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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 – 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, recites abstract idea but for the recitation of generic computer components:
Regarding claim 1:
select a plurality of graphs which are simultaneously processable …
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves reviewing available graphs and selecting a subset to simultaneously process them. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
An information processing device comprising: one or more memories; and one or more processors configured to:
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
receive information on a plurality of graphs from one or more second information processing devices;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
using a graph neural network model among the plurality of graphs;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
input information on the plurality of graphs which are simultaneously processable into the graph neural network model
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
simultaneously process the information on the plurality of graphs which are simultaneously processable to acquire a processing result for each of the plurality of graphs which are simultaneously processable;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph.
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I, III and V), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II, IV and VI), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Claim 15, recite similar subject matter as claim 1, so are rejected under the same rationale.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors select the plurality of graphs which are simultaneously processable among the plurality of graphs based on resources of the information processing device.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 16, recite similar subject matter as claim 2, so is rejected under the same rationale.
Regarding claim 3, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors select the plurality of graphs which are simultaneously processable based on at least one of the number of nodes or the number of edges of each graph included in the plurality of graphs.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 17, recite similar subject matter as claim 3, so is rejected under the same rationale.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors select the plurality of graphs which are simultaneously processable based on a priority, wherein the priority includes at least one of a priority set for the one or more second information processing devices, a priority set for the information processing device, or a priority set by the one or more second information processing devices.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 18, recite similar subject matter as claim 4, so is rejected under the same rationale.
Regarding claim 5, dependent upon claim 3, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors are configured to: receive information on a first graph having a first number of nodes and information on a second graph having a second number of nodes
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
select the first graph and the second graph as the plurality of graphs which are simultaneously processable when at least a sum of the first number of nodes and the second number of nodes is a predetermined number of nodes or less.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves comparing the sum of node counts to a threshold and selecting graphs based on the comparison. See (MPEP 2106.04)).
Claim 19, recite similar subject matter as claim 5, so is rejected under the same rationale.
Regarding claim 6, dependent upon claim 5, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors are configured to: further receive information on a third graph having a third number of nodes,
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
select the first graph and the second graph as the plurality of graphs which are simultaneously processable when at least a sum of the first number of nodes, the second number of nodes, and the third number of nodes exceeds the predetermined number of nodes and the sum of the first number of nodes and the second number of nodes is the predetermined number of nodes or less,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating numerical relationship and selecting graphs that satisfy those criteria. See (MPEP 2106.04)).
inputs the information on the third graph into the graph neural network model at timing different from timing of the plurality of graphs which are simultaneously processable.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Regarding claim 7, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the graph neural network model is an NNP (Neural Network Potential) model.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 11 and 14, recite similar subject matter as claim 7, so are rejected under the same rationale.
Regarding claim 8, dependent upon claim 7, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the number of nodes comprised in each of the plurality of the graphs is a value based on a number of atoms.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 9, dependent upon claim 7, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the processing result comprises at least one of information on energy or information on force.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 10, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more processors are configured to: receive the information on the plurality of graphs from the one or more second information processing devices via one or more other information processing devices;
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
transmit the processing result to the second device which has transmitted the corresponding information of the graph via the one or more other information processing devices.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Regarding claim 12, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the information processing device comprises a plurality of devices.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 13, In step 2A prong 1,
select a first information processing device which executes arithmetic operations on the plurality of graphs
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating available information processing device and selecting one based on its suitability for executing arithmetic operations on the selected graph. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
An information processing device comprising: one or more memories; and one or more processors configured to:
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
receive information on a plurality of graphs from a third information processing device;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
… using a graph neural network model from among a plurality of first information processing devices;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
transmit the information on the plurality of graphs to the selected first information processing device; receive a processing result for each of the plurality of graphs, from the selected first information processing device; and transmit the processing result to the third information processing device;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
the information on the plurality of graphs is information on a plurality of graphs which are simultaneously processable using the graph neural network model among information on a plurality of graphs transmitted from one or more second information processing devices.
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I, III and V), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II and IV), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Claim 20, recite similar subject matter as claim 13, so is rejected under the same rationale.
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) 1, 3, 5, 10, 12 – 13, 15, 17, 19 and 20 rejected under 35 U.S.C. 103 as being unpatentable over Laszlo et al., Pub. No.: US20220284268A1 in view of Shmueli et al., Pub. No.: US11093526B2.
Regarding claim 1, Laszlo teaches: An information processing device comprising: one or more memories; and one or more processors configured to:
(Laszlo, “[0110] FIG. 8 is a block diagram of an example computer system 800 that can be used to perform operations described previously. The system 800 includes a processor 810, a memory 820 [one or more memories; and one or more processors], a storage device 830, and an input/output device 840. Each of the components 810, 820, 830, and 840 can be interconnected, for example, using a system bus 850.”)
receive information on a plurality of graphs from one or more second information processing devices;
(Laszlo, “[0076] A recurrent sub-graph neural network may process a network input over multiple (internal) time steps to generate a respective alternative representation 412 of the network input at each time step. In particular, at each time step, the sub-graph neural network may process: (i) the network input [receive information on a plurality of graphs from one or more second information processing devices], and (ii) any outputs generated by the sub-graph neural network at the preceding time step, to generate the alternative representation for the time step.”)
select a plurality of graphs
(Laszlo, “[0042] The distributed processing system 110 can divide the synaptic connectivity graph 108 into a number of sub-graphs (202A, 202B, 202C) [select a plurality of graphs], where each sub-graph (202A, 202B, 202C) corresponds to a sub-set of data defining the synaptic connectivity graph 108, e.g., a subset of nodes and edges defining the synaptic connectivity graph 108.”)
which are simultaneously processable using a graph neural network model among the plurality of graphs;
(Laszlo, “[0057] Next, the distributed processing system 110 processes the sub-graph datasets using the data processing units (308). The distributed processing system 110 can include multiple processing units, and each processing unit can be assigned at least one sub-graph dataset. The processing units can process each of the sub-graph datasets in parallel e.g., simultaneously [which are simultaneously processable using a graph neural network model among the plurality of graphs], while the processing units can finish processing at different times. Further, the distributed processing system 110 can utilize load balancing techniques to minimize the idle time of processing units.”)
input information on the plurality of graphs which are simultaneously processable into the graph neural network model and
(Laszlo, “[0076] A recurrent sub-graph neural network may process a network input [input information on the plurality of graphs which are simultaneously processable into the graph neural network model] over multiple (internal) time steps to generate a respective alternative representation 412 of the network input at each time step. In particular, at each time step, the sub-graph neural network may process: (i) the network input, and (ii) any outputs generated by the sub-graph neural network at the preceding time step, to generate the alternative representation for the time step.”)
simultaneously process the information on the plurality of graphs which are simultaneously processable
(Laszlo, “[0057] Next, the distributed processing system 110 processes the sub-graph datasets using the data processing units (308). The distributed processing system 110 can include multiple processing units, and each processing unit can be assigned at least one sub-graph dataset. The processing units can process each of the sub-graph datasets in parallel e.g., simultaneously [simultaneously process the information on the plurality of graphs which are simultaneously processable], while the processing units can finish processing at different times. Further, the distributed processing system 110 can utilize load balancing techniques to minimize the idle time of processing units.”)
Laszlo does not teach:
to acquire a processing result for each of the plurality of graphs which are simultaneously processable;
transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph
Shmueli teaches:
to acquire a processing result for each of the plurality of graphs which are simultaneously processable; and
((Shmueli, col. 4 line [31 – 40]), “distributes a search for a match to the query tree by dividing at least part of the graph database, according to a match between edge type values of the plurality of query edges and at least a portion of the plurality of unique thread identifiers, to a plurality of unique search sub-graphs and distributing the search operation in each of the plurality of unique search sub-graphs to one of the plurality of threads [to acquire a processing result for each of the plurality of graphs which are simultaneously processable], and simultaneously processes the plurality of unique search sub-graphs by the plurality of threads according to the distributing.”)
transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph.
((Shmueli, col. 5 line [36 – 45]), “The plurality of threads simultaneously processes the associated search sub-graphs and look for a match of the query following the sub-graph specification by interpreting their own thread identifiers as navigation instructions. The results from the plurality of threads are collected and aggregated [transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph] to identify a match of the query or part of it within the graph database. After completion of processing, search results including match indication and a set of one or more matching patterns are outputted. In case no match was identified an absence of match indication is outputted.”)
Shmueli and Laszlo are related to the same field of endeavor (i.e.: process optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Shmueli with teachings of Laszlo to assign processors or threads to specific sub graphs, where each subgraph is identified and processes using a unique thread identifier to improve parallelization and workload distribution. (Shmueli, Abstract)
Claim 15, recites limitations analogous to claim 1, so is rejected under the same rationale.
Regarding claim 3, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo further teaches: wherein the one or more processors select the plurality of graphs which are simultaneously processable based on at least one of the number of nodes or the number of edges of each graph included in the plurality of graphs.
(Laszlo, “… Further, the distributed processing system 110 can assign the same sub-graph dataset to multiple processing units if, for example, it is desirable to calculate multiple parameters for that particular sub-graph dataset. In this case, multiple processing units can process the same sub-graph dataset to simultaneously calculate multiple parameters for the sub-graph [wherein the one or more processors select the plurality of graphs which are simultaneously processable based on at least one of the number of nodes or the number of edges of each graph included in the plurality of graphs].”)
Claim 17, recites limitations analogous to claim 3, so is rejected under the same rationale.
Regarding claim 5, Laszlo in view of Shmueli teach the method of claim 3.
Laszlo further teaches: wherein the one or more processors are configured to: receive information on a first graph having a first number of nodes and information on a second graph having a second number of nodes, and
(Laszlo, “[0042] The distributed processing system 110 can divide the synaptic connectivity graph 108 into a number of sub-graphs (202A, 202B, 202C), where each sub-graph (202A, 202B, 202C) corresponds to a sub-set of data defining the synaptic connectivity graph 108 [wherein the one or more processors are configured to: receive information on a first graph having a first number of nodes and information on a second graph having a second number of nodes], e.g., a subset of nodes and edges defining the synaptic connectivity graph 108.”)
select the first graph and the second graph as the plurality of graphs which are simultaneously processable
(Laszlo, “[0057] Next, the distributed processing system 110 processes the sub-graph datasets using the data processing units (308). The distributed processing system 110 can include multiple processing units, and each processing unit can be assigned at least one sub-graph dataset. The processing units can process each of the sub-graph datasets in parallel e.g., simultaneously [select the first graph and the second graph as the plurality of graphs which are simultaneously processable], while the processing units can finish processing at different times. Further, the distributed processing system 110 can utilize load balancing techniques to minimize the idle time of processing units.”)
when at least a sum of the first number of nodes and the second number of nodes is a predetermined number of nodes or less.
(Laszlo, “… By processing the sub-graphs using multiple processing units and aggregating the outcomes, the processing units are able to determine graph statistics for the entire synaptic connectivity graph 108 [when at least a sum of the first number of nodes and the second number of nodes is a predetermined number of nodes or less], such as, for example, the distribution of a particular statistical parameter across the entire synaptic connectivity graph 108.”)
Claim 19, recites limitations analogous to claim 5, so is rejected under the same rationale.
Regarding claim 10, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo further teaches: wherein the one or more processors are configured to: receive the information on the plurality of graphs from the one or more second information processing devices via one or more other information processing devices; and
(Laszlo, “[0076] A recurrent sub-graph neural network may process a network input over multiple (internal) time steps to generate a respective alternative representation 412 of the network input at each time step. In particular, at each time step, the sub-graph neural network may process: (i) the network input [receive the information on the plurality of graphs from the one or more second information processing devices via one or more other information processing devices], and (ii) any outputs generated by the sub-graph neural network at the preceding time step, to generate the alternative representation for the time step.”)
Shmueli further teaches: transmit the processing result to the second device which has transmitted the corresponding information of the graph via the one or more other information processing devices.
((Shmueli, col. 5 line [36 – 45]), “The plurality of threads simultaneously processes the associated search sub-graphs and look for a match of the query following the sub-graph specification by interpreting their own thread identifiers as navigation instructions. The results from the plurality of threads are collected and aggregated [transmit the processing result to the second device which has transmitted the corresponding information of the graph via the one or more other information processing devices] to identify a match of the query or part of it within the graph database. After completion of processing, search results including match indication and a set of one or more matching patterns are outputted. In case no match was identified an absence of match indication is outputted.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Shmueli with teachings of Laszlo for the same reasons disclosed for claim 1.
Regarding claim 12, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo further teaches: wherein the information processing device comprises a plurality of devices.
(Laszlo, “[0113] The input/output device 840 provides input/output operations for the system 800. In one implementation, the input/output device 840 can include one or more network interface devices, for example, an Ethernet card, a serial communication device, for example, and RS-232 port, and/or a wireless interface device [wherein the information processing device comprises a plurality of devices], for example, and 802.11 card… ”)
Regarding claim 13, Laszlo teaches: An information processing device comprising: one or more memories; and one or more processors configured to:
receive information on a plurality of graphs from a third information processing device;
(Laszlo, “[0052] The distributed processing system 110 can assign each sub-graph (202A, 202B, 202C) to an individual processing unit (204A, 204B, 204C) [receive information on a plurality of graphs from a third information processing device] (i.e.: 204C (third information processing device)) and process each sub-graph in parallel.”)
select a first information processing device which executes arithmetic operations on the plurality of graphs using a graph neural network model from among a plurality of first information processing devices;
(Laszlo, “[0080] … For each sub-graph that is assigned to a processing unit [from among a plurality of first information processing devices], the processing unit instantiates the corresponding reservoir computing neural network, trains it by using a set of training data, and evaluates its performance on a particular machine learning task by using a set of validation data. Next, the processing unit calculates a performance measure defining a performance of the reservoir computing neural network at the machine learning task [select a first information processing device which executes arithmetic operations on the plurality of graphs using a graph neural network model].”)
transmit the information on the plurality of graphs to the selected first information processing device;
(Laszlo, “[0080] The distributed processing system 110 can assign each sub-graph to an individual processing unit (204A, 204B, 204C) [transmit the information on the plurality of graphs to the selected first information processing device]. For each sub-graph that is assigned to a processing unit, the processing unit instantiates the corresponding reservoir computing neural network, trains it by using a set of training data, and evaluates its performance on a particular machine learning task by using a set of validation data.”)
the information on the plurality of graphs is information on a plurality of graphs which are simultaneously processable using the graph neural network model among information on a plurality of graphs transmitted from one or more second information processing devices.
(Laszlo, “[0057] Next, the distributed processing system 110 processes the sub-graph datasets using the data processing units (308). The distributed processing system 110 can include multiple processing units, and each processing unit can be assigned at least one sub-graph dataset. The processing units can process each of the sub-graph datasets in parallel e.g., simultaneously [the information on the plurality of graphs is information on a plurality of graphs which are simultaneously processable using the graph neural network model among information on a plurality of graphs transmitted from one or more second information processing devices], while the processing units can finish processing at different times. Further, the distributed processing system 110 can utilize load balancing techniques to minimize the idle time of processing units.”)
Laszlo does not teach:
receive a processing result for each of the plurality of graphs, from the selected first information processing device;
transmit the processing result to the third information processing device;
Shmueli teaches:
receive a processing result for each of the plurality of graphs, from the selected first information processing device; and
((Shmueli, col. 5 line [36 – 45]), “The plurality of threads simultaneously processes the associated search sub-graphs and look for a match of the query following the sub-graph specification by interpreting their own thread identifiers as navigation instructions. The results from the plurality of threads are collected and aggregated [receive a processing result for each of the plurality of graphs, from the selected first information processing device] to identify a match of the query or part of it within the graph database. After completion of processing, search results including match indication and a set of one or more matching patterns are outputted. In case no match was identified an absence of match indication is outputted.”)
transmit the processing result to the third information processing device; and
((Shmueli, col. 5 line [36 – 45]), “The plurality of threads simultaneously processes the associated search sub-graphs and look for a match of the query following the sub-graph specification by interpreting their own thread identifiers as navigation instructions. The results from the plurality of threads are collected and aggregated [transmit the processing result to the third information processing device] to identify a match of the query or part of it within the graph database. After completion of processing, search results including match indication and a set of one or more matching patterns are outputted. In case no match was identified an absence of match indication is outputted.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Shmueli with teachings of Laszlo for the same reasons disclosed for claim 1.
Claim 20, recites limitations analogous to claim 13, so is rejected under the same rationale.
Claim(s) 2 and 16 rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Shmueli and in further view of Chi et al., "Nxgraph: An efficient graph processing system on a single machine."
Regarding claim 2, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo in view of Shmueli do not teach:
wherein the one or more processors select the plurality of graphs which are simultaneously processable among the plurality of graphs based on resources of the information processing device.
Chi teaches:
wherein the one or more processors select the plurality of graphs which are simultaneously processable among the plurality of graphs based on resources of the information processing device
(Chi, (page: 2), “Adaptive updating strategies: To reduce the amount of disk data transfer and ensure streamlined access to the disk, we propose NXgraph with three updating strategies for graph computation, Single-Phase Updating (SPU), Double-Phase Updating (DPU), and Mixed-Phase Updating (MPU). SPU applies to machines with large memory space and minimizes the amount of disk data transfer. DPU applies to machines with small memory space. MPU combines the advantages of both SPU and DPU. All these three strategies exploit streamlined disk access pattern. We quantitatively model the updating strategies and analyze how to select a proper one based on the graph size and available memory resources [select the plurality of graphs which are simultaneously processable among the plurality of graphs based on resources of the information processing device].”)
Chi, Laszlo and Shmueli are related to the same field of endeavor (i.e.: process optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Chi with teachings of Laszlo and Shmueli to add partitioning graph data into smaller graph portions to enable fine grained scheduling and parallel processing to utilize more efficiently, resulting in increased parallelism and reduce processing overhead. (Chi, Abstract)
Claim 16, recites limitations analogous to claim 2, so is rejected under the same rationale.
Claim(s) 4 and 18 rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Shmueli and in further view of Fan et al., Pub. No.: US12182633B2.
Regarding claim 4, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo in view of Shmueli do not teach:
wherein the one or more processors select the plurality of graphs which are simultaneously processable based on a priority, wherein the priority includes at least one of a priority set for the one or more second information processing devices, a priority set for the information processing device, or a priority set by the one or more second information processing devices.
Fan teaches:
wherein the one or more processors select the plurality of graphs which are simultaneously processable based on a priority,
((Fan, col. 9 [52 – 60]), “dividing graph data into multiple partitions; scheduling the partitions to be allocated to multiple processors; successively assigning the partitions to corresponding threads of the processors for computation according to a respective degree of criticality of each partition; [wherein the one or more processors select the plurality of graphs which are simultaneously processable based on a priority] determining whether an idle processor exists, a processor being the idle processor when the processor includes one or more idle threads;”)
wherein the priority includes at least one of a priority set for the one or more second information processing devices, a priority set for the information processing device, or a priority set by the one or more second information processing devices.
((Fan, col. 7 [58 – 65]), “Each thread retrieves partition data from an associated processor to perform computations, and after an execution is completed, can continue to retrieve a next partition from the processor for execution. At this time, it is necessary to evaluate a degree of criticality of each partition, determine a key partition and call for execution [wherein the priority includes at least one of a priority set for the one or more second information processing devices, a priority set for the information processing device], to allow the thread to compute the key partition first, which can reduce an execution time of the partition.”)
Fan, Laszlo and Shmueli are related to the same field of endeavor (i.e.: process optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Fan with teachings of Laszlo and Shmueli to add scheduling the graph partition among processors and threads based on processing priorities or workload conditions, including dynamically reallocating partitions from one processor to another when resource become available to improve load balancing and resource utilization across processing units. (Fan, Abstract)
Claim 18, recites limitations analogous to claim 4, so is rejected under the same rationale.
Claim(s) 7 – 9, 11 and 14 rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Shmueli and in further view of Schütt et al., "Schnet: A continuous-filter convolutional neural network for modeling quantum interactions."
Regarding claim 7, Laszlo in view of Shmueli teach the method of claim 1.
Laszlo in view of Shmueli do not teach:
wherein the graph neural network model is an NNP (Neural Network Potential) model.
Schütt teaches:
wherein the graph neural network model is an NNP (Neural Network Potential) model.
((Schütt, page: 6), “Models such as circular fingerprints [17], molecular graph convolutions or message-passing neural networks[21] [wherein the graph neural network model] for property prediction across chemical compound space are only concerned with equilibrium molecules, i.e., the special case where the forces are vanishing. They cannot be trained with forces in a similar manner, as they include discontinuities in their predicted potential energy surface caused by discrete binning or the use of one-hot encoded bond type information [is an NNP (Neural Network Potential) model].”)
Schütt, Laszlo and Shmueli are related to the same field of endeavor (i.e.: process optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Schütt with teachings of Laszlo and Shmueli to add processing graph structured data using a deep learning model configuration to learn representations of nodes and relationships within the graph, including modeling local interactions between connected entities to improve extraction of information from graph data and more accurate analysis or prediction results (Schütt, Abstract)
Claim(s) 11 and 14, recite limitations analogous to claim 4, so are rejected under the same rationale.
Regarding claim 8, Laszlo in view of Shmueli and Schütt teach the method of claim 7.
Schütt further teaches: wherein the number of nodes comprised in each of the plurality of the graphs is a value based on a number of atoms.
((Schütt, page: 4), “Molecular representation A molecule in a certain conformation can be described uniquely by a set of n atoms with nuclear charges Z = (Z1,...,Zn) and atomic positions R = (r1,...rn). Through the layers of the neural network, we represent the atoms using a tuple of features Xl = (xl 1,...xl n), with xl i ∈ RF with the number of feature maps F, the number of atoms n [wherein the number of nodes comprised in each of the plurality of the graphs is a value based on a number of atoms] and the current layer l. The representation of atom i is initialized using an embedding dependent on the atom type Zi:.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Schütt with teachings of Laszlo and Shmueli for the same reasons disclosed for claim 7.
Regarding claim 9, Laszlo in view of Shmueli and Schütt teach the method of claim 8.
Schütt further teaches: wherein the processing result comprises at least one of information on energy or information on force.
((Schütt, page: 4), “4.1 Architecture Fig. 2 shows an overview of the SchNet architecture. At each layer, the molecule is represented atom wise analogous to pixels in an image. Interactions between atoms are modeled by the three interaction blocks. The final prediction is obtained after atom-wise updates of the feature representation and pooling of the resulting atom-wise energy [wherein the processing result comprises at least one of information on energy or information on force]. In the following, we discuss the different components of the network.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Schütt with teachings of Laszlo and Shmueli for the same reasons disclosed for claim 7.
Allowable subject matter
Claim 6 objected to as being dependent upon a rejected base claim and would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action. The prior art made of record does not teach, make obvious, or suggest the claim limitations as disclosed in applicant's claims.
6. The information processing device according to claim 5, wherein the one or more processors are configured to: further receive information on a third graph having a third number of nodes, select the first graph and the second graph as the plurality of graphs which are simultaneously processable when at least a sum of the first number of nodes, the second number of nodes, and the third number of nodes exceeds the predetermined number of nodes and the sum of the first number of nodes and the second number of nodes is the predetermined number of nodes or less, and inputs the information on the third graph into the graph neural network model at timing different from timing of the plurality of graphs which are simultaneously processable.
Laszlo et al., Pub. No.: US20220284268A1.
Laszlo teaches obtaining graph data defining the synaptic connectivity graph that represents synaptic connectivity between neurons in the brain of the biological organism. The method further includes dividing the graph data defining the synaptic connectivity graph into multiple sub-graph datasets that each define a respective sub-graph of the synaptic connectivity graph. However, Laszlo does not teach receiving a third graph and determine whether it can be processed together with the first and second graphs based on a node count threshold. When the combined node count of all three graphs exceeds the predetermined limit, but the combined node count of the first and second graphs alone is within the limit, the system selects only the first and second graphs for simultaneous processing and schedules the third graph for processing at a different time.
Shmueli et al., Pub. No.: US11093526B2.
Shmueli teaches providing a plurality of threads to be executed on a plurality of processors, each the thread is associated with one of a plurality of unique thread identifiers, providing a graph database having a plurality of graph database nodes and a plurality of graph database edges, each the graph database edge represents a relationship between two of the plurality of graph database nodes. However, Shmueli does not teach receiving a third graph and determine whether it can be processed together with the first and second graphs based on a node count threshold. When the combined node count of all three graphs exceeds the predetermined limit, but the combined node count of the first and second graphs alone is within the limit, the system selects only the first and second graphs for simultaneous processing and schedules the third graph for processing at a different time.
Chi et al., "Nxgraph: An efficient graph processing system on a single machine."
Chi teaches NXgraph, an efficient graph processing system on a single machine. With the abstraction of vertex intervals and edge sub-shards, proposes the Destination-Sorted Sub-Shard (DSSS) structure to store a graph. By dividing vertices and edges into intervals and sub-shards, NXgraph ensures graph data access locality and enables fine-grained scheduling. However, Chi does not teach receiving a third graph and determine whether it can be processed together with the first and second graphs based on a node count threshold. When the combined node count of all three graphs exceeds the predetermined limit, but the combined node count of the first and second graphs alone is within the limit, the system selects only the first and second graphs for simultaneous processing and schedules the third graph for processing at a different time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liang, et al., "EnGN: A high-throughput and energy-efficient accelerator for large graph neural networks."
Liang teaches EnGN to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow.
Zheng et al., "DistDGL: Distributed graph neural network training for billion-scale graphs."
Zheng teaches DistDGL, a system for training GNNs in a mini-batch fashion on a cluster of machines. DistDGL is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner compute rule.
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/M.T.M./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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