discussion of the parent claims, a human can mentally perform the limitations of these claims with the aid of pencil and paper. Hence, the limitations of these claims are also an abstract idea under the “Mental Processes” grouping. Alternatively, these limitations may be deemed an abstract idea under the “Mathematical Concepts” grouping because they amount to no more than mathematical calculations and/or operations.
As to dependent claim 16, the following is recited: “calculating a task loss by performing a predefined task based on the generated embedding representation; and updating values of parameters involved in the generating the embedding representation
based on the task loss.” Given that the BRI of the claims encompasses simple graphs having just a few nodes and edges, as set forth above in the discussion of the parent claims, a human can mentally perform this limitation with the aid of pencil and paper. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping. Alternatively, these limitations may be deemed an abstract idea under the “Mathematical Concepts” grouping because they amount to no more than mathematical calculations and/or operations.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UMAR MIAN whose telephone number is (571)270-3970. The examiner can normally be reached Monday to Friday, 10 am to 6:30 pm.
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/Umar Mian/
Examiner, Art Unit 2163
1 A multi-layer perceptron is defined as a neural network having at least three layers: an input layer, an hidden layer and an output layer, where each layer operates on the outputs of its preceding layer.
See Yehoshua, Roi. “Multi-Layer Perceptrons.” Published 2 April 2023 by towardsdatascience.com. Accessing 2 Jan 2026 from https://towardsdatascience.com/multi-layer-perceptrons-8d76972afa2b/
2 “The application addresses technical problems in graph embedding methods in the prior art that convert a given graph into a representation (e.g., a vector or matrix representation) in an embedding space.”
Applicant’s remarks, page 15, first paragraph
3 Bergmann, David and Stryker, Cole. "What is vector embedding?" Published 12 June 2024 by IBM. Accessed 28 August 2025 from https://www.ibm.com/think/topics/vector-embedding
See pages 4 and 6.
4 Bergmann, David and Strykyer, Cole. "What is vector embedding?" Published 12 June 2024 by IBM. Accessed 28 August 2025 from https://www.ibm.com/think/topics/vector-embedding
See pages 4 and 6.
5 A multi-layer perceptron is defined as a neural network having at least three layers: an input layer, an hidden layer and an output layer, where each layer operates on the outputs of its preceding layer.
See Yehoshua, Roi. “Multi-Layer Perceptrons.” Published 2 April 2023 by towardsdatascience.com. Accessing 2 Jan 2026 from https://towardsdatascience.com/multi-layer-perceptrons-8d76972afa2b/