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.
Claims 14-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claims 1 (A method for…), and 27 (A device for) are directed towards a method, and a machine respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine, manufacture, or composition of matter).
Claim 14
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
check for iterations convergence according to a predetermined convergence criterion; and if the predetermined convergence criterion is not satisfied then:
This limitation recites a mental process, using observation, evaluation, judgment, and opinion, with aid of pen and paper, to evaluate and judge if a condition is met. See MPEP 2106.05(a)(2)(III).
apply a data dimensionality reduction on at least a part of the first parameter values of first dimensionality to compute representative second parameters of second dimensionality smaller than the first dimensionality;
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to decide a subset of parameters to use that has smaller dimensionality than the original set of parameters. See MPEP 2106.05(a)(2)(III).
apply an extrapolation on at least a subset of the second parameters of second dimensionality to predict a set of predicted second parameter values, and compute predicted first parameter values from the predicted second parameter values,
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to predict sets of values. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: The additional elements in this claim do not integrate this judicial exception into a practical application.
Additional Elements:
A method for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising the following steps, implemented by a processor of an electronic programmable device
This limitation is recited at a high level of generality and recites that a method is to be applied using a generically recited processor. Mere instruction that a judicial exception is to be applied using a generically recited processor cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
apply the iterative computation code, starting from an input data set, for a given number of iterations, to obtain first parameter values, of first dimensionality;
This limitation is recited at a high level of generality and recites applying computation code repetitively. Mere instruction that a judicial exception is to be repetitively applied cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
keep available, in a memory of said programmable device, the first parameter values for each iteration for post-processing;
This limitation is an insignificant extra-solution activity of mere data gathering. See MPEP 2106.05(g).
use the predicted first parameter values as an input data set for a new iterative computation with the iterative computation code,
This limitation is recited at a high level of generality and recites that predicted values are to be applied as input data. Mere instruction that a judicial exception is to be generically applied as input data cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
repeat steps a) to d) until the convergence according to the predetermined convergence criterion is reached.
This limitation is recited at a high level of generality and recites that steps are to be applied repetitively. Mere instruction that a judicial exception is to be applied repetitively cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A method for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising the following steps, implemented by a processor of an electronic programmable device
This limitation is recited at a high level of generality and recites that a method is to be applied using a generically recited processor. Mere instruction that a judicial exception is to be applied using a generically recited processor is not significantly more than the judicial exception. See MPEP 2106.05(f).
apply the iterative computation code, starting from an input data set, for a given number of iterations, to obtain first parameter values, of first dimensionality;
MPEP 2106.05(d)(II) Flook, 437 U.S. at 594, 198 USPQ2d at 199 indicates that performing repetitive calculations is a well-understood, routine, and conventional function, when recited in a merely generic manner, as it is in the present limitation.
keep available, in a memory of said programmable device, the first parameter values for each iteration for post-processing;
MPEP 2106.05(d)(II) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) indicates that storing and retrieving information in memory is a well-understood, routine, and conventional function, when recited in a merely generic manner, as it is in the present limitation.
use the predicted first parameter values as an input data set for a new iterative computation with the iterative computation code,
This limitation is recited at a high level of generality and recites that predicted values are to be applied as input data. Mere instruction that a judicial exception is to be generically applied as input data is not significantly more than the judicial exception. See MPEP 2106.05(f)
repeat steps a) to d) until the convergence according to the predetermined convergence criterion is reached.
This limitation is recited at a high level of generality and recites that steps are to be applied repetitively. Mere instruction that a judicial exception is to be applied repetitively is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 15
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the data dimensionality reduction comprises applying principal component analysis, and each representative second parameter is a principal component.
This limitation recites a mathematical calculation using a well-known statistical procedure to determine which parameters to keep. See MPEP 2106.05(a)(2)(I)(C).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 16
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein each principal component has an associated score, and the principal components are ordered according to decreasing associated score.
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to evaluate and order each principal component. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 17
Step 2A, Prong 1: This claim does not recite any additional judicial exceptions.
Step 2A, Prong 2: The additional elements in this claim do not integrate the judicial exception into a practical application.
Additional Elements:
wherein the data dimensionality reduction comprises applying an upstream first neural network, which is obtained by splitting an identity multi-layer neural network, comprising at least one hidden layer with a number of neurons smaller than the first dimensionality.
