DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant’s submission filed 03 December 2025 [hereinafter Response] has been entered, where:
Claims 1, 7, and 13 have been amended.
Claims 2, 4, 8, 10, 14, and 16 have been cancelled.
Claims 1, 3, 5-7, 9, 11-13, 15, 17, and 18 are pending.
Claims 1, 3, 5-7, 9, 11-13, 15, 17, and 18 are rejected.
Foreign priority is claimed to CN 202011055677.9 filed 29 September 2020. A certified copy of this paper has been filed 19 April 2023. Accordingly, receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1, 3, 5-7, 9, 11-13, 15, 17, and 18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitation of “[(e)] applying the parameter to the model to determine the remaining life of the target disk.” The activity of “[(e)] determine” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not integrate the abstract idea into a practical application. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(d)] training,” “[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the abstract idea. The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
The claim also recites further details or specifics the additional elements of “[(a)] acquiring,” in that “[acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[acquiring a parameter] . . . , [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used, and [(b.2)] include one or more of : . . .” and of the additional element of “[(c)] acquiring additional parameters,” “[(c.1)] wherein the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation, includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, is the post-processing, insignificant activity of transmitting data over a network, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not amount to significantly more than the abstract idea. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more details or specifics to the additional element of “[(d)] training,” “[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the additional element.
The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” and “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are well-understood, routine, and conventional activities of storing and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that do not amount to significantly more than the abstract idea. The claim also recites further details or specifics the additional elements of “[(b),(c)] acquiring,” in that “[acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[(b)] acquiring a parameter] . . . , [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used,” and of the additional element of “[(c)] acquiring additional parameters,” “wherein “[(c.1)] the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation, includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly is the well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Thus, claim 1 is subject-matter ineligible.
Claim 7 recites an electronic device, which is a machine, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitation of “applying the parameter to the model to determine the remaining life of the target disk.” The activity of [(e)] determine” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 7 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “at least one processing unit,” and “at least one memory which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit.” The execution of instructions on generic computer components (processing unit, at least one memory) does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not integrate the abstract idea into a practical application. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “training,” wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the abstract idea.
The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. The claim also recites further details or specifics the additional elements of “acquiring,” in that “[(a) acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[(b) acquiring a set of parameters] . . . , [(b.1)] wherein the parameter indicates usage information of the target disk when it is used, and [(b.2)] include one or more of : . . .” and of the additional element of “[(c)] acquiring additional parameters,” “[(c.1)] wherein the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, is the post-processing, insignificant activity of transmitting data over a network, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application, Therefore, claim 7 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “at least one processing unit,” and “at least one memory which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit.” The execution of instructions on generic computer components (processing unit, at least one memory) does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not amount to significantly more than the abstract idea. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more details or specifics to the additional element of “[(d)] training,” “[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the abstract idea.
The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are well-understood, routine, and conventional activities of storing and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that do not amount to significantly more than the abstract idea.
The claim also recites further details or specifics the additional elements of “[(a)] acquiring,” in that “[[(a)] acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[[(b)] acquiring a set of parameters] . . . , [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used, and [(b.2)] include one or more of . . . ,” and of the additional element of “[(c)] acquiring additional parameters,” “[(c.1)] wherein the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Thus, claim 7 is subject-matter ineligible.
Claim 13 recites an computer program product, which is an article of manufacture, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitation of “applying the parameter to the model to determine the remaining life of the target disk.” The activity of “[(e)] determine” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 13 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “machine,” and “a non-transitory computer readable medium . . . including machine-executable instructions, wherein the machine-executable instructions, when executed, cause a machine to perform steps.” The execution of instructions on generic computer components (a machine, a non-transitory computer readable medium) does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not integrate the abstract idea into a practical application. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(d)] training,” “[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the abstract idea.
The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. The claim also recites further details or specifics the additional elements of “acquiring,” in that “[(a) acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[(b) acquiring a parameter] . . . , [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used, and [(b.2)] include one or more of ” and of the additional element of “[(c)] acquiring additional parameters,” “[(c.1)] wherein the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, is the post-processing, insignificant activity of transmitting data over a network, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Therefore, claim 13 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a “machine,” and “a non-transitory computer readable medium . . . including machine-executable instructions, wherein the machine-executable instructions, when executed, cause a machine to perform steps.” The execution of instructions on generic computer components (a machine, a non-transitory computer readable medium) does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim also recites “a disk, a target disk, and a group of reference disks, which are generic computer components used in implementation of the abstract idea, (MPEP § 2106.05(f)), which do not amount to significantly more than the abstract idea. Also, the claim recites a “model,” which is recited at such a high-level of generality that it too is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. The claim recites “[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output.” The activity of “[(d)] training” is the use of the generic computer component (model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more details or specifics to the additional element of “[(d)] training,” “[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model,” and accordingly, is merely more specific to the additional element.
