DETAILED ACTION
Claims 1-26 are presented for examination.
This Office Action is in response to submission of documents on September 27, 2022.
Rejection of claims 1-7 and 11-26 under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Rejection of claims 1-3, 11-15, 19-22, and 24 under 35 U.S.C. 102(a)(1) as being anticipated by Huang.
Rejection of claims 4-7, 10, and 16-18 under 35 U.S.C. 103 as being obvious over Huang in view of Ahn.
Rejection of claim 23 under 35 U.S.C. 103 as being obvious over Huang in view of Kapp.
Rejection of claims 25-26 under 35 U.S.C. 103 as being obvious over Huang in view of Villegas.
Objection to claims 8 and 9 for being dependent on a rejected based claim but are otherwise allowable.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 16, 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-7 and 11-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical calculations and mental processes. The judicial exceptions are not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that reciting generic computer components is not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
A method comprising: at a device:
determining a correlation between a plurality of input coordinates;
inputting the plurality of input coordinates as well as the correlation into a machine learning environment; and
obtaining a result from the machine learning environment.
Abstract Idea: Mental Process
The limitation recites a mental process, which requires observation, evaluation, judgment, and opinion. For example, a human can mentally review a set of coordinates and, with pencil and paper, determine whether there is a relation between the coordinates. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
A method comprising: at a device:
determining a correlation between a plurality of input coordinates;
inputting the plurality of input coordinates as well as the correlation into a machine learning environment; and
obtaining a result from the machine learning environment.
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
Providing data (i.e., inputting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is inputted and therefore does not improve the functioning of a computer or another technical field. See MPEP 2106.05(d)(II).
The limitation is directed to the extra-solution activity of data gathering. The limitation does not impose meaningful limits on the claim and thus is minimally or tangentially related to the invention. See MPEP 2106.05(g).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Courts have found that reciting generic computer components that perform a judicial exception does not add significantly more to the claim. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Further, sending and receiving data has been found by courts to be insignificant extra-solution activity. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI 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 also In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982); OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93; CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites wherein the plurality of input coordinates are associated with a simulation. The limitation characterizes input data and does not add additional elements to the claim. As previously indicated, inputting coordinates is an additional element of providing data, which court have found does not amount to significantly more than the judicial exception. Accordingly, claim 2 is rejected for being directed to unpatentable subject matter.
Claim 3
Claim 3 recites wherein the correlation is determined by querying the input coordinates within a physics space. As previously indicated, determining a correlation is a mental process. “Querying” is a process that can be performed by a human, either using pencil and paper or with the aid of a generic computer. Accordingly, claim 3 is rejected for being directed to unpatentable subject matter.
Claim 4
Claim 4 recites wherein the correlation is determined by performing interpolation within a physics space. As previously indicated, determining a correlation is a judicial exception (i.e., a mental process). In light of the present claim, the step of “determining a correlation” can alternatively be characterized as a mathematical concept, which is also a judicial exception. See MPEP 2106.04(a)(2), Subsection I. In either instance, the claim does not add additional elements that integrate the judicial exception into a practical application. Accordingly, claim 4 is rejected for being directed to unpatentable subject matter.
Claim 5
Claim 5 recites wherein the physics space is created utilizing a second machine learning environment. The limitation is directed to a mathematical concept that is accomplished by a machine learning model. The mathematical concept of creating a “physics space” is an abstract idea and therefore a judicial exception. See MPEP 2106.04(a)(2), Subsection I. Performing the mathematical concepts using a machine learning environment is mere instructions to apply an exception using generically claims computer components. See MPEP 2106.05(f). Accordingly, claim 5 is rejected for being directed to unpatentable subject matter.
Claim 6
Claim 6 recites wherein the machine learning environment takes an initial condition (IC) and a boundary condition (BC) as inputs. The limitation further specifies an input type and does not add additional elements to the claim that would integrate the recited judicial exception into a practical application. Accordingly, claim 6 is rejected for being directed to unpatentable subject matter.