This limitation is recited at a high level of generality and recites application of a neural network, that has at least one hidden layer with a smaller number of neurons, obtained by generic splitting of an identity multi-layer neural network. Mere instruction that a judicial exception is to be applied using a neural network obtained by a generic splitting cannot integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the data dimensionality reduction comprises applying an upstream first neural network, which is obtained by splitting an identity multi-layer neural network, comprising at least one hidden layer with a number of neurons smaller than the first dimensionality.
This limitation is recited at a high level of generality and recites application of a neural network, that has at least one hidden layer with a smaller number of neurons, obtained by generic splitting of an identity multi-layer neural network. Mere instruction that a judicial exception is to be applied using a neural network obtained by a generic splitting is not significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 18
Step 2A, Prong 1: This claim does not recite any additional judicial exceptions.
Step 2A, Prong 2: The additional elements in this claim do not integrate this judicial exception into a practical application.
Additional Elements:
wherein the computation of predicted first parameter values from the predicted second parameter values comprises applying a downstream second neural network, obtained by splitting said identity multi-layer neural network.
This limitation is recited at a high level of generality and recites application of a neural network obtained by generic splitting of an identity multi-layer neural network. Mere instruction that a judicial exception is to be applied using a neural network obtained by a generic splitting cannot integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the computation of predicted first parameter values from the predicted second parameter values comprises applying a downstream second neural network, obtained by splitting said identity multi-layer neural network.
This limitation is recited at a high level of generality and recites application of a neural network obtained by generic splitting of an identity multi-layer neural network. Mere instruction that a judicial exception is to be applied using a neural network obtained by a generic splitting is not significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 19
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the extrapolation comprises applying auto-regressive integrated moving average.
This limitation recites a mathematical calculation of using an algorithm to calculate predicted values. See MPEP 2106.05(a)(2)(I)(C).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 20
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the extrapolation comprises applying a parameterized algorithm trained on an available database.
This limitation recites a mathematical calculation of using an algorithm to calculate predicted values. See MPEP 2106.05(a)(2)(I)(C).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 21
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the extrapolation comprises applying a computational extrapolation to predict second parameter values and store the predicted second parameter values as trajectories, …
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to predict values for parameters and use trajectories to represent them. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: The additional elements in this claim do not integrate this judicial exception into a practical application.
Additional Elements:
… and further apply training of the parameterized algorithm based on the stored trajectories.
This limitation is recited at a high level of generality and recites general training of a mathematical algorithm using the trajectories formed from the predicted values. Mere instruction that a judicial exception is to be applied for a generically stated training of a generic algorithm cannot integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
… and further apply training of the parameterized algorithm based on the stored trajectories.
This limitation is recited at a high level of generality and recites general training of a mathematical algorithm using the trajectories formed from the predicted values. Mere instruction that a judicial exception is to be applied for a generically stated training of a generic algorithm is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 22
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
after applying the data dimensionality reduction, computing a variation rate of values of at least one chosen second parameter associated to successive iterations of steps a) to d), and
This limitation recites a mathematical calculation of calculating a variation rate. See MPEP 2106.05(a)(2)(I)(C).
determining the subset of the second parameters used for extrapolation in function of said variation rate.
This limitation recites a mental process using observation, evaluation, judgment and opinion, with aid of pen and paper, to determine which parameters to use based on the calculation of variation rate. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 23
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the data dimensionality reduction is principal component analysis,
This limitation recites a mathematical concept of using a well-known statistical procedure. See MPEP 2106.05(a)(2)(I)(C).
the principal components being ordered, and
This limitation recites a mental process using observation, evaluation, judgment and opinion, with aid of pen and paper, to determine the order of selected parameters. See MPEP 2106.05(a)(2)(III)
wherein said variation rate is computed for a first principal component.
This limitation recites a mathematical calculation of calculating a variation rate for a selected parameter. See MPEP 2106.05(a)(2)(I)(C).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 24
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
wherein the determining the subset of the second parameters used for extrapolation comprises comparing the variation rate to a predetermined threshold, and selecting second parameter values associated to iterations for which the variation rate is lower than said predetermined threshold.
This limitation recites a mental process using observation, evaluation, judgment, and opinion, with aid of pen and paper, to determine the parameters used by evaluating if the calculated variation rates for the parameters are less than a predetermined value. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 25
Step 2A, Prong 1: There are no additional judicial exceptions recited in this claim.
Step 2A, Prong 2: The additional elements in this claim do not integrate the judicial exception into a practical application.
Additional Elements:
wherein the multi-parameter system is for fluid dynamics computation.