The claim also recites additional elements of “[(a)] acquiring a model for determining a remaining life of a disk,” “[(b)] acquiring a set of parameters related to a remaining life of a target disk,” and “[(c)] acquiring additional parameters for adjusting the training of the model.” The additional elements are well-understood, routine, and conventional activities of storing and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that do not amount to significantly more than the abstract idea. The claim also recites further details or specifics the additional elements of “acquiring,” in that “[(a) acquiring a model] . . . , [(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output,” and “[(b) acquiring a parameter] . . . , [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used,” and of the additional element of “[(c)] acquiring additional parameters,” “[(c.1)] wherein the additional parameters include at least a time range between a time point when the set of parameters are acquired and a time point when the failure occurs,” and accordingly, are merely more specific to the additional element.
The claim also recites “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.” The plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, is the well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Thus, claim 13 is subject-matter ineligible.
Claim 3 depends from claim 1. Claim 9 depends from claim 7. Claim 15 depends from claim 13. The claims recite more details or specifics to the additional element of “model,” (claims 3, 9, and 15: “wherein the model is a random forest model or a neural network model”), and accordingly, are merely more specific to the additional element. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Thus, claims 3, 9, and 15 are subject-matter ineligible.
Claim 5 depends directly or indirectly from claim 1. Claim 11 depends directly or indirectly from claim 7. Claim 17 depends directly or indirectly from claim 13. The claims recite more details or specifics to the additional element of “acquiring additional parameters,” (claims 5, 11, and 17: “wherein the additional parameters include at least one of the following: weights of the set of parameters; and the number of trees included in the model, the model being a random forest model”), and accordingly, are merely more specific to the additional element. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Thus, claims 5, 11, and 17 are subject-matter ineligible.
Claim 6 depends from claim 1. Claim 12 depends from claim 7. Claim 18 depends from claim 13. The claims recite a mental process, (claims 6, 12, and 18: “determining that the target disk needs to be replaced if it is determined that the remaining life is shorter than a threshold remaining time”), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(g)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Thus, claims 6, 12, and 18 are subject-matter ineligible.
Claim Rejections – 35 U.S.C. § 103
5. 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.
6. 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.
7. 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.
8. Claims 1, 3, 5-7, 9, 11-13, 15, 17, and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200104200 to Kocberber et al. [hereinafter Kocberber] in view of US Published Application 9189309 to Ma et al. [hereinafter Ma], and US Published Application 20200371693 to Lyu [hereinafter Lyu].
Regarding claims 1, 7, and 13, Kocberber teaches [a] method for managing a disk (Kocberber ¶ 0130 teaches a “Database as a Service (DBaaS) in which consumers use a database server or Database Management System that is running upon a cloud infrastructure, while a DbaaS provider manages or controls the underlying cloud infrastructure, applications, and servers, including one or more database servers [(that is, a method for managing a disk)]) of claim 1, [an] electronic device (Kocberber ¶ 0131 teaches “techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices”) of claim 7, and [a] computer program product tangible stored in a non-transitory computer-readable medium and including machine-executable instructions (Kocberber ¶ 0136 teaches “the techniques herein are performed by computer system 900 in response to processor 904 executing one or more sequences of one or more instructions contained in main memory 906. Such instructions may be read into main memory 906 from another storage medium, such as storage device 910””) of claim 13, including:
[(a)] acquiring a model (Kocberber ¶ 0085 teaches “[a] machine learning model is trained using a particular machine learning algorithm [(that is “a particular machine learning algorithm” is acquiring a model)] for determining a remaining life of a disk (Kocberber, Fig 1a, teaches acquiring a model from a plurality of machine learning models 120 [Examiner annotations in dashed-line text boxes]:
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Kocberber ¶ 0084 teaches “[u]nderstanding the remaining useful life of a disk is a significant factor in helping businesses with capacity planning, allocation, and forecasting [(that is, acquiring a model for determining a remaining life of a disk)]”),
