Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The present application is being examined under the claims filed on 10/27/2022.
Claims 1-18 are rejected.
Claims 1-18 are pending.
Drawings
The Drawings filed on 10/27/2022 are acceptable for examination purposes.
Specification
The Specification filed on 10/27/2022 is acceptable for examination purposes.
Prior Art References
The short names that are used to identify the references of prior art in the analysis that follows are:
Short Name
Reference
Reddy
Reddy, K.R.L., Babu, G.R. and Kishore, L., 2010. Face recognition based on eigen features of multi scaled face components and an artificial neural network. Procedia computer science, 2, pp.62-74.
Hu
Hu, X. and Yao, F., 2022. Dynamic principal component analysis in high dimensions. Journal of the American Statistical Association, 119(545), pp.308-319.
Kaplunovich
Kaplunovich, A. and Yesha, Y., 2020, December. Automatic tuning of hyperparameters for neural networks in serverless cloud. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 2751-2756). IEEE.
Mantovani
Mantovani, R.G., Horváth, T., Cerri, R., Junior, S.B., Vanschoren, J. and de Carvalho, A.D.L., 2018. An empirical study on hyperparameter tuning of decision trees. arXiv preprint arXiv:1812.02207.
Heinrich
US 20150102216 A1 - Classification Generation Method Using Combination Of Mini-Classifiers With Regularization And Uses Thereof
Torok
Toeroek, C., 1997. On the estimation of the degree of regression polynomial (No. JINR-E--5-97-254). Laboratory of Computing Techniques and Automation, Joint Institute for Nuclear Research, Dubna (Russian Federation).
Palmer
Palmer, E.M. and Schwenk, A.J., 1979. On the number of trees in a random forest. Journal of Combinatorial Theory, Series B, 27(2), pp.109-121.
Dongen
van Dongen, B.F., Crooy, R.A. and van der Aalst, W.M., 2008, November. Cycle time prediction: When will this case finally be finished?. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 319-336). Berlin, Heidelberg: Springer Berlin Heidelberg.
Azeem
Azeem, S.A. and Sharma, S.K., 2017. Study of converged infrastructure & hyper converge infrastructre as future of data centre. International Journal of Advanced Research in Computer Science, 8(5), pp.900-903.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. This judicial exception is not integrated into a practical application as outlined in the 2-step analyses for each claim that follows.
In reference to claim 1.
Step 1 – Statutory Subject Matter:
The claim is directed towards a machine:
“1. An information handling system comprising: at least one processor; and a non-transitory memory coupled to the at least one processor; wherein the information handling system is configured to:”
Step 2A Prong 1 – Judicial Exception:
The claim recites an abstract idea in the form of a mental process:
“perform principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“in response to a change in the plurality of variables, dynamically update the reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“and determine at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
Step 2A Prong 2 – Additional Elements:
The claim recites the following additional element:
“receive information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
Step 2B – Significantly More:
The following element does not amount to significantly more:
“receive information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 2.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the deep learning task:
“2. The information handling system of claim 1, wherein the machine learning task is a deep learning task.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 3.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the task being performed:
“3. The information handling system of claim 1, wherein the machine learning task is implemented via a neural network.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 4.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the type of hyperparameter:
“4. The information handling system of claim 1, wherein the at least one hyperparameter is selected from the group consisting of a logistic regression penalty, a stochastic gradient descent loss, a degree of a polynomial for a linear model, a maximum depth for a decision tree, a minimum number of samples for a leaf node in a decision tree, a number of trees in a random forest, a number of neurons in a neural network layer, a number of layers in a neural network, and a gradient descent learning rate.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 5.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the target variable:
“5. The information handling system of claim 1, wherein the target variable is a time required for a lifecycle management event.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 6.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the compute node’s existence in a cluster:
“6. The information handling system of claim 1, wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 7.
Step 1 – Statutory Subject Matter:
The claim is directed towards a method:
“7. A method comprising:”
Step 2A Prong 1 – Judicial Exception:
The claim recites an abstract idea in the form of a mental process:
“the information handling system performing principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“in response to a change in the plurality of variables, the information handling system dynamically updating the reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“and the information handling system determining at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
Step 2A Prong 2 – Additional Elements:
The claim recites the following additional element:
“an information handling system receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
Step 2B – Significantly More:
The following element does not amount to significantly more:
“an information handling system receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 8.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the deep learning task:
“8. The method of claim 7, wherein the machine learning task is a deep learning task.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 9.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the task being performed:
“9. The method of claim 7, wherein the machine learning task is implemented via a neural network.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 10.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the type of hyperparameter:
“10. The method of claim 7, wherein the at least one hyperparameter is selected from the group consisting of a logistic regression penalty, a stochastic gradient descent loss, a degree of a polynomial for a linear model, a maximum depth for a decision tree, a minimum number of samples for a leaf node in a decision tree, a number of trees in a random forest, a number of neurons in a neural network layer, a number of layers in a neural network, and a gradient descent learning rate.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 11.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the target variable:
“11. The method of claim 7, wherein the target variable is a time required for a lifecycle management event.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 12.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the compute node’s existence in a cluster:
“12. The method of claim 11, wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 13.
Step 1 – Statutory Subject Matter:
The claim is directed towards a manufacture:
“13. An article of manufacture comprising a non-transitory, computer-readable medium having computer-executable code thereon that is executable by an information handling system for:”
Step 2A Prong 1 – Judicial Exception:
The claim recites an abstract idea in the form of a mental process:
“performing principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“in response to a change in the plurality of variables, dynamically updating the reduced set of variables;” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
“and determining at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
Step 2A Prong 2 – Additional Elements:
The claim recites the following additional element:
“receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
Step 2B – Significantly More:
The following element does not amount to significantly more:
“receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 14.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the deep learning task:
“14. The article of claim 13, wherein the machine learning task is a deep learning task.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 15.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the task being performed:
“15. The article of claim 13, wherein the machine learning task is implemented via a neural network.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 16.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the type of hyperparameter:
“16. The article of claim 13, wherein the at least one hyperparameter is selected from the group consisting of a logistic regression penalty, a stochastic gradient descent loss, a degree of a polynomial for a linear model, a maximum depth for a decision tree, a minimum number of samples for a leaf node in a decision tree, a number of trees in a random forest, a number of neurons in a neural network layer, a number of layers in a neural network, and a gradient descent learning rate.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 17.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the nature of the target variable:
“17. The article of claim 13, wherein the target variable is a time required for a lifecycle management event.”
Thus, the claim is directed to an abstract idea without significantly more.
In reference to claim 18.
The claim recites the same abstract idea of the parent claim and only provides further details regarding the compute node’s existence in a cluster:
“18. The article of claim 17, wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.”
Thus, the claim is directed to an abstract idea without significantly more.
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.
Claims 1-18 are rejected under 35 U.S.C. 103.
Reddy, Hu, Kaplunovich.
Claims 1-3, 7-9, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Hu in further view of Kaplunovich.
In reference to claim 1.
“1. An information handling system comprising: at least one processor; and a non-transitory memory coupled to the at least one processor; wherein the information handling system is configured to:” (preamble)
Reddy teaches:
“receive information regarding a set of variables relating to a machine learning task for analyzing a target variable;” (Reddy Figure 5.1, “Face Data Base”)
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“perform principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” (Reddy Figure 5.1, “Projecting feature vectors on PCA/LDA space to find weight”)
Hu teaches:
“in response to a change in the plurality of variables, dynamically update the reduced set of variables;” (Hu 6, “One advantage of DPCA is the ability to capture the dynamic information contained in data […] The problem (2) is reduced to perform multivariate PCA at each t”, As time t changes so does the data which PCA is being performed on. DPCA dynamically updates the reduced set of variables.)
Motivation to combine Reddy, Hu.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy and Hu. Reddy discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network. Hu discloses an improved version of PCA that dynamically varies with time. One would be motivated to combine these references because the disclosure of Hu provides an obvious improvement on the PCA of Reddy. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (E) Obvious to try – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Kaplunovich teaches:
“and determine at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” (Kaplunovich 2751, “We decided to create a tool that will automatically optimize hyperparameters on assorted supervised neural networks (CNN, RNN, ANN, etc.). It is important that the whole process of the model hyperparameters detection and optimization is automatic.”)
Motivation to combine Reddy, Hu, Kaplunovich.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu and Kaplunovich. Reddy, Hu discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network. Kaplunovich discloses hyperparameter optimization strategies for a neural network. One would be motivated to combine these references because the disclosure of Kaplunovich provides an obvious improvement to Reddy and Hu because Kaplunovich provides a method of hyperparameter optimization. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art.
In reference to claim 2.
“2. The information handling system of claim 1,” (preamble)
Kaplunovich teaches:
“wherein the machine learning task is a deep learning task.” (Kaplunovich TABLE I, The table teaches a number of deep learning architectures aimed at learning for a specific task)
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In reference to claim 3.
“3. The information handling system of claim 1,” (preamble)
Reddy teaches:
“wherein the machine learning task is implemented via a neural network.” (Reddy Figure 5.1, “ANN Training for face classification”)
In reference to claim 7.
“7. A method comprising:” (preamble)
Reddy teaches:
“an information handling system receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” (Reddy Figure 5.1, “Face Data Base”)
“the information handling system performing principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” (Reddy Figure 5.1, “Projecting feature vectors on PCA/LDA space to find weight”)
Hu teaches:
“in response to a change in the plurality of variables, the information handling system dynamically updating the reduced set of variables;” (Hu 6, “One advantage of DPCA is the ability to capture the dynamic information contained in data […] The problem (2) is reduced to perform multivariate PCA at each t”, As time t changes so does the data which PCA is being performed on. DPCA dynamically updates the reduced set of variables.)
Kaplunovich teaches:
“and the information handling system determining at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” (Kaplunovich 2751, “We decided to create a tool that will automatically optimize hyperparameters on assorted supervised neural networks (CNN, RNN, ANN, etc.). It is important that the whole process of the model hyperparameters detection and optimization is automatic.”)
In reference to claim 8.
“8. The method of claim 7,” (preamble)
Kaplunovich teaches:
“wherein the machine learning task is a deep learning task.” (Kaplunovich TABLE I, The table teaches a number of deep learning architectures aimed at learning for a specific task)
In reference to claim 9.
“9. The method of claim 7,” (preamble)
Reddy teaches:
“wherein the machine learning task is implemented via a neural network.” (Reddy Figure 5.1, “ANN Training for face classification”)
In reference to claim 13.
“13. An article of manufacture comprising a non-transitory, computer-readable medium having computer-executable code thereon that is executable by an information handling system for:” (preamble)
Reddy teaches:
“receiving information regarding a set of variables relating to a machine learning task for analyzing a target variable;” (Reddy Figure 5.1, “Face Data Base”)
“performing principal component analysis (PCA) on the set of variables to determine a reduced set of variables;” (Reddy Figure 5.1, “Projecting feature vectors on PCA/LDA space to find weight”)
Hu teaches:
“in response to a change in the plurality of variables, dynamically updating the reduced set of variables;” (Hu 6, “One advantage of DPCA is the ability to capture the dynamic information contained in data […] The problem (2) is reduced to perform multivariate PCA at each t”, As time t changes so does the data which PCA is being performed on. DPCA dynamically updates the reduced set of variables.)
Kaplunovich teaches:
“and determining at least one hyperparameter for the machine learning task based on the dynamically updated reduced set of variables.” (Kaplunovich 2751, “We decided to create a tool that will automatically optimize hyperparameters on assorted supervised neural networks (CNN, RNN, ANN, etc.). It is important that the whole process of the model hyperparameters detection and optimization is automatic.”)
In reference to claim 14.
“14. The article of claim 13,” (preamble)
Kaplunovich teaches:
“wherein the machine learning task is a deep learning task.” (Kaplunovich TABLE I, The table teaches a number of deep learning architectures aimed at learning for a specific task)
In reference to claim 15.
“15. The article of claim 13,” (preamble)
Reddy teaches:
“wherein the machine learning task is implemented via a neural network.” (Reddy Figure 5.1, “ANN Training for face classification”)
Reddy, Hu, Kaplunovich, Mantovani, Heinrich, Torok, Palmer.
Claims 4, 10, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Hu in further view of Kaplunovich in further view of Mantovani in further view of Heinrich in further view of Torok in further view of Palmer.
In reference to claim 4.
“4. The information handling system of claim 1,” (preamble)
Kaplunovich teaches:
“a stochastic gradient descent loss,” (Kaplunovich 2751, “Optimization function”)
“a number of neurons in a neural network layer,” (Kaplunovich 2751, “Number of neurons”)
“a number of layers in a neural network,” (Kaplunovich 2751, “Number of layers”)
“and a gradient descent learning rate.” (Kaplunovich 2751, “Learning rate”)
Mantovani teaches:
“a maximum depth for a decision tree,” (Mantovani 6, “the maximum depth of any node of the final tree (‘maxdepth’);”)
“a minimum number of samples for a leaf node in a decision tree,” (Mantovani 6, “This hyperparameter controls the minimum number of instances necessary for a split to be attempted.”)
Motivation to combine Reddy, Hu, Kaplunovich, Mantovani.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu, Kaplunovich, and Mantovani. Reddy, Hu, Kaplunovich discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network and an optimization strategy for the hyperparameters of the neural network. Mantovani discloses hyperparameter optimization strategies for decision trees. One would be motivated to combine these references because the disclosure of Mantovani provides an alternative machine learning model to the neural network of Reddy which may be more suitable depending on the application. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art.
Heinrich teaches:
“logistic regression penalty,” (Heinrich [0066-0067], “[0066] Other methods for performing the regularized combination method in step (d) that could be used include: [0067] Logistic regression with a penalty function like ridge regression”)
Motivation to combine Reddy, Hu, Kaplunovich, Mantovani, Heinrich.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu, Kaplunovich, Mantovani, and Heinrich. Reddy, Hu, Kaplunovich, Mantovani discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network and an optimization strategy for the hyperparameters of the neural network. Heinrich discloses hyperparameter regularization strategies for logistic regression. One would be motivated to combine these references because the disclosure of Heinrich provides an alternative machine learning model to the neural network of Reddy which may be more suitable depending on the application. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art.
Torok teaches:
“a degree of a polynomial for a linear model,” (Torok 1, “Polynomial models are primarily used as means to fit a relatively smooth curve to a set of data but the degree of polynomial required to fit a set of data is not usually known a priori. There are several approaches to determining the right degree of the polynomial.”)
Motivation to combine Reddy, Hu, Kaplunovich, Mantovani, Heinrich, Torok.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu, Kaplunovich, Mantovani, Heinrich, and Torok. Reddy, Hu, Kaplunovich, Mantovani, Heinrich discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network and an optimization strategy for the hyperparameters of the neural network. Torok discloses hyperparameter estimation strategies for polynomial regression. One would be motivated to combine these references because the disclosure of Torok provides an alternative machine learning model to the neural network of Reddy which may be more suitable depending on the application. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art.
Palmer teaches:
“a number of trees in a random forest,” (Palmer 4, “The number of trees in a forest is a parameter that is not tunable in the classical sense but should be set sufficient high”)
Motivation to combine Reddy, Hu, Kaplunovich, Mantovani, Heinrich, Torok, Palmer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu, Kaplunovich, Mantovani, Heinrich, Torok, Palmer. Reddy, Hu, Kaplunovich, Mantovani, Heinrich, Torok discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network and an optimization strategy for the hyperparameters of the neural network. Palmer discloses estimation strategies for the number of trees in a random forest. One would be motivated to combine these references because the disclosure of Palmer provides an alternative machine learning model to the neural network of Reddy which may be more suitable depending on the application. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art.
In reference to claim 10.
“10. The method of claim 7,” (preamble)
Kaplunovich teaches:
“a stochastic gradient descent loss,” (Kaplunovich 2751, “Optimization function”)
“a number of neurons in a neural network layer,” (Kaplunovich 2751, “Number of neurons”)
“a number of layers in a neural network,” (Kaplunovich 2751, “Number of layers”)
“and a gradient descent learning rate.” (Kaplunovich 2751, “Learning rate”)
Mantovani teaches:
“a maximum depth for a decision tree,” (Mantovani 6, “the maximum depth of any node of the final tree (‘maxdepth’);”)
“a minimum number of samples for a leaf node in a decision tree,” (Mantovani 6, “This hyperparameter controls the minimum number of instances necessary for a split to be attempted.”)
Heinrich teaches:
“logistic regression penalty,” (Heinrich [0066-0067], “[0066] Other methods for performing the regularized combination method in step (d) that could be used include: [0067] Logistic regression with a penalty function like ridge regression”)
Torok teaches:
“a degree of a polynomial for a linear model,” (Torok 1, “Polynomial models are primarily used as means to fit a relatively smooth curve to a set of data but the degree of polynomial required to fit a set of data is not usually known a priori. There are several approaches to determining the right degree of the polynomial.”)
Palmer teaches:
“a number of trees in a random forest,” (Palmer 4, “The number of trees in a forest is a parameter that is not tunable in the classical sense but should be set sufficient high”)
In reference to claim 16.
“16. The article of claim 13,” (preamble)
Kaplunovich teaches:
“a stochastic gradient descent loss,” (Kaplunovich 2751, “Optimization function”)
“a number of neurons in a neural network layer,” (Kaplunovich 2751, “Number of neurons”)
“a number of layers in a neural network,” (Kaplunovich 2751, “Number of layers”)
“and a gradient descent learning rate.” (Kaplunovich 2751, “Learning rate”)
Mantovani teaches:
“a maximum depth for a decision tree,” (Mantovani 6, “the maximum depth of any node of the final tree (‘maxdepth’);”)
“a minimum number of samples for a leaf node in a decision tree,” (Mantovani 6, “This hyperparameter controls the minimum number of instances necessary for a split to be attempted.”)
Heinrich teaches:
“logistic regression penalty,” (Heinrich [0066-0067], “[0066] Other methods for performing the regularized combination method in step (d) that could be used include: [0067] Logistic regression with a penalty function like ridge regression”)
Torok teaches:
“a degree of a polynomial for a linear model,” (Torok 1, “Polynomial models are primarily used as means to fit a relatively smooth curve to a set of data but the degree of polynomial required to fit a set of data is not usually known a priori. There are several approaches to determining the right degree of the polynomial.”)
Palmer teaches:
“a number of trees in a random forest,” (Palmer 4, “The number of trees in a forest is a parameter that is not tunable in the classical sense but should be set sufficient high”)
Reddy, Hu, Kaplunovich, Dongen.
Claims 5, 11, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Hu in further view of Kaplunovich in further view of Dongen.
In reference to claim 5.
“5. The information handling system of claim 1,” (preamble)
Dongen teaches:
“wherein the target variable is a time required for a lifecycle management event.” (Dongen Definition 3.8, the definition defines such a predictor for the duration of an activity)
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Motivation to combine Reddy, Hu, Kaplunovich, Dongen.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu and Dongen. Reddy, Hu, Kaplunovich discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network. Dongen discloses the use of historical data such as event logs to predict the length of time required for future cycle events. One would be motivated to combine these references because the disclosure of Dongen teaches an obvious use of the system of Reddy and Hu. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
In reference to claim 11.
“11. The method of claim 7,” (preamble)
Dongen teaches:
“wherein the target variable is a time required for a lifecycle management event.” (Dongen Definition 3.8, the definition defines such a predictor for the duration of an activity)
In reference to claim 17.
“17. The article of claim 13,” (preamble)
Dongen teaches:
“wherein the target variable is a time required for a lifecycle management event.” (Dongen Definition 3.8, the definition defines such a predictor for the duration of an activity)
Reddy, Hu, Kaplunovich, Azeem.
Claims 6, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Hu in further view of Kaplunovich in further view of Azeem.
In reference to claim 6.
“6. The information handling system of claim 1,” (preamble)
Azeem teaches:
“wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.” (Azeem 901, “IV. HYPER CONVERGED INFRASTRUCTURE (HCI)”, The section discusses a computing paradigm capable of running the system as described in claim 1.)
Motivation to combine Reddy, Hu, Kaplunovich, Azeem.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Reddy, Hu and Azeem. Reddy, Hu, Kaplunovich discloses a method of machine learning that utilizes PCA for dimensionality reduction prior to using a neural network. Azeem discloses HCI as a computing infrastructure paradigm. One would be motivated to combine these references because the disclosure of Azeem teaches an obvious system to deploy Reddy and Hu on. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (A) Combining prior art elements according to known methods to yield predictable results.
In reference to claim 12.
“12. The method of claim 11,” (preamble)
Azeem teaches:
“wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.” (Azeem 901, “IV. HYPER CONVERGED INFRASTRUCTURE (HCI)”, The section discusses a computing paradigm capable of running the system as described in claim 1.)
In reference to claim 18.
“18. The article of claim 17,” (preamble)
Azeem teaches:
“wherein the information handling system is a node of a hyperconverged infrastructure (HCI) cluster.” (Azeem 901, “IV. HYPER CONVERGED INFRASTRUCTURE (HCI)”, The section discusses a computing paradigm capable of running the system as described in claim 1.)
Conclusion
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/CODY RYAN GILLESPIE/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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