This limitation is recited at a high level of generality and indicates limiting multi-parameter system to the field of use of fluid dynamics computation. Merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the multi-parameter system is for fluid dynamics computation.
This limitation is recited at a high level of generality and indicates limiting multi-parameter system to the field of use of fluid dynamics computation. Merely indicating a field of use or technological environment in which to apply a judicial exception is not significantly more than the judicial exception. See MPEP 2106.05(h).
Claim 26
Step 2A, Prong 1: There are no additional judicial exceptions recited in this claim.
Step 2A, Prong 2: The additional elements in this claim do not integrate this judicial exception into a practical application.
Additional Elements:
A non-transitory computer readable medium including software instructions which, when executed by a programmable electronic device, carry out the method as recited in claim 14.
This limitation is recited at a high level of generality and recites application of the method recited in claim 14 on a generic non-transitory computer readable medium. Mere instruction that a judicial exception is to be applied on a generic non-transitory computer readable medium cannot integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A non-transitory computer readable medium including software instructions which, when executed by a programmable electronic device, carry out the method as recited in claim 14.
This limitation is recited at a high level of generality and recites application of the method recited in claim 14 on a generic non-transitory computer readable medium. Mere instruction that a judicial exception is to be applied on a generic non-transitory computer readable medium is not significantly more the judicial exception. See MPEP 2106.05(f).
Claim 27
Step 2A, Prong 1: The claim(s) recite(s), inter alia:
[…] check for iterations convergence according to a predetermined convergence criterion; […] the predetermined convergence criterion is not satisfied:
This limitation recites a mental process, using observation, evaluation, judgment, and opinion, with aid of pen and paper, to evaluate and judge if a condition is met. See MPEP 2106.05(a)(2)(III).
apply a data dimensionality reduction on at least a part of the first parameter values of first dimensionality to compute representative second parameters of second dimensionality smaller than the first dimensionality;
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to decide a subset of parameters to use that has smaller dimensionality than the original set of parameters. See MPEP 2106.05(a)(2)(III).
apply an extrapolation on at least a subset of the second parameters of second dimensionality to predict a set of predicted second parameter values, and compute predicted first parameter values from the predicted second parameter values,
This limitation recites a mental process, using observation, evaluation, judgment and opinion, with aid of pen and paper, to predict sets of values. See MPEP 2106.05(a)(2)(III).
Step 2A, Prong 2: The additional elements in this claim do not integrate this judicial exception into a practical application.
Additional Elements:
A device for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising at least one processor configured to implement:
This limitation is recited at a high level of generality and recites that a method is to be applied using a generically recited device. Mere instruction that a judicial exception is to be applied using a generically recited device cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
a module configured for applying the iterative computation code, starting from an input data set, for a given number of iterations, and obtaining first parameter values, of first dimensionality;
This limitation is recited at a high level of generality and recites applying computation code repetitively. Mere instruction that a judicial exception is to be repetitively applied cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
a module configured to keep available, in a memory of the device, the first parameter values for each iteration for post-processing;
This limitation is an insignificant extra-solution activity of mere data gathering. See MPEP 2106.05(g).
a module configured to […] … modules configured to […]
This limitation is recited at a high level of generality and recites that a limitation is to be applied using generically recited modules. Mere instruction that a judicial exception is to be applied using generically recited modules cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
use the predicted first parameter values as an input data set for a new iterative computation with the iterative computation code,
This limitation is recited at a high level of generality and recites that predicted values are to be applied as input data. Mere instruction that a judicial exception is to be generically applied as input data cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
wherein the modules are applied repeatedly until the converge according to the predetermined convergence criterion is reached.
This limitation is recited at a high level of generality and recites that steps are to be applied repetitively. Mere instruction that a judicial exception is to be applied repetitively cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A device for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising at least one processor configured to implement:
This limitation is recited at a high level of generality and recites that a method is to be applied using a generically recited device. Mere instruction that a judicial exception is to be applied using a generically recited device is not significantly more than the judicial exception. See MPEP 2106.05(f).
a module configured for applying the iterative computation code, starting from an input data set, for a given number of iterations, and obtaining first parameter values, of first dimensionality;
MPEP 2106.05(d)(II) Flook, 437 U.S. at 594, 198 USPQ2d at 199 indicates that performing repetitive calculations is a well-understood, routine, and conventional function, when recited in a merely generic manner, as it is in the present limitation.