[(a.1)] wherein the model is trained (Kocberber ¶ 0085 teaches “[o]nce trained, input is applied to the machine learning model to make a prediction, which may also be referred to herein as a predicated output or output [(that is, wherein the model is trained )]”) by taking a set of parameters related to a failure of a group of reference disks as an input (Kocberber, Fig. 6, describes preprocessing parameters 620 [Examiner annotations in dashed-line text boxes]:
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Kocberber ¶ 0046 teaches a “preprocessing phase goes through the disk drive sensor readings corresponding to failed disks [(that is, a group of reference disks)], creates enhanced sequences of data, and outputs the enhanced sequences; Kocberber ¶ 0075 teaches that “Some of the preprocessing parameters [(that is, taking a set of parameters related to a failure)] that may be specified include, without limitation, Heads_up_Period 621, Failure_Time 622, Healthy/Failed Disk ratio 623, and E-Factor 624 [(that is, taking a set of parameters related to a failure of a group of reference disks as an input)]”) and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output (Kocberber, Fig. 3, teaches successfully predicting failure on disk failure time [Examiner annotations in dashed-line text boxes]:
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Kocberber ¶ 0050 teaches “Fig. 3 depicts the relationship between the failure_time parameter, the heads_up_time parameter, and the last_valid_sample time parameter defining the preprocessing that establishes the training sequence of data. Thus, for a timeline of days 310, when the heads_up_time parameter 330 is specified, then, for successfully predicting an impending failure on disk failure time 320 [(that is, taking a reference remaining life of the group of reference disks)], the last valid sample to be used for training must be last_valid_sample_time 330 [(that is, “last_valid_sample_time” is at the time when the set of parameters are acquired as an output)]. Any of (including all) the disk drive sensor readings obtained on or before the last_valid_sample_time may be provided in the training phase to the machine learning model”);
[(b)] acquiring a set of parameters related to a remaining life of a target disk (Kocberber, Fig. 1b, teaches “a machine learning system for disk drive failure prediction after training the machine learning model [Examiner annotations in dashed-line text boxes]:”
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Kocberber ¶ 0038 teaches “FIG. 1b depicts the trained machine learning model 160 that may be used subsequently with Disk Drive Sensor Input Data 150 [(that is, acquiring a parameter related to a remaining life of a target disk)] in order to output Predicted Impending Disk Failure Information 170”),
[(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used (Kocberber, Fig. 2, teaches input data received from disk drive sensor [Examiner annotations in dashed-line text boxes]:”
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Kocberber ¶ 0044 teaches Figure 2 “shows that P sensor attributes 210 are received for each disk, the sensor attributes are received over N days 220, and the sensor attributes are received for Q disks in total 230 [(that is, wherein the parameter indicates usage information of the target disk when it is used)]”) . . . ; and
[(c)] acquiring additional parameters for adjusting the training of the model (Kocberber ¶ 0108 teaches “[a]s model training continues with additional input samples [(that is, “additional input samples” is acquiring additional parameters)], the error of the ANN should decline. Training may cease when the error stabilizes (i.e. ceases to reduce) or vanishes beneath a threshold (i.e. approaches zero) [(that is, a “error the ANN should decline” is for adjusting the training of the model)]”),
[(c.1)] wherein the additional parameters include at least a time range between a timepoint when the set of parameters are acquired and a time point when the failure occurs (Kocberber, Fig. 3, teaches a relationship between some parameters [Examiner annotations in dashed-line text-boxes]:
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Kocberber ¶ 0043 teaches “disk sensor data contain sensor readings that are collected from a disk for a certain period of time. A sample contains disk sensor attributes, i.e., readings of different sensors, on a given day. In order to characterize the training process, consider that P attributes are obtained from sensor data for a single disk. Additionally, samples collected on N consecutive days [(that is, a timepoint when the set of parameters are acquired)] is termed a sequence of size N. Then, the sensor data from a disk corresponds to a <N×P> matrix”; Kocberber ¶ 0040 teaches “FIG. 3 depicts the relationship between the failure_time parameter, the heads_up_time parameter, and the last_valid_sample time parameter defining the preprocessing that establishes the training sequence of data. Thus, for a timeline of days 310, when the heads_up_time parameter 330 is specified [(that is, at least a time range between a timepoint when the set of parameters are acquired and a time point when the failure occurs)], then, for successfully predicting an impending failure on disk failure time 320, the last valid sample to be used for training must be last_valid_sample_time 330”);
