DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 7 November 2025.
Claims 1-3, 6-10, 13-17, and 20 are pending. Claims 1, 8, and 15 are independent claims.
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 1-3, 6-10, 13-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-3 and 6-7 are directed toward a method (process). Claims 8-10 and 13-14 are directed toward an apparatus (machine). Claims 15-17 and 20 are directed toward a non-transitory computer-readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 1, the claims recite:
clustering the training data into one or more clusters, each of the one or more clusters including a respective performance identifier (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses performing an evaluation to cluster training data into one or more clusters)
converting the precise system specification into a generic system specification (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses replacing the specific part identifiers comprising the precise system specification with corresponding generic performance identifiers, such as if provided the precise system specification and a mapping of part identifiers to generic performance identifiers with the aid of pencil and paper)
performing a respective search of one or more data structures to identify at least one corresponding performance identifier, the corresponding performance identifier belonging to one of the clusters, the corresponding performance identifier including an alphanumerical string that specifies a property shared by a plurality of part identifiers (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses assessing a collection of elements in one or more data structures to determine, evaluate, or judge which identifier most closely aligns with the provided part identifier, such as if provided a mapping from part identifier to associated performance identifier)
encoding the generic system specification into a hardware configuration signature (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses encoding a system specification into a specific format, such as by assigning each element in the set of possible system elements to an index in a vector and recording a 1 at the associated index in the vector if the element is included in the specification and a 0 if not, such as with the aid of pencil and paper if provided a complete list of elements)
classifying the hardware configuration signature… to yield an estimated system throughput for the system (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses analyzing a system specification to assign any category)
detecting whether the estimated system throughput is greater than or equal to a required system throughput (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses comparing an estimated measure of throughput to a required measure of throughput, such as comparing numbers)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim recites the additional element:
obtaining training data that includes telemetry data and hardware configuration data, the telemetry data indicating a respective system throughput of each of a plurality of deployed systems, and hard configuration data identifying a respective hardware configuration of each of the plurality of deployed systems
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claims disclose the additional element:
receiving a user input including a precise system specification for a system
This limitation is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e., pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
The claim discloses the additional element:
training a machine learning model based on the clustered training data, the machine learning model being configured to yield estimated system throughput
the precise system specification including one or more part identifiers that describe, at least in part, a hardware configuration of the system
the generic system specification describing the hardware configuration in terms of one or more performance identifiers, the conversion including: (i) instantiating, in a memory, the generic system specification, and (ii) for each of the part identifiers:
performing a respective search of one or more data structures to identify at least one performance identifier that is mapped to the part identifier by the one or more data structures and which encompasses a larger number of parts than the part identifier, and including the corresponding performance identifier into the generic system specification
by using the machine learning model that is configured to yield an estimated system throughput for the system
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recites the additional element:
wherein outputting the one or more recommended system specification includes at least one of displaying the one or more system specifications on a display device or transmitting the one or more system specifications over a communication network
This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim recites the additional element:
obtaining training data that includes telemetry data and hardware configuration data, the telemetry data indicating a respective system throughput of each of a plurality of deployed systems, and hard configuration data identifying a respective hardware configuration of each of the plurality of deployed systems
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claims disclose the additional element:
receiving a user input including a precise system specification for a system
This limitation is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e., pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
The claim discloses the additional element:
training a machine learning model based on the clustered training data, the machine learning model being configured to yield estimated system throughput
the precise system specification including one or more part identifiers that describe, at least in part, a hardware configuration of the system
the generic system specification describing the hardware configuration in terms of one or more performance identifiers, the conversion including: (i) instantiating, in a memory, the generic system specification, and (ii) for each of the part identifiers:
performing a respective search of one or more data structures to identify at least one performance identifier that is mapped to the part identifier by the one or more data structures and which encompasses a larger number of parts than the part identifier, and including the corresponding performance identifier into the generic system specification
by using the machine learning model that is configured to yield an estimated system throughput for the system
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Finally, the claim recites the additional element:
wherein outputting the one or more recommended system specification includes at least one of displaying the one or more system specifications on a display device or transmitting the one or more system specifications over a communication network
This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2:
With respect to dependent claim 2, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 2, the claim recites the element:
further comprising, when the estimated system throughput is less than the required system throughput, discarding the generic system specification (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses discarding a specification or removing the specification from a list of valid or active specifications)
Claim 3:
With respect to dependent claim 3, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 3, the claim recites the element:
includes: generating a first recommended system specification by replacing a given performance identifier in the generic system specification with a first part identifier (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. Where generating a system specification might include recording or writing the settings, including associated part identifiers, of a system specification or configuration, as drafted and under its broadest reasonable interpretation this limitation encompasses copying a system specification and updating a part identifier within the specification, such as with the aid of pencil and paper)
generating a second recommended system specification by replacing the given performance identifier with a second part identifier (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. Where generating a system specification might include recording or writing the settings, including associated part identifiers, of a system specification or configuration, as drafted and under its broadest reasonable interpretation, this limitation encompasses copying a system specification and updating a part identifier within the specification, such as with the aid of pencil and paper)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
In particular, the claim recites the additional element of “wherein outputting the one or more recommended system specifications”, which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Further, the claim recites the additional element of “and outputting the first recommended system specification and the second recommended system specification”, which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In particular, the claim recites the additional element of “wherein outputting the one or more recommended system specifications”, which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Further, the claim recites the additional element of “and outputting the first recommend system specification and the second recommended system specification”, which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 6:
With respect to dependent claim 6, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim discloses the additional element:
wherein the machine learning model is trained by using a supervised learning process
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the additional element:
wherein the machine learning model is trained by using a supervised learning process
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7:
With respect to dependent claim 7, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
In particular, the claim recites the additional element:
obtaining a configuration data set, the configuration data set identifying a respective configuration of each of the plurality of deployed systems
This limitation is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e., pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Further, the claim recites:
wherein the machine learning model is trained further based on the configuration data set
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
In particular, the claim recites the additional element:
obtaining a configuration data set, the configuration data set identifying a respective configuration of each of the plurality of deployed systems
This limitation is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e., pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Further, the claim recites:
wherein the machine learning model is trained further based on the configuration data set
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 8:
With respect to claim 8, the claim recites the apparatus corresponding to the method of claim 1. Therefore, claim 8 is rejected under similar rationale as provided with respect to claim 1, and the analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
a memory
at least one processor operatively coupled to the memory, the at least one processing being configured to perform operations
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment through a recitation of generic computing components. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim recites the elements:
a memory
at least one processor operatively coupled to the memory, the at least one processing being configured to perform operations
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment through a recitation of generic computing components. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
Claim 9 depends from claim 8 and recites an apparatus that corresponds to the limitations of claim 2, and therefore claim 9 is rejected under the same rationale as outlined above for claims 2 and 8 for being substantially similar, mutatis mutandis.
Claim 10:
Claim 10 depends from claim 8 and recites an apparatus that corresponds to the limitations of claim 3, and therefore claim 10 is rejected under the same rationale as outlined above for claims 3 and 8 for being substantially similar, mutatis mutandis.
Claim 13:
Claim 13 depends from claim 8 and recites an apparatus that corresponds to the limitations of claim 6, and therefore claim 13 is rejected under the same rationale as outlined above for claims 6 and 8 for being substantially similar, mutatis mutandis.
Claim 14:
Claim 14 depends from claim 8 and recites an apparatus that corresponds to the limitations of claim 7, and therefore claim 14 is rejected under the same rationale as outlined above for claims 7 and 8 for being substantially similar, mutatis mutandis.
Claim 15:
With respect to claim 15, the claim recites the non-transitory computer-readable medium corresponding to the method of claim 1. Therefore, claim 15 is rejected under similar rationale as provided with respect to claim 1, and the analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the elements:
a non-transitory computer-readable medium storing one or more processor-executable instructions, which, when executed by at least one processor, cause the processor to perform operations
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment through a recitation of generic computing components. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim recites the elements:
a non-transitory computer-readable medium storing one or more processor-executable instructions, which, when executed by at least one processor, cause the processor to perform operations
Such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment through a recitation of generic computing components. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." (Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 16:
Claim 16 depends from claim 15 and recites a non-transitory computer-readable medium that corresponds to the limitations of claim 2, and therefore claim 16 is rejected under the same rationale as outlined above for claims 2 and 15 for being substantially similar, mutatis mutandis.