a module configured to keep available, in a memory of the device, the first parameter values for each iteration for post-processing;
MPEP 2106.05(d)(II) Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) indicates that storing and retrieving information in memory is a well-understood, routine, and conventional function, when recited in a merely generic manner, as it is in the present limitation.
a module configured to […] … modules configured to […]
This limitation is recited at a high level of generality and recites that a limitation is to be applied using generically recited modules. Mere instruction that a judicial exception is to be applied using generically recited modules is not significantly more than a judicial exception. See MPEP 2106.05(f).
use the predicted first parameter values as an input data set for a new iterative computation with the iterative computation code,
This limitation is recited at a high level of generality and recites that predicted values are to be applied as input data. Mere instruction that a judicial exception is to be generically applied as input data is not significantly more than the judicial exception. See MPEP 2106.05(f)
wherein the modules are applied repeatedly until the converge according to the predetermined convergence criterion is reached.
This limitation is recited at a high level of generality and recites that steps are to be applied repetitively. Mere instruction that a judicial exception is to be applied repetitively is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 28
Step 2A, Prong 1: There are no additional judicial exceptions recited in this claim.
Step 2A, Prong 2: The additional elements in this claim do not integrate the judicial exception into a practical application.
Additional Elements:
wherein the multi-parameter system is for fluid dynamics computation.
This limitation is recited at a high level of generality and indicates limiting multi-parameter system to the field of use of fluid dynamics computation. Merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the multi-parameter system is for fluid dynamics computation.
This limitation is recited at a high level of generality and indicates limiting multi-parameter system to the field of use of fluid dynamics computation. Merely indicating a field of use or technological environment in which to apply a judicial exception is not significantly more than the judicial exception. See MPEP 2106.05(h).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 14-18, 20-24, 26, and 27 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US 20200218984 A1 by Thornton et al., hereafter Thornton.
Regarding independent claim 14, Thornton teaches:
A method for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising the following steps, implemented by a processor of an electronic programmable device:
apply (extract) the iterative computation code (a SIP feature extractor), starting from an input data set (call data), for a given number of iterations (M sets), to obtain first parameter values of first dimensionality (M extracted SIP feature sets); (Paragraph [0198])
keep available (able to be retrieved), in a memory of said programmable device (“database 128”), the first parameter values (“information associated with the identified group call signature”) for each iteration for post-processing; (Paragraph [0190])
check for iterations convergence (“until loss converges”) according to a predetermined convergence criterion (minimized loss); (Paragraph [0323])
if the predetermined convergence criterion is not satisfied then:
apply a data dimensionality reduction (“The latent layer produces the equivalent of the high variance components of a principal component analysis (PCA) of the input data”) on at least a part of the first parameter values of first dimensionality (“input vectors”) to compute representative second parameters of second dimensionality smaller than the first dimensionality (“the latent layer having the fewest neural nodes of any layer in the neural network”); (Paragraph [0149])
ii. apply an extrapolation (“reconstructs a ‘post-image’”) on at least a subset of the second parameters of second dimensionality (“neural network latent layer”) to predict a set of predicted second parameter values (links to decoder neural network nodes), and compute predicted first parameter values (“output layer neural network nodes”) from the predicted second parameter values, (Paragraph [0144])
iii. use the predicted first parameter values (output of the decoder) as an input data set for a new iterative computation with the iterative computation code (“used for future training of the neural network”), (Paragraph [0211])
repeat steps a) to d) until the convergence (“This is repeated over and over until the loss converges”) according to the predetermined convergence criterion is reached (minimized loss and “the autoencoder neural network is considered trained”). (Paragraph [0323])
Regarding claim 15, Thornton teaches the material disclosed in claim 14, and additionally teaches:
wherein the data dimensionality reduction comprises applying principal component analysis(“The latent layer produces the equivalent of the high variance components of a principal component analysis (PCA)”), and each representative second parameter (latent layer node) is a principal component. (Paragraph [0149])
Regarding claim 16, Thornton teaches the material disclosed in claim 15, and additionally teaches:
wherein each principal component has an associated score (variability), and the principal components are ordered according to decreasing associated score (“ranked in order of decreasing variability”). (Paragraph [0149])
Regarding claim 17, Thornton teaches the material disclosed in claim 14, and additionally teaches:
wherein the data dimensionality reduction comprises applying an upstream first neural network (“input layer 704” and “encoder 706” and “latent layer 708”), which is obtained by splitting an identity multi-layer neural network (“The left half of the neural network comprises the input layer 704 and encoder 706.”), comprising at least one hidden layer with a number of neurons smaller than the first dimensionality (“latent layer 708”). (Paragraph [0144])
Regarding claim 18, Thornton teaches the material disclosed in claim 17, and additionally teaches:
wherein the computation of predicted first parameter values (“output layer 712”) from the predicted second parameter values (links from latent layer neural network nodes) comprises applying a downstream second neural network (“decoder 710”), obtained by splitting said identity multi-layer neural network (“The right half of the autoencoder neural network 700 is the decoder 710.”). (Paragraph [0144])
