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 .
Status of the Claims
Claims 1-20 are pending for examination.
Claims 1, 10 and 18 are independent Claims.
Claims 1-20 are rejected under 35 U.S.C. §101.
Claims 3-9, 12-17 and 20 are rejected under 35 U.S.C. §§112(b), 112(a).
Claims 1-4, 10-13 and 18-20 are rejected under 35 U.S.C. §102.
Claims 5-9 and 14-17 are rejected under 35 U.S.C. §103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-9, 12-17 and 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3, 12 and 20 disclose the formula “aji = a(hj, hj| w, β)” without disclosing what the terms mean.
Claims 4 and 13 disclose the formula “aji = a(hj, hj| w, β)” without disclosing what the terms mean.
Claims 6 and 15 disclose the formula “aji = a(hj, hj, eji| w, β)” without disclosing what the terms mean.
Claims 7 and 17 disclose the formula “a’ji = a(hj, hj, eji| w’, β’)” without disclosing what the terms mean.
Claim 12-17 and 20 recite the formulas without explanations for the terms.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph 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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 3-9, 12-17 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claims 3, 12 and 20 disclose the formula “aji = a(hj, hj| w, β)” without disclosing what the terms mean. Applicant’s specification does not disclose what “a” is in this formula.
Claims 4 and 13 disclose the formula “aji = a(hj, hj| w, β)” without disclosing what the terms mean. Applicant’s specification does not disclose what “a” is in this formula.
Claims 6 and 15 disclose the formula “aji = a(hj, hj, eji| w, β)” without disclosing what the terms mean. Applicant’s specification does not disclose what “a” is in this formula.
Claims 7 and 17 disclose the formula “a’ji = a(hj, hj, eji| w’, β’)” without disclosing what the terms mean. Applicant’s specification does not disclose what “a” is in this formula.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent Claims
As Claims 1, 10 and 18:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A system, comprising:
a memory that stores computer executable components;
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:
a convolution aggregation component that aggregates features through incoming edges, and aggregates features through outgoing edges to learn the different roles of those edges; and
a node level aggregation component that aggregates edge attributes and node features from neighborhood nodes and edges to optimize computing workload and memory; and
a generating component that generates a prediction for an input graph, or for a part of a graph such as a node (vertex), an edge, or a subgraph, based on the node attributes (features) and edge attributes that are attached to the input graph.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “a convolution aggregation component that aggregates features through incoming edges, and aggregates features through outgoing edges to learn the different roles of those edges; and
a node level aggregation component that aggregates edge attributes and node features from neighborhood nodes and edges to optimize computing workload and memory; and
a generating component that generates a prediction for an input graph, or for a part of a graph such as a node (vertex), an edge, or a subgraph, based on the node attributes (features) and edge attributes that are attached to the input graph.” is directed to a mathematical concepts group of abstract ideas. Mathematical concepts are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “a memory that stores computer executable components;
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “a memory that stores computer executable components;
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)).
The claim is directed to mathematical concepts group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As Claim 2, 11 and 19, the Claim recites “further utilizing distinct trainable functions that are applied to aggregated values of the incoming edges and outgoing edges, respectively, to form a vector as its output.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “ further utilizing distinct trainable functions that are applied to aggregated values of the incoming edges and outgoing edges, respectively, to form a vector as its output.” is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 3, 12 and 20, the Claim recites “wherein the convolution aggregation component utilizes an equation for yiIn as follows:
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”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 4 and 13, the Claim recites “wherein the convolution aggregation component utilizes an equation yiOut as follows:
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”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 5 and 14, the Claim recites “wherein the convolution aggregation component utilizes an equation for yi as follows:
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”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 6 and 15, the Claim recites “wherein the convolution aggregation component utilizes an equation for yiIn as follows:
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The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 7 and 16, the Claim recites “wherein the convolution aggregation component utilizes an equation for yiOut as follows:
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The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 8 and 17, the Claim recites “wherein the node level aggregation component utilizes an equation for yi as follow: yi = [yiIn || yiOut]”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 10-13 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (U.S. 11704541 hereinafter Li).
As Claim 1, Li teaches a system, comprising:
a memory that stores computer executable components (Li (col. 19 line 5-8), computer storage medium);
a processor that executes computer executable components stored in the memory, wherein the computer executable components (Li (col. 19 line 18-19), a programmable processor) comprise:
a convolution aggregation component that aggregates features through incoming edges, and aggregates features through outgoing edges to learn the different roles of those edges (Li (col. 13 line 44-49, col. 14 line 49-61), “for each node, the message vectors associated with incoming edges are aggregated. It will be appreciated that aggregation of message vectors associated with outgoing edges is also possible and that the aggregated message vector may be an aggregation of the message vectors associated with either incoming edges, outgoing edges or every edge connected to a node”, incoming and outgoing message vectors are calculated separately. Examiner construed that grouping incoming/outgoing edges together is “to learn the different roles of those edges”); and
a node level aggregation component that aggregates edge attributes (Li (col. 13 line 44-50, fig. 5 item 502), “for each node, the message vectors associated with incoming edges are aggregated. It will be appreciated that aggregation of message vectors associated with outgoing edges is also possible and that the aggregated message vector may be an aggregation of the message vectors associated with either incoming edges, outgoing edges or every edge connected to a node”) and node features from neighborhood nodes and edges (Li (col. 14 line 1-3, fig. 5 item 504), “a single round of information propagation obtain information regarding its local neighborhood whilst further rounds of information propagation will enable a node to obtain information from further afield in the graph”) to optimize computing workload and memory (Li (col. 8 line 47-51), “for each node, the message vectors associated with incoming edges are aggregated. It will be appreciated that aggregation of message vectors associated with outgoing edges is also possible and that the aggregated message vector may be an aggregation of the message vectors associated with either incoming edges, outgoing edges or every edge connected to a node”); and
a generating component that generates a prediction for an input graph, or for a part of a graph such as a node (vertex), an edge, or a subgraph, based on the node attributes (features) and edge attributes that are attached to the input graph (Li (col. 18 line 43-47), “In the center FIG. 6 shows a schematic illustration of a process for generating one or more probabilities based upon the graph, in particular graph level predictions for add node and add edge functions.”).