Claim 17:
Claim 17 depends from claim 15 and recites a non-transitory computer-readable medium that corresponds to the limitations of claim 3, and therefore claim 17 is rejected under the same rationale as outlined above for claims 3 and 15 for being substantially similar, mutatis mutandis.
Claim 20:
Claim 20 depends from claim 15 and recites a non-transitory computer-readable medium that corresponds to the limitations of claim 6, and therefore claim 20 is rejected under the same rationale as outlined above for claims 6 and 15 for being substantially similar, mutatis mutandis.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6-9, 13-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez et al. (Predicting Computer Performance based on Hardware Configuration Using Multiple Neural Networks, 2018, hereafter Lopez) and further in view of Gajski et al. (Specification and Design of Embedded Hardware Software Systems, 1995, hereafter Gajski), and further in view of Strand (What is one-hot encoding and when is it used in data science?, 2017), and further in view of Wetherbee et al. (US 11720823, filed 26 May 2022, hereafter Wetherbee) and further in view of Kronenbitter et al. (US 2022/0269248, filed 18 February 2022, hereafter Kronenbitter) and further in view of Mishra et al. (PC Configuration and Component Recommendation System, 2021, hereafter Mishra) and further in view of Shah et al. (WO 2019/169147, published 6 September 2019, hereafter Shah) and further in view of Perl (US 2019/0132378, published 2 May 2019).
As per independent claim 1, Lopez discloses a method comprising:
obtaining training data that includes telemetry data and hardware configuration data, the telemetry data indicating a respective system throughput of each of the plurality of deployed systems, and the hardware configuration data identifying a respective hardware configuration of each of the plurality of deployed systems (page 825; Section IIA and Figure 1: Here, the variables considered for training include telemetry data, such as processor speed, and hardware configuration data, such as the number of cores per chip and number of chips in the system)
clustering the training data into one or more clusters and training a machine learning model based on the clustered training data, the machine learning model being configured to yield estimated system throughput (page 826; Section IIB: Here, the training data is split into separate data sets based upon benchmark idea and used to train neural networks having 20 and 50 nodes in the hidden layer (Table 1))
receiving a user input including a precise system specification for a system (page 825, section 2, paragraph 2: “a neural network capable of taking an input hardware configuration”; Examiner’s Note (EN): A hardware configuration is encompassed by the BRI of a precise system specification)
the precise system specification including one or more part identifiers that describe, at least in part, a hardware configuration of the system (page 825, section 1, paragraph 1: “In computer systems, a Personal Computer (PC) configuration is an arrangement of functional hardware units, each with unique performance, number, and chief characteristics. A PC configuration can also pertain to the choice of hardware”; (EN): The part identifiers correspond to the functional hardware units)
describing the hardware configuration in terms of one or more performance identifiers (page 825, section 1, paragraph 1: “In computer systems, a Personal Computer (PC) configuration is an arrangement of functional hardware units, each with unique performance… characteristics)
classifying the hardware configuration signature by using the machine learning model to yield an estimated system throughput for the system (page 826, section 4, paragraph 1: “We also developed feed-forward [sic] network to use the component data as inputs and give the expected base and peak performance scores as outputs”; (EN): The component data, equivalently referenced by LOPEZ as “computer hardware configuration” (page 825, ABSTRACT, LOPEZ), is encompassed by the BRI of the hardware configuration signature and the performance scores are encompassed by the BRI of estimated system throughput).