[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output (Kocberber ¶ 0108 teaches “[a]s model training continues with additional input samples [(that is, “additional input samples” is the additional parameters)], the error of the ANN should decline. Training may cease when the error stabilizes (i.e. ceases to reduce) or vanishes beneath a threshold (i.e. approaches zero) [(that is, training the model by taking the set of parameters and the additional parameters as input and taking the reference remining life as an output)]”),
[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model (Kocberber ¶ 0062 teaches “[raw data] readings may represent disk drive attribute values generated by disk drive sensors that are monitoring the disk drives. Sets raw data readings are used to form one or more training data set [(that is, a first subset of the set of the parameters is used to train the model)], test sets [(that is, a second subset of the set of parameters is used to test the model)] as well as validation sets for training a RNN LSTM deep learning model”);
[(e)] applying the parameter to the model to determine the remaining life of the target disk (Kocberber, Fig. 5, teaches the use of the model to determining the remaining life of the target disk [Examiner annotations in dashed-line text boxes]:
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Kocberber ¶ 0071 teaches “[a]fter the training of the RNN LSTM is completed, the Preprocessing Module 503 b will receive the raw disk drive sensor data 508 [(that is, applying the parameter to the model)] to be analyzed by the trained RNN LSTM model 507 for making predictions about disk failures [(that is, “predictions about disk failures” is to determine the remaining life of the target disk)]”; Kocberber ¶ 0077 teaches a “GUI 600 also may include an output display component 640, that may display for a set of one or more Disk IDs 641 [(that is, the target disk)], an Impending Failure Time 642, as well as a Confidence Measure 643 associated with the impending failure date prediction [(that is, remaining time)]”); and
* * *
Though Kocberber teaches a disk drive is considered to have failed when the disk drive is replaced because it has either stopped working or is showing signs of impending failure, Kocberber, however, does not explicitly teach –
* * *
[(b) acquiring a set of parameters related to a remaining life of a target disk],
* * *
[(b.2)] include one or more of:
command timeout,
load cycle count,
currently pending sector,
offline uncorrectable,
total logical block address writes, and
total logical block address reads;
* * *
But Ma teaches –
* * *
[(b) acquiring a set of parameters related to a remaining life of a target disk],
* * *
[(b.2)] include one or more of (Ma, Appendix I, teaches “S.M.A.R.T. Attributes [(that is, a set of parameters indicate usage information of the target disk when it is used, and include one or more of)]”):
command timeout (Ma, Appendix I, teaches “188 0xBC Command Timeout”),
load cycle count (Ma, Appendix I, teaches “193 0xC 1 Load Cycle Count or Load/Unload Cycle Count”),
currently pending sector (Ma, Appendix I, teaches “197 0xC5 Current Pending Sector Count”),
offline uncorrectable (Ma, Appendix I, teaches “198 0xC6 Uncorrectable Sector Count or Offline Uncorrectable or Off-Line Scan Uncorrectable Sector Count”),
total logical block address writes (Ma, Appendix I, teaches “241 0xF1 Total [logical block addresses (LBAs)] Written”), and
total logical block address reads Ma, Appendix I, teaches “242 0xF2 Total [logical block addresses (LBAs)] Read”);
* * *
Kocberber and Ma are from the same or similar field of endeavor. Kocberber teaches predicting disk drive failure using a machine learning model. Ma teaches he remaining lifetime can provide information on how much longer the storage drive can be in operation to estimate the failure date of the storage device.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Kocberber pertaining to a disk failure time prediction with the parameters relating to remaining disk life of Ma.
The motivation to do so is because there is “a need for an improved life time and performance for solid state drives.” (Ma ¶ 0003).
Though Kocberber and Ma teaches a disk drive is considered to have failed when the disk drive is replaced and informing a user about the time to replace the drive, the combination of Kocberber and Ma, however, does not explicitly teach –
[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.
But Lyu teaches -
* * *
[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk (Lyu ¶ 0070 teaches “exchanging (e.g., manually, automatically, or virtually) an old storage volume [(that is, the target disk)] with a newer storage volume within a same RAID array or in a different RAID array of the storage system [(that is, “actively exchanging” is actively replacing the target disk before the failure of the target disk)]. In some embodiments, the exchange uses a spare storage volume as an intermediary. In some embodiments, the exchanging ensures that the newer storage volume has a predicted remaining life exceeding the older storage volume by a predetermined threshold [(that is, actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk)]”).
Kocberber, Ma, and Lyu are from the same or similar field of endeavor. Kocberber teaches predicting disk drive failure using a machine learning model. Ma teaches he remaining lifetime can provide information on how much longer the storage drive can be in operation to estimate the failure date of the storage device. Lyu teaches predicting remaining life of disk storage volumes and exchanging an old disk storage value with a newer disk storage volume.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Kocberber and Ma pertaining to a disk failure time prediction with the disk storage volume replacement of Lyu.
The motivation to do so is because, “as a result of the exchange, the storage system can realize a lowered global probability of data loss.” (Kocberber ¶ 0070).
Regarding claims 3, 9, and 15, the combination of Kocberber, Ma, and Lyu teaches all of the limitations of claims 1, 7, and 13, respectively, as described above in detail.
Kocberber teaches -
wherein the model is a random forest model or a neural network model (Kocberber ¶ 0037 teaches “system 100 may have stored within it, several machine learning models 120 that may be used for training. These machine learning models may include, without limitation, Random Forest 122 [(that is, a random forest model)], Autoencoder 124, Multilayer Perceptron 126, and Recurrent Neural Networks (RNN)/Long Short-Term Memory (LSTM) 128 [(that is, a neural network model)]”).
Regarding claims 5, 11, and 17, the combination of Kocberber, Ma, and Lyu teaches all of the limitations of claims 1, 7, and 13, respectively, as described above in detail.
Kocberber teaches -
wherein the additional parameters include at least one of the following:
weights of the set of parameters (Kocberber ¶ 0093 teaches that “[f]rom each neuron in the input layer [(that is, the “input layer” receives the set of parameters)] and a hidden layer, there may be one or more directed edges to an activation neuron in the subsequent hidden layer or output layer. Each edge is associated with a weight. An edge from a neuron to an activation neuron represents input from the neuron to the activation neuron, as adjusted by the weight [(that is, “adjusted by weight” is in relation to the set of parameters)]”; Kocberber ¶ 0097 teaches the “The artifact of a neural network may comprise matrices of weights and biases. Training a neural network may iteratively adjust the matrices of weights and biases [(that is, weights of the set of parameters)]”); and
the number of trees included in the model, the model being a random forest model (Kocberber ¶ 0032 teaches “Random forests or random decision forests are an ensemble learning approach that construct a collection of randomly generated nodes and decision trees during the training phase. The different decision trees are constructed to be each randomly restricted to only particular subsets of feature dimensions of the data set. Therefore, the decision trees gain accuracy as the decision trees grow without being forced to over fit the training data as would happen if the decision trees were forced to be restricted to all the feature dimensions of the data set [(that is, the number of trees included in the model, the model being a random forest model)]”).
Regarding claims 6, 12, and 18, the combination of Kocberber, Ma, and Lyu teaches all of the limitations of claims 1, 7, and 13, respectively, as described above in detail.
Lyu teaches -
further including:
determining that the target disk needs to be replaced if it is determined that the remaining life is shorter than a threshold remaining time (Lyu ¶ 0069 teaches “identifying one or more old storage volumes in a storage system having one or more RAID arrays. Old storage volumes can have an estimated remaining life below a threshold [(that is, determining that the target disk needs to be replaced if it is determined that the remaining life is shorter than a threshold remaining time)]”).
Response to Argument
9. Examiner has fully considered Applicant’s arguments and amendments, and responds below accordingly.
Claim Rejections – 35 U.S.C. § 101
10. Exemplar claim 1 recites:
1. A method for managing a disk, including:
[(a)]1 acquiring a model for determining a remaining life of a disk,
[(a.1)] wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output;
[(b)] acquiring a set of parameters related to a remaining life of a target disk,
[(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used, and
[(b.2)] include one or more of:
command timeout,
load cycle count,
currently pending sector,
offline uncorrectable,
total logical block address writes, and
total logical block address reads; and
[(c)] acquiring additional parameters for adjusting the training of the model,
[(c.1)] wherein the additional parameters include at least a time range between a timepoint when the set of parameters are acquired and a time point when the failure occurs;
[(d)] training the model by taking the set of parameters and the additional parameters as an input and taking the reference remaining life as an output,
[(d.1)] wherein a first subset of the set of the parameters is used to train the model and a second subset of the set of parameters is used to test the model;
[(e)] applying the parameter to the model to determine the remaining life of the target disk; and
[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk.