Regarding claim 20, Thornton teaches the material disclosed in claim 14, and additionally teaches:
wherein the extrapolation (“The decoder 710 reconstructs a “post-image””) comprises applying a parameterized algorithm (“The decoder 710” The decoder is part of a neural network, which is a parameterized algorithm) trained on an available database (Paragraph [0110], “…trained with feature sets of SIP INVITEs corresponding to known good calls.”). (Paragraph [0144])
Regarding claim 21, Thornton teaches the material disclosed in claim 20, and additionally teaches:
wherein the extrapolation comprises applying a computational extrapolation (weightings from latent layer 708 to decoder 710) to predict second parameter values (decoder neural network nodes) and store the predicted second parameter values as trajectories (“arrows from the decoder 710 neural network nodes … to the output layer 712 neural network nodes”), and further apply training of the parameterized algorithm based on the stored trajectories The value or weightings of the links are determined during the training of the encoder neural network (The value or weightings of the links are determined during the training of the encoder neural network.). (Paragraph [0144])
Regarding claim 22, Thornton teaches the material disclosed in claim 14, and additionally teaches:
After applying the data dimensionality reduction, computing a variation rate (“variability”) of values of at least one chosen second parameter (principal component variable / latent layer output) associated to successive iterations of steps a) to d), and determining the subset of the second parameters used for extrapolation in function of said variation rate (“top K variables in terms of variability”). (Paragraph [0149])
Regarding claim 23, Thornton teaches the material disclosed in claim 22, and additionally teaches:
wherein the data dimensionality reduction is principal component analysis (“the latent layer produces the equivalent of the high variance components of a principal component analysis (PCA) of the input data”), the principal components being ordered (“The principal component variables can then be ranked in order of decreasing variability of the values of each variable”), and wherein said variation rate (“variability”) is computed for a first principal component (latent layer node). (Paragraph [0149])
Regarding claim 24, Thornton teaches the material disclosed in claim 22, and additionally teaches:
wherein the determining the subset of the second parameters used for extrapolation comprises comparing the variation rate (“variability”) to a predetermined threshold (“some threshold percentage of total variability”), and selecting (“must effectively “choose” the most important input layer information”) second parameter values (latent layer nodes / principal components) associated to iterations for which the variation rate (“variability) is lower than said predetermined threshold (“some threshold percentage of total variability”). (Paragraph [0149])
Regarding claim 26, Thornton teaches the method recited in claim 14, and additionally teaches:
A non-transitory computer readable medium including software instructions which, when executed by a programmable electronic device, carry out the method (Paragraph [0508], “A non-transitory computer readable medium including a first set of computer executable instructions which when executed by a processor of a group session signature determination device cause the group session signature determination device to: perform the following operations:”)
Regarding independent claim 27, Thornton teaches:
A device for accelerating a convergence of an iterative computation code of physical parameters of a multi-parameter system, comprising at least one processor configured to implement: (Paragraph [0509], “A non-transitory computer readable medium including a first set of computer executable instructions which when executed by a processor of a system or device cause the system or device to:”)
a module configured for applying (extracting) the iterative computation code (a SIP feature extractor), starting from an input data set (call data), for a given number of iterations (M sets), and obtaining first parameter values, of first dimensionality (M extracted SIP feature sets); (Paragraph [0198])
a module configured to keep available (able to be retrieved), in a memory of the device (“database 128”), the first parameter values (“information associated with the identified group call signature”) for each iteration for post-processing; (Paragraph [0190])
a module configured to check for iterations convergence (“until loss converges”) according to a predetermined convergence criterion(minimized loss); (Paragraph [0323])
modules configured to, if the predetermined convergence criterion is not satisfied:
apply a data dimensionality reduction (“The latent layer produces the equivalent of the high variance components of a principal component analysis (PCA) of the input data”) on at least a part of the first parameter values of first dimensionality to compute representative second parameters of second dimensionality smaller than the first dimensionality (“the latent layer having the fewest neural nodes of any layer in the neural network”); (Paragraph [0149])
apply an extrapolation (“reconstructs a ‘post-image’”) on at least a subset of the second parameters of second dimensionality (“neural network latent layer”) to predict a set of predicted second parameter values (links to decoder neural network nodes), and compute predicted first parameter values (“output layer neural network nodes”) from the predicted second parameter values, (Paragraph [0144])
use the predicted first parameter values (output of the decoder) as an input data set for a new iterative computation with the iterative computation code(“used for future training of the neural network”), (Paragraph [0211])
wherein the modules are applied repeatedly until the converge (“This is repeated over and over until the loss converges”) according to the predetermined convergence criterion is reached (minimized loss and “the autoencoder neural network is considered trained”). (Paragraph [0323])
Claim Rejections - 35 USC § 103
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 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.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thornton, in view of US 20190004484 A1 by Cussonneau et al., hereafter Cussonneau.