detecting whether the estimated system throughput is greater than or equal to a required system throughput (page 824, section 1, paragraph 1, LOPEZ: “How can we measure the performance of a PC configuration? We can use benchmarks. In computing, a benchmark is the act of running an application, a set of applications, or other operations to evaluate the relative performance of an object, normally by running many standard tests and trails against it. One might run a benchmark… to see if a piece of hardware supports a certain amount of workload”; (EN): As discussed above, the broadest reasonable interpretation of throughput in light of the instant specification includes performance measures. As such, the broadest reasonable interpretation of a required system throughput includes comparisons of performance for a specific benchmark. Thus, by teaching a method for determining if a piece of hardware supports a certain amount of workload utilizing a benchmark, LOPEZ teaches determining if the estimated system throughput is greater than or equal to a required throughput)
Lopez fails to specifically disclose:
each of the one or more clusters including a respective performance identifier
converting the precise system specification into a generic system specification, the generic system specification describing the hardware configuration in terms of one or more performance identifiers, the conversion including: (i) instantiating, in a memory, the generic system specification, and (ii) for each of the part identifiers: performing a respective search of one or more data structures to identify at least one performance identifier that is mapped to the part identifier by the one or more data structures and which encompasses a larger number of parts than the part identifier, and including the corresponding performance identifier into the generic system specification
the corresponding performance identifier belonging to one of the clusters, the corresponding performance identifier including an alphanumerical string that specifies a property shared by a plurality of part identifiers
encoding the generic system specification into a hardware configuration signature
encoding the… specification into a hardware configuration signature
when the estimated system throughput is greater than or equal to the required system throughput, outputting one or more recommended system specifications that are based on the generic system specification
wherein outputting the one or more recommended system specification includes at least one of displaying the one or more system specification on a display device or transmitting the one or more system specifications over a communications network
However, Gajski, which is analogous to the claimed invention because it is directed toward converting system specification data into a generic system specification, discloses:
converting the precise system specification into a generic system specification (page 58, section: “Exploration”, paragraph 1, GAJSKI: “Given a functional specification of a system, the designer must create a system-level design of interconnected components, each component implementing a portion of that specification”; (EN): The system-level design of interconnected components corresponds to the generic system specification)
the generic system specification describing the hardware configuration in terms of one or more performance identifiers (page 58, section: “Exploration”, paragraph 1, GAJSKI: “each component implementing a portion of that specification. A design’s acceptability depends on how well it satisfies constraints on design metrics such as performance, size, power, and cost”; (EN): The constraints correspond to the performance identifiers. As outlined, the components comprise the generic system specification).
the conversion including: (i) instantiating, in a memory, the generic system specification (page 54, section: “Embedded systems have”, paragraph 5, GAJSKI: “we transform the initial description into one more suitable for implementation”; (EN): In a computing setting, transforming a description into a transformed description requires instantiating, in a memory, the transformed description, which corresponds to the generic system specification).
(ii) for each of the part identifiers: performing a respective search of one or more data structures to identify at least one performance identifier that is mapped to the part identifier by the one or more data structures and which encompasses a larger number of parts than the part identifier” (page 56, section: “Model creation”, paragraph 1, GAJSKI: “we decomposed the ITVP’s functionality into functions such as video storing, audio storing, video generation, and audio generation”; and page 59, section: “Partitioning”, paragraph 1, GAJSKI: “Given a functional specification and an allocation of system components, we need to partition the specification and assign each part to one of the allocated components”; (EN): The category of video storing corresponds to a performance identifier and comprises, with reference to figure 2, elements such as Memory2 V500, ASIC2 XC40230, and Video_in. This performance identifier encompasses a larger number of parts than any one corresponding part identifier. The allocated components correspond to the generic identifiers. With reference to the storage of the mapping between part identifiers and performance identifiers when translating from generic performance identifiers to specific part identifiers, GAJSKI states “High-level synthesis transforms a system component’s functional description into a structure of RT components such as registers, multiplexers, and ALUs… Next, allocation selects, from an RT component database, the storage, function, and bus units to be used in the design” (page 63, section: “Hardware synthesis”, paragraphs 2-3, GAJSKI). This demonstrates that the method utilizes a database (which is encompassed by the BRI of a data structure) to retain mappings between functional units (e.g. the storage, processing, and other units utilized to outline the functional specification) and system components).
and including the performance into the generic system specification (page 59, section: “Partitioning”, paragraph 1, GAJSKI: “Given a functional specification and an allocation of system components, we need to partition the specification and assign each part to one of the allocated components”; (EN): As outlined above with reference to the 112(b) rejection, this limitation is understood to be directed to including the performance identifier in the generic system specification. The assignment is encompassed by the BRI of inclusion).
encoding the generic system specification (page 59, section: “Partitioning”, paragraph 1, GAJSKI: “Given a functional specification and an allocation of system components, we need to partition the specification and assign each part to one of the allocated components”; (EN): allocating and assigning are encompassed by the BRI of encoding in a computing context. The examiner further notes that, in a computing process, any data utilized in or stored on a computer may be reasonably understood to be encoded in a computer-readable format).