Claim 7
Claim 7 recites wherein the machine learning environment includes a latent grid network. The limitation further specifies a type of machine learning environment without adding additional elements to the claim. Accordingly, claim 7 is rejected for being directed to unpatentable subject matter.
Claim 11
Claim 11 recites wherein the machine learning environment is trained utilizing one or more physics model loss functions. The limitation is directed to training using a recited mathematical concept (i.e., a loss function). See MPEP 2106.04(a)(2), Subsection I. Thus, the claim recites an additional abstract idea. Accordingly, claim 11 is directed to unpatentable subject matter.
Claim 12
Claim 12 recites wherein the trained machine learning environment takes the plurality of input coordinates and the correlation as input, and outputs a solution as the result. The limitation is directed to the type of inputs that are provided to the machine learning environment and the outputs that are received from the environment. The limitations do not include additional elements and therefore do not include elements that would integrate the application into a practical application. Accordingly, claim 12 is directed to unpatentable subject matter.
Claim 13
Claim 13 recites a system that performs a method that is substantially the same as the method of claim 1. The limitations of “non-transitory memory storing instructions” and “a hardware processor” are generic computer components, which do not integrate the recited judicial exception into a practical application and are not significantly more than the recited judicial exception. Accordingly, for at least the same reasons as claim 1, claim 8 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claims 14-18
Claims 14-18 recite substantially the same imitations as claims 2-6. Accordingly, for at least the same reasons as claims 2-6, claims 14-18 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 19
Claim 19 recites “non-transitory computer-readable storage medium” that stores instructions that are substantially the same as the method of claim 1. Accordingly, for at least the same reasons as claim 1, claim 15 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 20
Claim 20 recites substantially the same imitations as claim 3. Accordingly, for at least the same reasons as claim 3, claim 20 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 21
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 21 limitations include (bolded for abstract idea identification):
Claim 21
Mapping Under Step 2A Prong 1
A method of using a trained neural network to perform a physics simulation, the method comprising, at a device:
determining, by one of a plurality of processors of the device, a correlation between a plurality of input coordinates of the physics simulation by querying the plurality of input coordinates within a physics space;
performing, by the trained neural network using one of the plurality of processors of the device, inference on the plurality of input coordinates and the correlation; and
outputting, by the trained neural network using one of the plurality of processors of the device, a result based on the performed inference.
Abstract Idea: Mental Process
The limitation recites a mental process, which requires observation, evaluation, judgment, and opinion. For example, a human can mentally review a set of coordinates and, with pencil and paper, determine whether there is a relation between the coordinates. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Inferring relationships and/or other information from data is a mental process that can be performed by a human. Reciting that it is performed by a generic neural network does not integrate the judicial exception into a practical application. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 21
Mapping Under Step 2A Prong 2
A method of using a trained neural network to perform a physics simulation, the method comprising, at a device:
determining, by one of a plurality of processors of the device, a correlation between a plurality of input coordinates of the physics simulation by querying the plurality of input coordinates within a physics space;
performing, by the trained neural network using one of the plurality of processors of the device, inference on the plurality of input coordinates and the correlation; and
outputting, by the trained neural network using one of the plurality of processors of the device, a result based on the performed inference.
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
Providing data (i.e., outputting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is inputted and therefore does not improve the functioning of a computer or another technical field. See MPEP 2106.05(d)(II).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Courts have found that reciting generic computer components that perform a judicial exception does not add significantly more to the claim. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Further, sending and receiving data has been found by courts to be insignificant extra-solution activity. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI 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 also In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982); OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93; CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).
Accordingly, claim 21 is rejected for being directed to unpatentable subject matter.
Claim 22
Claim 22 recites wherein the determination of the correlation, the performance of inference and the outputting of the result are performed utilizing the same physical processor of the plurality of processors. The claim merely indicates a generic computer component that performs the judicial exceptions. Thus, the claim is mere instructions to apply the recited exception. See MPEP 2106.05(f). Accordingly, claim 22 is directed to unpatentable subject matter.
Claim 23
Claim 23 recites wherein the determination of the correlation is performed utilizing a central processing unit (CPU) of the device, and the performance of inference and the outputting of the result are performed utilizing a graphics processing unit (GPU) of the device. The claim merely indicates a generic computer component that performs the judicial exceptions. Thus, the claim is mere instructions to apply the recited exception. See MPEP 2106.05(f). Accordingly, claim 23 is directed to unpatentable subject matter.
Claim 24
Claim 24 recites wherein the physics simulation includes a mathematical model having variables that define a state of a system at a predetermined time. The limitation is directed to a mathematical concept, which is a judicial exception, because a “mathematical model” includes one or more functions that are utilized to generate output based on provided input. See MPEP 2106.04(a)(2), Subsection I. Accordingly, claim 24 is directed to unpatentable subject matter.
Claim 25
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 21 limitations include (bolded for abstract idea identification):
Claim 21
Mapping Under Step 2A Prong 1
A method of using a trained neural network to perform a physics simulation, the method comprising: at a first device:
determining, by one of a plurality of processors of the first device, a correlation between a plurality of input coordinates of the physics simulation by querying the plurality of input coordinates within a physics space; and
at a second device physically distinct from the first device that is connected to the first device via a communications network:
performing, by the trained neural network using one of a plurality of processors of the second device, inference on the plurality of input coordinates and the correlation; and
outputting, by the trained neural network using one of the plurality of processors of the second device, a result based on the performed inference.
Abstract Idea: Mental Process
The limitation recites a mental process, which requires observation, evaluation, judgment, and opinion. For example, a human can mentally review a set of coordinates and, with pencil and paper, determine whether there is a relation between the coordinates. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Inferring relationships and/or other information from data is a mental process that can be performed by a human. Reciting that it is performed by a generic neural network does not integrate the judicial exception into a practical application. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 21
Mapping Under Step 2A Prong 2
A method of using a trained neural network to perform a physics simulation, the method comprising: at a first device:
determining, by one of a plurality of processors of the first device, a correlation between a plurality of input coordinates of the physics simulation by querying the plurality of input coordinates within a physics space; and
at a second device physically distinct from the first device that is connected to the first device via a communications network:
performing, by the trained neural network using one of a plurality of processors of the second device, inference on the plurality of input coordinates and the correlation; and
outputting, by the trained neural network using one of the plurality of processors of the second device, a result based on the performed inference.
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
Providing data (i.e., outputting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is inputted and therefore does not improve the functioning of a computer or another technical field. See MPEP 2106.05(d)(II).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Courts have found that reciting generic computer components that perform a judicial exception does not add significantly more to the claim. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Further, sending and receiving data has been found by courts to be insignificant extra-solution activity. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI 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 also In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982); OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93; CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).
Accordingly, claim 21 is rejected for being directed to unpatentable subject matter.
Claim 26
Claim 26 recites substantially the same imitations as claim 23. Accordingly, for at least the same reasons as claim 23, claim 26 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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) 1-3, 11-15, 19-22, and 24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang, et al., “ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting,” hereinafter “Huang.”
Claim 1
Huang discloses:
A method comprising: at a device: determining a correlation between a plurality of input coordinates;
Three types of information are used to characterize each node….(3) Lateral Info Ł(t,i,j) : an embedding vector (dashed dot-square set) capturing interaction (lateral info) between each node and its neighbors; Huang at pg. 3, Fig. 2.
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The coupling between nodes is a “correlation.”
inputting the plurality of input coordinates as well as the correlation into a machine learning environment; and
FN receives 1) dynamic data, which is subject to prediction and changes over time, 2) static information, which stays constant and characterizes the location of each FN, and 3) lateral information from neighbors. Huang at pg. 3, cols. 1-2.
“Static information” is analogous to input coordinates and “lateral information from neighbors” is analogous to correlations.
obtaining a result from the machine learning environment.
The output of each FN includes predicted dynamics and additional lateral information that will be interacted with its neighbors. Huang at pg. 3, col. 2.
Claim 2
Huang discloses:
wherein the plurality of input coordinates are associated with a simulation.
As illustrated in Fig. 5, single-waves are propagating outwards, where waves are reflected at borders such that wave fronts become interactive. Huang at pg. 6, col. 2.
Fig. 5 illustrates a simulation over time.
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Claim 3
Huang discloses:
wherein the correlation is determined by querying the input coordinates within a physics space.
We validate the hypothesis that the PDE-learning-net and ODE-informed-net are able to uncover the underlying hidden physics from raw data, and thus, are able to assist the spatio-temporal networks to capture the dynamics of the natural phenomenon. Huang at pg. 7, col. 2- pg. 8, col. 1.
Claim 11
Huang discloses:
wherein the machine learning environment is trained utilizing one or more physics model loss functions.
This approximation is optimized by a combination of multiple loss terms to place emphasis on different parts of the model… Huang at pg. 4, col. 2.
Claim 12
Huang discloses:
wherein the trained machine learning environment takes the plurality of input coordinates and the correlation as input, and outputs a solution as the result.
FN receives 1) dynamic data, which is subject to prediction and changes over time, 2) static information, which stays constant and characterizes the location of each FN, and 3) lateral information from neighbors. The output of each FN includes predicted dynamics and additional lateral information that will be interacted with its neighbors. Huang at pg. 3, cols. 1-2.
The “lateral information” is a “correlation.”
Claims 13-15
Claims 13-15 recite a system comprising:
non-transitory memory storing instructions; and a hardware processor in communication with the non-transitory memory,
The implementation was based on the Pytorch equipped with NVIDIA Geforce GTX 1080Ti and Titan Xp GPU with 32GB memory. Huang at pg. 7, col. 2.
instructions that share substantially the same steps as the method disclosed in claims 1-3. Accordingly, for at least the same reasons and based on the same prior art as claims 1-3, claims 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang.
Claims 19-20
Claims 19-20 recite a non-transitory medium that stores instructions that are substantially the same as the steps stored by the non-transitory memory of claims 13 and 15. Accordingly, for at least the same reasons and based on the same prior art as claim 13 and 15, claims 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang.
Claim 21
Huang discloses:
A method of using a trained neural network to perform a physics simulation, the method comprising, at a device:
By coupling three networks to learn underlying physics, enable transition of node interaction, and forecast the future, ST-PCNN shows superior performance to baseline models. The key contribution of the paper, compared to existing research in the field, is three-fold: 1) A novel ST-PCNN framework capturing complex localized spatial-temporal correlations; 2) A 2D PDE-learningnet and ODE-informed-net as the physics nets to learn hidden physics; and 3) The physics-coupled neural network (PCNN) for long-term forecasting using only limited observations. Further, ST-PCNN is a general framework suitable for many other physical processes modeling. Huang at pg. 9, col. 2.
determining, by one of a plurality of processors of the device, a correlation between a plurality of input coordinates of the physics simulation by querying the plurality of input coordinates within a physics space;
In this paper, we propose a spatio-temporal physics coupled neural networks (ST-PCNN) model to capture spatio-temporal correlations, and heterogeneity and its inherited homogeneity in spatially distributed manner. Huang at pg. 4, col. 1.
performing, by the trained neural network using one of the plurality of processors of the device, inference on the plurality of input coordinates and the correlation; and
Homogeneity and heterogeneity are two key characteristics of dynamical systems, but governed by different modules. We need to have a new way to enable the learning of physics, and use the inferred physics to further guide the spatio-temporal learning with robust prediction, regardless of physical plausibility. Huang at pg. 2, col. 1.
outputting, by the trained neural network using one of the plurality of processors of the device, a result based on the performed inference.
The output of each FN includes predicted dynamics and additional lateral information that will be interacted with its neighbors. Huang at pg. 3, col. 2.
Claim 22
Huang discloses:
wherein the determination of the correlation, the performance of inference and the outputting of the result are performed utilizing the same physical processor of the plurality of processors.
The implementation was based on the Pytorch equipped with NVIDIA Geforce GTX 1080Ti and Titan Xp GPU with 32GB memory. Huang at pg. 7, col. 2.
Claim 24
Huang discloses:
wherein the physics simulation includes a mathematical model having variables that define a state of a system at a predetermined time.
…a third network PN is developed to reveal unknown governing physics from pre-given spatio-temporal data and vice versa facilitates the overall model to capture the homogeneity. Huang at pg. 2, col. 2.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-7, 10, and 16-18 are rejected under 35 U.S.C. 103 as being obvious over Huang in view of Ahn, et al., “A Machine Learning-Based Approach for Spatial Estimation Using the Spatial Features of Coordinate Information,” hereinafter Ahn.
Claim 4
Huang does not appear to disclose:
wherein the correlation is determined by performing interpolation within a physics space.
Ahn, which is analogous art, discloses:
wherein the correlation is determined by performing interpolation within a physics space.
As with the ordinary Kriging process, variogram modelling should be conducted using the transformed indicators. The calculated indicator values are converted to the spatial attribute values using the conditional cumulative probability distribution based on each threshold value. Values between the threshold values are mainly calculated using linear interpolation… Ahn at pp. 2-3.
Ahn is analogous art to the claimed invention because both are directed to using correlations between inputs as input to a machine learning model to generate output. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine interpolations between input values in a physics space as the correlation in Huang. Motivation to combine includes improved quality of output by utilizing linear correlations between input values as input to the machine learning model.
Claim 5
Huang discloses:
wherein the physics space is created utilizing a second machine learning environment.
In order to incorporate physics laws with which an effective model is supposed to follow and helps training with less samples and being robust to unseen data, a third network PN is developed to reveal unknown governing physics from pre-given spatio-temporal data and vice versa facilitates the overall model to capture the homogeneity. Huang at pg. 2, col. 2.
Claim 6
Huang discloses:
wherein the machine learning environment takes an initial condition (IC) and a boundary condition (BC) as inputs.
Both the boundary conditions (when x < 0 or x > f ieldwidth, analogously for y) and initial condition (in time step 0) are treated as zero. The following variable choices were met: ∆t = 0.1, ∆x = ∆y = 1 and c = 3.0. The field was initialized using a Gaussian distribution:
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with amplitude factor a = 0.34, wave width in x and y directions σ 2 x = σ 2 y = 0.5, and sx, sy being the starting point or center of the circular wave. Huang at pg. 7, col. 1.
Claim 7
Huang discloses:
wherein the machine learning environment includes a latent grid network.
Model inputs are a set of derivatives of the target function, the current state, and the previous state. This is then encoded into the latent space, h1… Huang at pg. 5, col. 2.
Claim 10
Huang discloses:
wherein: the latent grid network performs one or more operations in a spatial domain, and one or more operations in a frequency domain, the spatial domain results and the frequency domain results are combined,
Based on above analysis, we describe the ST-PCNN to model the heterogeneous properties of spatio-temporal data and to reveal the homogeneous physics from raw data. Here, a stacking coupling mechanism is proposed to integrate the obtained physics into the spatio-temporal learning. Huang at pg. 6, cols. 1-2.
the combined domain results are decoded, and results of the decoding are upsampled to determine a physics space.
Our proposed STPCNN includes a physics-aware module, the physics network PN, to learn underlying hidden physics… ODE-informed-net: implicitly approximating time dependence with neural network and solving via an ordinary differential equation (ODE) solver. Huang at pg. 4, col. 1.
Fig 4: Diagram of the ODE-informed-net model structure with ODE-solver integrated. The neural network consists of an Encoder uE, an ODE-function uO, and a Decoder uD. Huang at pg. 5, col. 2.
Claims 16-18
Claims 16-18 recite limitations that are substantially the same as those recited in claims 4-6. Accordingly, for at least the same reasons, claims 16-18 are rejected under 35 U.S.C. 103 as being obvious over Huang in view of Ahn.
Claim 23 is rejected under 35 U.S.C. 103 as being obvious over Huang in view of Kapp, et al., U.S. Pat Pub No. 2021/0078735, hereinafter Kapp.
Claim 23
Huang does not appear to disclose:
wherein the determination of the correlation is performed utilizing a central processing unit (CPU) of the device, and the performance of inference and the outputting of the result are performed utilizing a graphics processing unit (GPU) of the device.
Kapp, which is analogous art to the claimed invention, discloses:
wherein the determination of the correlation is performed utilizing a central processing unit (CPU) of the device, and the performance of inference and the outputting of the result are performed utilizing a graphics processing unit (GPU) of the device.
Pre-processed data is then passed from the CPU to a GPU 108 in one embodiment of the method of the present disclosure. Kapp at [0077].
Kapp is analogous art to the claimed invention because both are directed to utilizing both GPUs and CPUs to perform portions of a machine learning process. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to separate the correlation and inference of a process on two different processors to result in a system that uses a CPU to perform correlations and interference utilizing a GPU. Motivation to combine includes improving the performance of the model by assigning tasks to a type of processors that can more efficiently perform inference operations rather than performing the operations on a CPU.
Claim 25 is rejected under 35 U.S.C. 103 as being obvious over Huang in view of Villegas, et al., U.S. Pat. Pub No. 2020/0082248, hereinafter Villegas.
Claim 25
Huang does not appear to disclose:
a first device and a second device
Villegas, which is analogous art to the claimed invention, discloses:
a first device and a second device
FIG. 5D is a system diagram for communication between cloud-based server(s) and the example autonomous vehicle 500 of FIG. 5A, in accordance with some embodiments of the present disclosure. The system 576 may include server(s) 578, network(s) 590, and vehicles, including the vehicle 500. The server(s) 578 may include a plurality of GPUs 584(A)-584(H) (collectively referred to herein as GPUs 584), PCIe switches 582(A)-582(H) (collectively referred to herein as PCIe switches 582), and/or CPUs 580(A)-580(B) (collectively referred to herein as CPUs 580). The GPUs 584, the CPUs 580, and the PCIe switches may be interconnected with high-speed interconnects such as, for example and without limitation, NVLink interfaces 588 developed by NVIDIA and/or PCIe connections 586. In some examples, the GPUs 584 are connected via NVLink and/or NVSwitch SoC and the GPUs 584 and the PCIe switches 582 are connected via PCIe interconnects. Although eight GPUs 584, two CPUs 580, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the server(s) 578 may include any number of GPUs 584, CPUs 580, and/or PCIe switches. For example, the server(s) 578 may each include eight, sixteen, thirty-two, and/or more GPUs 584. Villegas at [0184].
that perform substantially the same steps as performed by the device recited in claim 21. Accordingly, for at least the same reasons, claim 25 is rejected under 35 U.S.C. 103 as being obvious over Huang in view of Villegas.
Villegas is analogous art to the claimed invention because both are directed to performing simulations and machine learning processes on separate devices. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to perform the CPU operations and GPU operations on separate devices, as disclosed in Villegas, to result in a distributed computing environment to perform the processes of Huang. Motivation to combine includes improving the versatility of the system by allowing at least a portion of the operations to be performed on a second computing device, thereby allowing the system to be more compartmentalized and allow for greater flexibility in utilizing a portion of the analysis (e.g., correlation on a first device) and then selecting a separate device to perform additional analysis (e.g., inference on a second device). Thus, the user has more choice as to what devices to utilize in performing the analysis based on desired results.
Claim 26 is rejected under 35 U.S.C. 103 as being obvious over Huang in view of Kapp and Villegas.
Claim 26
Claim 26 recites “a first device and a second device,” as recited in claim 25, that perform substantially the same steps as performed by the device recited in claim 23. Accordingly, for at least the same reasons, claim 26 is rejected under 35 U.S.C. 103 as being obvious over Huang in view of Kapp and Villegas.
Allowable Subject Matter
Claims 8 and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Weiwel, “Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow”
Hengl, et al., “Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables”
Coco, et al., “Statistical Physics and Representations in Real and Artificial Neural Networks”
Eslamibidgoli, et al., “Recurrent Neural Network-Based Model For Accelerated Trajectory Analysis In Aimd Simulations”
Communication
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188