As Claim 2, Li teaches further utilizing distinct trainable functions that are applied to aggregated values of the incoming edges and outgoing edges (Li (col. 14 line 45-61), “the above aggregation is a summation of the message vectors associated with the incoming and/or outgoing edges connected to node v.”), respectively, to form a vector as its output (Li (col. 16 line 38-49), “An exemplary implementation of the edge addition neural network 102 will now be described in more detail. The schematic illustration of FIG. 6 also applies. One possible implementation has a similar form to the exemplary implementation of the node creation neural network 101 and is implemented as follows”).
As Claim 3, Li teaches wherein the convolution aggregation component utilizes an equation for yiIn as follows:
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(Li (col. 14 line 49-61), “
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” includes both incoming and outgoing edges. Li (col. 15 line 1-7), “
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” is calculated for incoming message and outgoing messages while W and U are respective weight matrix. yiIn is construed as the incoming component of Hv in the cited formula).
As Claim 4, Li teaches wherein the convolution aggregation component utilizes an equation yiOut as follows:
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(Li (col. 14 line 49-61), “
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” includes both incoming and outgoing edges. Li (col. 15 line 1-7), “
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” is calculated for incoming message and outgoing messages while W and U are respective weight matrix. yiOut is construed as the outgoing component of Hv in the cited formula).
As Claims 10-13, the Claims are rejected for the same reasons as Claim 1-4, respectively.
As Claims 18-20, the Claims are rejected for the same reasons as Claims 1-3, respectively.
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.
Claim(s) 5-9 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Virmaux et al. (U.S. 20230385615 hereinafter Virmaux).
As Claim 5, besides Claim 4, Li may not explicitly disclose wherein the convolution aggregation component utilizes an equation for yi as follows:
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.
Virmaux teaches:
wherein the convolution aggregation component utilizes an equation for yi as follows:
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(Virmaux (¶0063), “the above formulation can be extended in the case where the score function g(x) is computed multiple times in order to capture different information”).
Li discloses a system/method to predict additional edge/node to the input graph based on incoming and/or outgoing edges. Virmaux discloses a system/method to perform attention based operation on graph neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify graph network of Li instead be a graph neural network taught by Virmaux, with a reasonable expectation of success. The motivation would be to […].
As Claim 6, besides Claim 1, Li may not explicitly disclose wherein the convolution aggregation component utilizes an equation for yiIn as follows:
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Virmaux teaches:
wherein the convolution aggregation component utilizes an equation for yiIn as follows:
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(Virmaux (¶0058, ¶0059, ¶0060), “general formular is given by:
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. It is construed that aji = (xi) w(xj) and [hj||eji] = hi/xi (while xj = hj || hj)).
Li discloses a system/method to predict additional edge/node to the input graph based on incoming and/or outgoing edges. Virmaux discloses a system/method to perform attention-based operation on graph neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify graph network of Li instead be a graph neural network taught by Virmaux, with a reasonable expectation of success. The motivation would be “to enable deep graph neural networks to perform efficiently in order to capture long range interactions on graphs” (Virmaux (¶0006)).
As Claim 7, besides Claim 1, Li may not explicitly disclose wherein the convolution aggregation component utilizes an equation for yiOut as follows:
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Virmaux teaches:
wherein the convolution aggregation component utilizes an equation for yiOut as follows:
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(Virmaux (¶0058, ¶0059, ¶0060), “general formular is given by:
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; while
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. It is construed that aji = (xi) w(xj) and [hj||eji] = hi/xi (while xj = hj || hj)).
Li discloses a system/method to predict additional edge/node to the input graph based on incoming and/or outgoing edges. Virmaux discloses a system/method to perform attention-based operation on graph neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify graph network of Li instead be a graph neural network taught by Virmaux, with a reasonable expectation of success. The motivation would be “to enable deep graph neural networks to perform efficiently in order to capture long range interactions on graphs” (Virmaux (¶0006)).
As Claim 8, besides Claim 7, Li in view of Virmaux teaches wherein the node level aggregation component utilizes an equation for yi as follow: yi = [yiIn || yiOut] (Virmaux (¶0063), “the above formulation can be extended in the case where the score function g(x) is computed multiple times in order to capture different information”).
As Claim 9, besides Claim 8, Li in view of Virmaux teaches wherein the edge attribute features are concatenated for each node (Virmaux (¶0063), “the above formulation can be extended in the case where the score function g(x) is computed multiple times in order to capture different information”).
As Claim 14-17, the Claims are rejected for the same reasons as Claims 5-8, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang et al. (U.S. 2021/0081677) discloses a system/method for generating prediction using attentive graph neural network..
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
Prosecution Insights