LOPEZ and GAJSKI are analogous art because they are from the same field of endeavor as the claimed invention, namely assessment of computing configurations. LOPEZ teaches receiving a user input but does not appear to explicitly disclose converting the precise system specification into a generic system specification as taught by GAJSKI. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of LOPEZ with GAJSKI’s hierarchical specification conversion because “Today’s embedded-system designer has little assistance in performing system design tasks. No widely accepted methodology or tool is available to help the designer create a functional specification and map it to a system-level architecture. Most system designers work in an ad-hoc manner, relying heavily on informal and manual techniques and exploring only a handful of possibilities. A hierarchical modeling methodology can improve the situation” (page 54, section: “Embedded systems have”, paragraph 4, GAJSKI), as suggested by GAJSKI.
Further, Strand, which is analogous to the claimed invention because it is directed toward encoding specification data into a hardware configuration, discloses encoding the… specification into a hardware configuration signature (paragraph 1, STRAND: “One hot encoding transforms categorical features to a format that works better with classification and regression algorithms”; (EN): The instant specification does not appear to explicitly define a hardware configuration signature, but paragraph [0043] of the instant specification states “The hardware configuration signature may include a plurality of bits. Each bit in the hardware configuration signature may correspond to a specific part”, which encompasses a one hot encoding vector. The specification is comprised of categorical features. Encoding the information which comprises the configuration into a signature is encompassed by the BRI of encoding the specification into a signature).
LOPEZ and STRAND are analogous art because they are from the same field of endeavor as the claimed invention, namely assessment of computing configurations and encoding of nominal values, respectively. The combination of LOPEZ and GAJSKI teaches encoding the generic system specification but does not appear to explicitly disclose encoding the… specification into a hardware configuration signature as taught by STRAND. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of the combination of LOPEZ and GAJSKI with STRAND’s nominal encoding because, though “I could encode these to nominal values as I have done here, but that wouldn’t make much sense from a machine learning perspective. We can’t say that the category of Penguin is greater or smaller than “Human”. Then they would be ordinal values, not nominal” (paragraph 2, STRAND), as taught by STRAND.
Additionally, Wetherbee, which is analogous to the claimed invention because it is directed toward outputting specifications, discloses when the estimated system throughput is greater than or equal to the required system throughput, outputting one or more recommended system specifications that are based on the generic system specification (page 25, column 13, lines 5-7, WETHERBEE: “Thus, if the target latency is not exceeded, the processor will generate a recommendation in favor of the compute shape”; (EN): As discussed above, the broadest reasonable interpretation of a system specification includes hardware specifications, such as WETHERBEE’s compute shape (“the compute shape (configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment”, page 24, column 12, lines 61-63, WETHERBEE). WETHERBEE explicitly discuss both throughput and latency as a measure of system performance, stating “the autonomous container scoping tool generates expected cloud container performance in terms of throughput and latency as a function of the customer’s expected number of signals and sampling rates for those signals” (page 20, column 4, lines 7-10, WETHERBEE). As such, by bounding compute cost by a target latency (“compute costs of multiple test applications do not exceed the target latency”, page 25, column 13, lines 14-15, WETHERBEE), WETHERBEE determines if an estimated throughput is greater than or equal to a target throughput. Thus, by teaching to generate a recommendation in favor of the compute shape based on measures of latency, WETHERBEE teaches outputting a recommended system specification when the target throughput is achieved).
LOPEZ and WETHERBEE are analogous art because they are from the same field of endeavor as the claimed invention. The combination of LOPEZ, GAJSKI, and STRAND teaches encoding the generic system specification into a hardware configuration signature, but does not appear to distinctly disclose when the estimated system throughput is greater than or equal to the required system throughput, outputting one or more recommended system specifications as taught by WETHERBEE. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the machine learning system of the combination of LOPEZ, GAJSKI, and STRAND with WETHERBEE’s recommended system output in order to ensure optimized system specifications are utilized in industry settings with complicated and consistently updating needs (“But, achieving the low-latency and high-throughput specifications for large-scale streaming prognostics applications with prognostic ML algorithms… in a cloud computing environment requires that the cloud computing environment be correctly matched to the performance specifications before the prognostics applications are deployed… Correctly sizing the cloud container for the business enterprise’s application to ensure that the business enterprise has good performance for real-time streaming prognostics presents a number of technical challenges” (page 20, column 3, lines 6-20, WETHERBEE) and “This enables business entities to autonomously grow their cloud container capabilities through elasticity as their processing demands increase” (page 20, column 4, lines 53-56, WETHERBEE)), as suggested by WETHERBEE.
Further, Kronenbitter, which is analogous to the claimed invention because it is directed toward determining an optimized configuration and displaying the configuration parameters, discloses wherein outputting the one or more recommended system specification includes at least one of displaying the one or more system specification on a display device or transmitting the one or more system specifications over a communications network (Figure 3; paragraph 0035: Here, a set of configuration parameters are optimized based upon historical process configuration data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kronenbitter, with Lopez-Gajski-Strand-Wetherbee, with a reasonable expectation of success, as it would have allowed for leveraging machine learning to optimize and display configuration parameters based upon historical data (Kronenbitter: Figure 3).
Additionally, Mishra, which is analogous to the claimed invention because it is directed toward storing configuration and component information, discloses:
at least one of the performance identifiers including an alphanumerical string that specifies a hardware performance class, the hardware performance class corresponding to one of the clusters, the hardware performance class identifying a range of structural features, the range of structural features encompassing one of the part identifiers (Figure 2: Here, a record is stored that includes an alphanumeric string that specifies a hardware performance class (number of cores), a range of structure features (number of threads, core clock. boosted clock, and multithreading), and a part identifier (name))
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Mishra with Lopez-Gajski-Strand-Wetherbee-Kronenbitter, with a reasonable expectation of success, as it would have allowed for storing and tracking information associated with the training data. This would have allowed for further narrowing of the training data, as necessary, to improve results.
Additionally, Shah, which is analogous to the claimed invention because it is directed toward identifying clusters based upon similar parts, discloses clustering based upon a property shared by a plurality of part identifiers (paragraph 0024: Here, a parts dictionary is established that is used for clustering parts based on common descriptive words found in their parts description). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Shah with Lopez-Gajski-Strand-Wetherbee-Kronenbitter-Mishra, with a reasonable expectation of success, as it would have allowed for clustering based upon identified similar parts using a parts dictionary (Shah: paragraph 0024).
Finally, Perl, which is analogous to the claimed invention because it is directed toward grouping data, discloses each of the clusters including a respective performance identifier, the corresponding performance identifier belonging to one of the clusters (paragraph 0054: Here, a descriptive identifier is used for a group of resources having a common value for a resource property). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Perl with Lopez-Gajski-Strand-Wetherbee-Kronenbitter-Mishra-Shah, with a reasonable expectation of success, as it would have allowed for labeling and grouping resources based upon common properties (Perl: paragraph 0054)
As per dependent claim 2, Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. WETHERBEE further teaches further comprising, when the estimated system throughput is less than the required system throughput, discarding the generic system specification (page 24, column 12, line 56-page 25, column 13, line 1, WETHERBEE: “The processor determines whether the compute cost at the target combination exceeds a target latency for performance of the machine learning application at the target combination… If the target latency is exceeded, the compute shape (configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment) assigned to the machine learning application in test application 133is inadequate, and will cause a backup of unprocessed observations. Thus, if the target latency is exceeded, the processor will generate a recommendation against the compute shape of the test application 133”; (EN): As discussed above, WETHERBEE’s measure of performance includes throughput and latency is reasonably understood to be encompassed by estimations of throughput. Generating a recommendation against a system specification is reasonably understood to be equivalent to discarding the system specification in the use case outlined by WETHERBEE, as both actions result in suggesting the system specification should not be utilized).