Regarding claim 19, Thornton teaches the material disclosed in claim 14.
Thornton does not explicitly disclose:
wherein the extrapolation comprises applying auto-regressive integrated moving average.
Cussonneau teaches:
wherein the extrapolation (simulation of expected values of observations) comprises applying auto-regressive integrated moving average (“seasonal auto-regressive integrated moving average technique (SARIMA)”). ((Cussonneau) Paragraph [0164])
Cussonneau and Thornton are analogous art because they use the same area of invention: dimensionality reduction for machine learning algorithms.
Thus, it would have been obvious for a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined the auto-regressive integrated moving average as taught by Cussonneau, with the prediction and reconstruction of the input parameters from a reduced dimensionality set of parameters as Thornton teaches. This would have been motivated due to the effectiveness of auto-regressive integrated moving average technique for time-series analysis. The application of the technique of auto-regressive integrated moving average as taught by Cussonneau into the prediction and reconstruction of predicted input parameters as Thornton teaches would make the method taught by Thornton more effective in relation to fields involving time-series analysis and would yield the predictable result that is the same as claim 19 of the instant application.
Claim(s) 25, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thornton in view of US 20180341248 A1 by Mehr et al., hereafter Mehr.
Regarding claim 25, Thornton teaches the material disclosed in claim 14.
Thornton does not explicitly disclose:
Wherein the multi-parameter system is for fluid dynamics computation.
Mehr teaches:
Wherein the multi-parameter system ((Mehr) Paragraph [0144], “the machine learning algorithm(s) employed … may comprise … a deep learning algorithm…”) is for fluid dynamics computation ((Mehr) Paragraph [0100], “Any of a variety of process simulation tools known to those of skill in the art may be used including, … computational fluid dynamics …”).
Mehr and Thornton are analogous art because they both use the same area of invention: multi-layer neural networks and autoencoders.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined using the multi-parameter system as Thornton teaches for fluid dynamics computation as taught by Mehr. The motivation for this would be for the effectiveness of computational fluid dynamics in assessing physical performances in simulations. Applying the multi-parameter system as Thornton teaches for a fluid dynamics computation as taught by Mehr would make a system that is more effective at assessing physical performances in simulations and would yield the predictable result that is the same as in claim 25 of the instant application.
Regarding claim 28, Thornton teaches the material disclosed in claim 27.
Thornton does not explicitly disclose:
Wherein the multi-parameter system is for fluid dynamics computation.
Mehr teaches:
Wherein the multi-parameter system ((Mehr) Paragraph [0144], “the machine learning algorithm(s) employed … may comprise … a deep learning algorithm…”) is for fluid dynamics computation ((Mehr) Paragraph [0100], “Any of a variety of process simulation tools known to those of skill in the art may be used including, … computational fluid dynamics …”).
Mehr and Thornton are analogous art because they both use the same area of invention: multi-layer neural networks and autoencoders.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined using the multi-parameter system as Thornton teaches for fluid dynamics computation as taught by Mehr. The motivation for this would be for the effectiveness of computational fluid dynamics in assessing physical performances in simulations. Applying the multi-parameter system as Thornton teaches for a fluid dynamics computation as taught by Mehr would make a system that is more effective at assessing physical performances in simulations and would yield the predictable result that is the same as in claim 28 of the instant application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to computational fluid dynamics, auto-regressive integrated moving average, and autoencoder networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN H LAI whose telephone number is (571)272-8628. The examiner can normally be reached Monday - Friday 7:30am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 5712524241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DYLAN H. LAI
Examiner
Art Unit 2144
/TAMARA T KYLE/ Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights