DETAILED ACTION
This action is in response to the Applicant Response filed 19 November 2025 for application 17/969,358 filed 19 October 2022.
Claim(s) 1, 4, 8, 10, 13, 16-17, 19, 22 is/are currently amended.
Claim(s) 9, 18 is/are cancelled.
Claim(s) 1-8, 10-17, 19-22 is/are pending.
Claim(s) 1-8, 10-17, 19-22 is/are rejected.
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 .
Response to Arguments
Applicant's arguments regarding the objections to the claims have been fully considered and, in light of the amendments to the claims, are persuasive.
Applicant's arguments regarding the 35 U.S.C. 112(b) rejection(s) of claim(s) 8 have been fully considered and, in light of the amendments to the claims, are persuasive. The 35 U.S.C. 112(b) rejection(s) of claim(s) 8 has/have been withdrawn.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claims 1-8, 10-17, 19-22 have been fully considered but are not persuasive. Applicant first asserts that the claim does not recite an abstract idea, but fails to provide evidence to support such an assertion. As can be seen in more detail below, the claims do, in fact, recite an abstract idea. Applicant next argues that the claims integrate a practical application by providing an improvement to generating a predictive model. Examiner respectfully disagrees. First while applicant cites exemplary language from the specification, applicant does not provide evidence to support an assertion of an improvement. As noted below the steps of model generation are abstract. The MPEP states that it is important to note, the judicial exception alone cannot provide the improvement. MPEP 2106.05(a). It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Therefore, the improvement cannot be in the steps of generating a model. Moreover, the MPEP states, claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept. MPEP 2106.05(f). Therefore, the 35 U.S.C. 101 rejection of claims 1-8, 10-17, 19-22 is maintained.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of claims 1-8, 10-17, 19-22 have been considered but are not persuasive. Specifically, applicant asserts that the cited reference does not teach the limitations of claim 1. Examiner respectfully disagrees. As noted in more detail below, Elthakeb teaches a ReLeQ network that determines quantization for a neural network layer-by-layer (Elthakeb, section 3). The quantization determination is based on data values associated with the input of the current layer (Elthakeb, section 3). Using these data values, the neural network is evaluated to generate a target vector (Elthakeb, section 3). Given this target vector and the feature vector determined from the data values, the ReLeQ model is evaluated to determine quantization values based on modifications of the policies and rewards of the ReLeQ model (Elthakeb, section 3). Therefore, Elthakeb does, in fact, teach the limitations of claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-8, 10-17, 19-22 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network.
The limitation of generating a feature vector of one or more features extracted from the data values of the first vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating, from the data values of the second vector, a target vector of data values comprising one or more quantization parameters for the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluating, on the basis of the feature vector and the target vector, the predictive model for predicting the one or more quantization parameters of the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of modifying the predictive model on the basis of the evaluation of the predictive model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of wherein the first and second vectors are generated based on an evaluation of the neural network that is given by a sample from a training dataset for the neural network, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – predictive model, neural network. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites accessing a first vector of data values corresponding to input values to a first layer implemented in the neural network; accessing a second vector of data values corresponding to input values of a second layer implemented in the neural network, wherein the second layer is subsequent to the first layer, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
predictive model, neural network amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network.
The limitation of generating a feature vector of one or more features extracted from the data values of the vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluating the predictive model on the basis of the feature vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating one or more quantization parameters for the second layer, on the basis of the evaluation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving a vector of data values corresponding to input values for the first layer of the neural network, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network.
The limitation of wherein the first layer and the second layer are selected from layers of the neural network on the basis of a user-generated input, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the method of claim 1 but for the recitation of additional element(s) of wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector which is simply additional information regarding the features, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – statistical function. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
statistical function amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the features do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 5 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the predictive model is at least one of a linear predictive function, a non-linear predictive function, a neural network, a gradient boosting machine, a random forest, a support vector machine, a nearest neighbour model, a Gaussian process, a Bayesian regression, or an ensemble.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the predictive model is at least one of a linear predictive function, a non-linear predictive function, a neural network, a gradient boosting machine, a random forest, a support vector machine, a nearest neighbour model, a Gaussian process, a Bayesian regression, or an ensemble which is simply additional information regarding the predictive model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – linear predictive function, non-linear predictive function, neural network, gradient boosting machine, random forest, support vector machine, nearest neighbour model, Gaussian process, Bayesian regression, ensemble. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
linear predictive function, non-linear predictive function, neural network, gradient boosting machine, random forest, support vector machine, nearest neighbour model, Gaussian process, Bayesian regression, ensemble amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the predictive model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network.
The limitation of determining an error between the output and the target vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites computing an output of the predictive model on the basis of the feature vector which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network.
The limitation of wherein modifying the predictive model on the basis of the evaluation of the predictive model comprises modifying one or more parameters of the predictive model to minimize the error between the output and the target vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for generating a predictive model for quantization parameters of a neural network. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 8 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the one or more quantization parameters comprise parameters of a function that maps floating point numbers to fixed point numbers.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system.
The limitation of generate a feature vector of one or more features extracted from the data values of the first vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate, from the data values of the second vector, a target vector of data values comprising one or more quantization parameters for the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluate, on the basis of the feature vector and the target vector, a predictive model for predicting the one or more quantization parameters of the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of modify the predictive model on the basis of the evaluation of the predictive model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of wherein the first and second vectors are generated based on an evaluation of the neural network that is given by a sample from a training dataset for the neural network, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, at least one processor, at least one memory, program code, instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural network, predictive model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites access a first vector of data values corresponding to input values to a first layer implemented in a neural network; access a second vector of data values corresponding to input values of a second layer implemented in the neural network, wherein the second layer is subsequent to the first layer, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
system, at least one processor, at least one memory, program code, instructions amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
neural network, predictive model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system.
The limitation of generate a feature vector of one or more features extracted from the data values of the vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluate the predictive model on the basis of the feature vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate one or more quantization parameters for the second layer, on the basis of the evaluation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receive a vector of data values corresponding to input values for first layer of the neural network, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system.
The limitation of select the first layer and second layer from layers of the neural network on the basis of a user- generated input received at the system, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 13 carries out the system of claim 10 but for the recitation of additional element(s) of wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector which is simply additional information regarding the features, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – statistical function. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
statistical function amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the features do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 14 carries out the system of claim 10 but for the recitation of additional element(s) of wherein the predictive model is at least one of a linear predictive function, a non-linear predictive function, a neural network, a gradient boosting machine, a random forest, a support vector machine, a nearest neighbour model, a Gaussian process, a Bayesian regression, or an ensemble.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the predictive model is at least one of a linear predictive function, a non-linear predictive function, a neural network, a gradient boosting machine, a random forest, a support vector machine, a nearest neighbour model, a Gaussian process, a Bayesian regression, or an ensemble which is simply additional information regarding the predictive model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – linear predictive function, non-linear predictive function, neural network, gradient boosting machine, random forest, support vector machine, nearest neighbour model, Gaussian process, Bayesian regression, ensemble. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
linear predictive function, non-linear predictive function, neural network, gradient boosting machine, random forest, support vector machine, nearest neighbour model, Gaussian process, Bayesian regression, ensemble amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the predictive model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system.
The limitation of determine an error between the output and the target vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites compute an output of the predictive model on the basis of the feature vector which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system.
The limitation of modify one or more parameters of the predictive model to minimize the error between the output and the target vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 17 carries out the system of claim 10 but for the recitation of additional element(s) of wherein the one or more quantization parameters comprise parameters of a function that maps floating point numbers to fixed point numbers.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of generating a feature vector of one or more features extracted from the data values of the first vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating, from the data values of the second vector, a target vector of data values comprising one or more quantization parameters for the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluating, on the basis of the feature vector and the target vector, a predictive model for predicting the one or more quantization parameters of the second layer, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of modifying the predictive model on the basis of the evaluation of the predictive model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of wherein the first and second vectors are generated based on an evaluation of the neural network that is given by a sample from a training dataset for the neural network, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-readable medium, computer instructions, one or more hardware processors. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – neural network, predictive model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites accessing a first vector of data values corresponding to input values to a first layer implemented in a neural network; accessing a second vector of data values corresponding to input values of a second layer implemented in the neural network, wherein the second layer is subsequent to the first layer, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-readable medium, computer instructions, one or more hardware processors amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
neural network, predictive model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of generating a feature vector of one or more features extracted from the data values of the vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of evaluating the predictive model on the basis of the feature vector, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating one or more quantization parameters for the second layer, on the basis of the evaluation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving a vector of data values corresponding to input values for the first layer of the neural network, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 21, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of wherein the first layer and the second layer are selected from layers of the neural network on the basis of a user-generated input, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 22, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 22 carries out the computer-readable medium of claim 19 but for the recitation of additional element(s) of wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector which is simply additional information regarding the features, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – statistical function. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
statistical function amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the features do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-8, 10-11, 13-17, 19-20, 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Elthakeb et al. (ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks, hereinafter referred to as “Elthakeb”).
Regarding claim 1 (Currently Amended), Elthakeb teaches a method for generating a predictive model for quantization parameters of a neural network (Elthakeb, section 2.3 – teaches a reinforcement learning model to quantize parameters of a pre-trained neural network), the method comprising:
accessing a first vector of data values corresponding to input values to a first layer implemented in the neural network (Elthakeb, section 2.3 – teaches quantizing network layer by layer; Elthakeb, section 2.4 – teaches accessing input vector to a first layer; see also Elthakeb, section 4.1 – input data);
generating a feature vector of one or more features extracted from the data values of the first vector (Elthakeb, section 2.4 – teaches extracting a feature vector corresponding to the input vector of a first layer);
accessing a second vector of data values corresponding to input values of a second layer implemented in the neural network, wherein the second layer is subsequent to the first layer (Elthakeb, section 3 – teaches propagating data through the model, including through the first layer to generate output of the first layer as input to a second layer; see also Elthakeb, sections 2.4-2.7);
generating, from the data values of the second vector, a target vector of data values comprising one or more quantization parameters for the second layer (Elthakeb, section 3 – teaches based on the second vector propagated to determine model accuracy, modifying quantization values to create a target vector; see also Elthakeb, sections 2.4-2.7);
evaluating, on the basis of the feature vector and the target vector, the predictive model for predicting the one or more quantization parameters of the second layer (Elthakeb, section 3 – teaches evaluating the ReLeQ model for the target vector to determine policies and rewards based on the feature vector; see also Elthakeb, sections 2.4-2.7); and
modifying the predictive model on the basis of the evaluation of the predictive model (Elthakeb, section 3 – teaches modifying policies and rewards based on evaluation of the selected quantization values of the predicted model; see also Elthakeb, sections 2.4-2.7),
wherein the first and second vectors are generated based on an evaluation of the neural network that is given by a sample from a training dataset for the neural network (Elthakeb, section 3 – teaches that the vectors are generated based on propagating the vectors through the pretrained network; see also Elthakeb, section 4.1 – teaches training data).
Regarding claim 2 (Original), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. Elthakeb further teaches
receiving a vector of data values corresponding to input values for the first layer of the neural network (Elthakeb, section 2.3 – teaches quantizing network layer by layer; Elthakeb, section 2.4 – teaches accessing input vector to a first layer; see also Elthakeb, section 4.1 – teaches input data);
generating a feature vector of one or more features extracted from the data values of the vector (Elthakeb, section 3 – teaches based on the second vector propagated to determine model accuracy, modifying quantization values to create a target vector; see also Elthakeb, sections 2.4-2.7);
evaluating the predictive model on the basis of the feature vector (Elthakeb, section 3 – teaches evaluating the ReLeQ model for the target vector to determine policies and rewards based on the feature vector; see also Elthakeb, sections 2.4-2.7); and
generating one or more quantization parameters for the second layer, on the basis of the evaluation (Elthakeb, section 3 – teaches generating quantization values for each layer of the network; see also Elthakeb, sections 2.4-2.7; Elthakeb, sections 4-5, Table 2).
Regarding claim 4 (Currently Amended), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. Elthakeb further teaches wherein at least one of the one or more features extracted from the data values of the first vector comprises a statistical function computed from the data values of the first vector (Elthakeb, section 2.4 – teaches the feature vector includes statistical data corresponding to the input of a first layer).
Regarding claim 5 (Previously Presented), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. Elthakeb further teaches wherein the predictive model is at least one of a linear predictive function, a non-linear predictive function, a neural network, a gradient boosting machine, a random forest, a support vector machine, a nearest neighbour model, a Gaussian process, a Bayesian regression, or an ensemble (Elthakeb, section 2.3 – teaches the predictive model is a reinforcement learning model).
Regarding claim 6 (Original), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. Elthakeb further teaches wherein evaluating the predictive model comprises:
computing an output of the predictive model on the basis of the feature vector (Elthakeb, section 3 – teaches computing quantization value outputs based on the feature vector), and
determining an error between the output and the target vector (Elthakeb, section 3 – teaches determining and adjusting policies and rewards [errors] from output to determine target; see also Elthakeb, sections 2.4-2.7).
Regarding claim 7 (Previously Presented), Elthakeb teaches all of the limitations of the method of claim 6 as noted above. Elthakeb further teaches wherein modifying the predictive model on the basis of the evaluation of the predictive model comprises modifying one or more parameters of the predictive model to minimize the error between the output and the target vector (Elthakeb, section 3 – teaches modifying policies and rewards based on the evaluation of the ReLeQ model to minimize the error between outputs and the target value).
Regarding claim 8 (Original), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. Elthakeb further teaches wherein the one or more quantization parameters comprise parameters of a function that maps floating point numbers to fixed point numbers (Elthakeb, section 3 – teaches quantizing to 8-bit or less all layers of a pretrained full precision [floating point] DNN).
Regarding claim 10 (Currently Amended), it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Elthakeb further teaches a system, comprising:
at least one processor (Elthakeb, section 4.4 – teaches deploying the model on various hardware platforms); and
at least one memory including program code which when executed by the at least one processor provides instructions to (Elthakeb, section 4.4 – teaches deploying the model on various hardware platforms) …
Regarding claim 11 (Original), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 2.
Regarding claim 13 (Currently Amended), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 4.
Regarding claim 14 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 5.
Regarding claim 15 (Original), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 6.
Regarding claim 16 (Currently Amended), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 7.
Regarding claim 17 (Currently Amended), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 8.
Regarding claim 19 (Currently Amended), it is the computer-readable medium embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Elthakeb further teaches a non-transitory computer-readable medium storing computer instructions, that when executed by one or more hardware processors, cause the one or more hardware processors to perform operations (Elthakeb, section 4.4 – teaches deploying the model on various hardware platforms) …
Regarding claim 20 (Previously Presented), the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 2.
Regarding claim 22 (Currently Amended), the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb for the reasons set forth in the rejection of claim 4.
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.
Claim(s) 3, 12, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elthakeb in view of Oh et al. (A Portable, Automatic Data Quantizers for Deep Neural Networks, hereinafter referred to as “Oh”).
Regarding claim 3 (Original), Elthakeb teaches all of the limitations of the method of claim 1 as noted above. However, Elthakeb does not explicitly teach wherein the first layer and the second layer are selected from layers of the neural network on the basis of a user-generated input.
Oh teaches wherein the first layer and the second layer are selected from layers of the neural network on the basis of a user-generated input (Oh, section 3 – teaches user selected layers for quantization).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Elthakeb with the teachings of Oh in order to improve neural network efficiency based on a user’s needs while requiring minimal user effort in the field of neural network quantization (Oh, Abstract – “With the proliferation of AI-based applications and services, there are strong demands for efficient processing of deep neural networks (DNNs). DNNs are known to be both compute and memory-intensive as they require a tremendous amount of computation and large memory space. Quantization is a popular technique to boost efficiency of DNNs by representing a number with fewer bits, hence reducing both computational strength and memory footprint. However, it is a difficult task to find an optimal number representation for a DNN due to a combinatorial explosion in feasible number representations with varying bit widths, which is only exacerbated by layer-wise optimization. Besides, existing quantization techniques often target a specific DNN framework and/or hardware platform, lacking portability across various execution environments. To address this, we propose libnumber, a portable, automatic quantization framework for DNNs. By introducing Number abstract data type (ADT), libnumber encapsulates the internal representation of a number from the user. Then the auto-tuner of libnumber finds a compact representation (type, bit width, and bias) for the number that minimizes the user-supplied objective function, while satisfying the accuracy constraint. Thus, libnumber effectively separates the concern of developing an effective DNN model from low-level optimization of number representation. Our evaluation using eleven DNN models on two DNN frameworks targeting an FPGA platform demonstrates over 8× (7×) reduction in the parameter size on average when up to 7% (1%) loss of relative accuracy is tolerable, with a maximum reduction of 16×, compared to the baseline using 32-bit floating-point numbers. This leads to an geomean speedup of 3.79× with a maximum speedup of 12.77× over the baseline, while requiring only minimal programmer effort.”).
Regarding claim 12 (Original), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb in view of Oh for the reasons set forth in the rejection of claim 3.
Regarding claim 21 (Previously Presented), the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Elthakeb in view of Oh for the reasons set forth in the rejection of claim 3.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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