As per dependent claim 6, Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lopez fails to specifically disclose wherein the learning process is a supervised learning process.
However, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date that a learning model may be trained via supervised learning. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Lopez-Gajski-Strand-Wetherbee-Kronenbitter-Mishra, with a reasonable expectation of success, as it would have allowed for improving training speed by using supervised learning.
As per dependent claim 7, Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein.
LOPEZ further teaches
obtaining a configuration data set, the configuration data set identifying a respective configuration of each of the plurality of deployed systems, wherein the machine learning model is trained further based on the configuration data set (page 825, Section 2, paragraph 2, LOPEZ: “To keep the network simple and give the most consistently accurate results, we split the dataset into clusters by benchmark and trained a separate network on each benchmark data cluster. The networks can then be combined into a collection of predictive models based on benchmark class”, (EN): [0024] of the instant specification states that “according to the present example, the neural network 113 includes a fully connected neural network. However, alternative implementations are possible in which the neural network 113 includes another type of neural network, such as a convolutional neural network, etc. In other words, it is understood that the present disclosure is not limited to any specific type of neural network being used”. Thus, by training the utilized neural networks with the telemetry and configuration data, as discussed above, LOPEZ teaches the machine learning model as a neural network trained on the training data).
With respect to claim 8, the applicant discloses the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference. Further, Lopez discloses an apparatus, comprising: a memory; and at least one processor operatively coupled to the memory, the at least one processor being configured to perform the operations (page 824, ABSTRACT, LOPEZ: "we predict the performance of a computer hardware configuration using Multiple Neural Networks (MNN)"; (EN): A neural network is a computing entity).
With respect to claim 9, the applicant discloses the limitations substantially similar to those in claim 2. The analysis of claim 2 is incorporated herein by reference.
With respect to claim 13, the applicant discloses the limitations substantially similar to those in claim 6. The analysis of claim 6 is incorporated herein by reference.
With respect to claim 14, the applicant discloses the limitations substantially similar to those in claim 7. The analysis of claim 7 is incorporated herein by reference.
With respect to claim 15, the applicant discloses the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference. Lopez discloses a non-transitory computer-readable medium storing one or more processor-executable instructions, which, when executed by at least one processor, cause the at least one processor to perform to operations (page 824, ABSTRACT, LOPEZ: "we predict the performance of a computer hardware configuration using Multiple Neural Networks (MNN)"; (EN): A computing entity is controlled through computer-readable medium storing processor-executable instructions).
With respect to claim 16, the applicant discloses the limitations substantially similar to those in claim 2. The analysis of claim 2 is incorporated herein by reference.
With respect to claim 20, the applicant discloses the limitations substantially similar to those in claim 6. The analysis of claim 6 is incorporated herein by reference.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl and further in view of About PCPartsPicker (2017, hereafter PC).
As per dependent claim 3, Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Further, WETHERBEE further teaches “and outputting the first recommend system specification and the second recommended system specification” (page 25, column 13, lines 5-7, WETHERBEE: “Thus, if the target latency is not exceeded, the processor will generate a recommendation in favor of the compute shape”; (EN): As discussed above, WETHERBEE’s compute shape and associated parameters are reasonably understood to be encompassed by a system specification and WETHERBEE’s target latency expresses the throughput threshold. Additionally, WETHERBEE teaches to perform this process iteratively for each possible system, referenced as “evaluating multiple compute shapes” (page 29, column 21, lines 31-32, WETHERBEE), by stating “the method 900 is an additional outermost loop is added to process 300, repeating for each of a set of compute shapes” (page 29, column 21, lines 32-34, WETHERBEE). Thus, by teaching to generate a recommendation in favor or against for multiple compute shapes, WETHERBEE teaches outputting recommended system specifications).
Lopez fails to specifically disclose “outputting the one or more recommended system specifications includes: generating a first recommended system specification by replacing a given performance identifier with a first part identifier” or “generating a second recommended system specification by replacing the given performance identifier with a second part identifier”.
However, in the same field, analogous art PC provides this additional functionality by teaching “outputting the one or more recommended system specifications includes: generating a first recommended system specification by replacing a given performance identifier with a first part identifier” (page 4, PC: “Choose A CPU List”; (EN): The performance identifier corresponds to the categories, groups, or labels associated with computing elements with at least one shared characteristic. By teaching a system configuration builder with the ability to specify real world system parts, such as the specific elements listed, to replace the performance identifiers, such as CPU, PC teaches a method for generating a system specification by replacing the performance identifier with a first part identifier).
PC further provides this additional functionality by teaching “generating a second recommended system specification by replacing the given performance identifier with a second part identifier” (page 4, PC: “Choose A CPU List”; (EN): With reference to figures 7B and 7C, [0054] of the instant application discusses this replacement of parts by stating “different recommended system specifications may be generated by replacing the same performance identifier in a generic system specification with different part identifiers”. As discussed above, the part identifiers includes the outlined specific CPU options. By providing the ability to select or choose between multiple different specific parts, such as specific CPUs, for each performance identifier, or component in the part list such as CPU, PC is teaching the ability to create multiple recommended system specifications by replacing performance identifiers with a part identifier from an assortment of valid part identifiers).
LOPEZ and PC are analogous art because they are from the same field of endeavor as the claimed invention, namely hardware configuration planning and assessment. The combination of LOPEZ, GAJSKI, STRAND, and WETHERBEE teaches outputting the first recommend system specification and the second recommended system specification, but does not appear to distinctly disclose generating a first recommended system specification by replacing a given performance identifier with a first part identifier as taught by PC. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the system of the combination of LOPEZ, GAJSKI, STRAND, and WETHERBEE with PC’s system configuration generator in order to utilize parametric price alerts and benefit from automatic compatibility guidance (page 1, PC), as suggested by PC.
With respect to claims 10 and 17, the applicant discloses the limitations substantially similar to those in claim 3. The analysis of claim 3 is incorporated herein by reference.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Lopez, Gajski, Strand, Wetherbee, Kronenbitter, Mishra, Shah, and Perl.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant argues that the conversion of a precise system specification into a generic system specification, the encoding the generic system specification into a signature, and the subsequent classification of the signature to determine throughput collectively constitute a non-conventional and non-generic arrangement of components (pages 16-17).
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
In this instance, the abstract idea encompasses the elements:
clustering the training data into one or more clusters, each of the one or more clusters including a respective performance identifier (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses performing an evaluation to cluster training data into one or more clusters)
converting the precise system specification into a generic system specification (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses replacing the specific part identifiers comprising the precise system specification with corresponding generic performance identifiers, such as if provided the precise system specification and a mapping of part identifiers to generic performance identifiers with the aid of pencil and paper)
performing a respective search of one or more data structures to identify at least one corresponding performance identifier, the corresponding performance identifier belonging to one of the clusters, the corresponding performance identifier including an alphanumerical string that specifies a property shared by a plurality of part identifiers (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses assessing a collection of elements in one or more data structures to determine, evaluate, or judge which identifier most closely aligns with the provided part identifier, such as if provided a mapping from part identifier to associated performance identifier)
encoding the generic system specification into a hardware configuration signature (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses encoding a system specification into a specific format, such as by assigning each element in the set of possible system elements to an index in a vector and recording a 1 at the associated index in the vector if the element is included in the specification and a 0 if not, such as with the aid of pencil and paper if provided a complete list of elements)
classifying the hardware configuration signature… to yield an estimated system throughput for the system (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses analyzing a system specification to assign any category)
detecting whether the estimated system throughput is greater than or equal to a required system throughput (mental process; As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. As drafted and under its broadest reasonable interpretation, this limitation encompasses comparing an estimated measure of throughput to a required measure of throughput, such as comparing numbers)
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II). For these reasons, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beaudoin (US 2020/0065708): Discloses training a plurality of machine learning models (Figure 3, item 120) based upon specific system data (Figure 3, item 110) and treating this this trained model as a generic system for use on another system (Figure 3, item 200) where the model is further trained using the another system data to improve the model (paragraph 0053)
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 communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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/KYLE R STORK/Primary Examiner, Art Unit 2128