11. With regards to the amended subject matter under Step 2A Prong Two, that “[a]s described in paragraphs [0045] and [0049] of the specification, Applicant respectfully asserts that independent claims 1, 7, and 13 recite a practical application of the alleged mental process by actively replacing a target disk before failure. For example, in paragraph [0049] states
For example, with the technical solution of the present disclosure, a remaining life of a disk can be predicted, so that the disk can be actively replaced before it fails. This not only can increase the reliability of a storage system, but also can reduce the time taken to reconstruct the storage system, thereby improving the user experience of a user of the storage system.
In this manner, the target disk is actively replaced before the failure of the target disk, thus increasing the reliability of the storage system. Accordingly, Applicant respectfully submits the amended independent claims 1, 7, and 13 recite an improvement in the functioning of a storage system and are therefore directed to patentable subject matter under Step 2A.
The subject action states ‘the plain meaning of 'actively replacing' includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant's disclosure, (MPEP § 2111 ), and accordingly, under Step 2A Prong Two, is the post-processing, insignificant activity of transmitting data over a network, (MPEP § 2106.05(g)), and under Step 2B, is the well-understood, routine, and conventional activity of transmitting data over a network. (MPEP § 2106.05(d) sub 11.i).’ See subject action, p. 22; emphasis original. This interpretation of actively replacing is entirely inconsistent with the stated technical solution (i.e., actively replacing a disk before it fails) as noted above in paragraph [0049]. Actively replacing a disk before it fails requires the replacement of that disk. To construe that as ‘broadcasting and/or announcing a predictive result’ would lead to the possibility that a disk is not replaced before it fails, thus not accomplishing the technical solution nor the claimed feature.
Accordingly, Applicant respectfully submits that amended independent claims 1, 7, and 13 recite a practical application of the alleged abstract idea, and as such, recite patentable subject matter under § 101. As such, Applicant respectfully asserts that claims 1-3, 5-9, 11-15, and 17-18 are directed to patentable subject matter under 35 U.S.C. § 101. Applicant respectfully requests that the rejection of claims 1-3, 5-9, 11-15, and 17-18 under 35 U.S.C. § 101 be withdrawn.” (Response at pp. 7-8 (emphasis added by Applicant)).
Examiner’s Response:
Examiner respectfully disagrees. The Step 2A, Prong Two analysis considers the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application.’’ (MPEP § 2106.04(d) sub III; see 2024 SME Guidance, 89 Fed. Reg. 137, at p. 58136 (17 July 2024)).
The additional elements of the claim are those of “[(a)] acquiring a model,” “[(b)] acquiring a set of parameters,” and “[(c)] acquiring additional parameters,” which are identified as insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application, and under are well-understood, routine, and conventional activities of storing and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that do not amount to significantly more than the abstract idea.
With regard to the additional element of “a model,” this is a generic computer component used to implement the abstract idea of “determine the remaining life,” and “[(d)] training the model,” and [(e)] applying the parameter to the model.” However, the claim simply recites an abstract idea with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception is performed using the “model,” which is used generally to apply the abstract idea without placing any limits on how the model functions. Rather, these limitations only recite the outcome of “determine the remaining life” of a component, and do not include any details about how the “determining” is accomplished, such determining being a mental process. (MPEP § 2106.04(a)(2) sub III)).
With regard to the limitation of “[(f)] actively replacing the target disk before the failure of the target disk based upon, at least in part, the remaining life of the target disk,” the limitation does not provide any details or specifics as to how “actively replacing” performed. That is, under Step 2A Prong Two, claim that integrates an abstract idea into a practical application of the exception will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the abstract idea, such that the claim is more than a drafting effort designed to monopolize or preempt the judicial exception.
Further, as set out above in detail, the plain meaning of “actively replacing” includes broadcasting and/or announcing a predictive result of the model, which under a broadest reasonable interpretation includes the activity of transmitting the predictive result, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and accordingly, under Step 2A Prong Two, is the post-processing, insignificant activity of transmitting data over a network, (MPEP § 2106.05(g)), and under Step 2B, is the well-understood, routine, and conventional activity of transmitting data over a network. (MPEP § 2106.05(d) sub II.i).
Applicant points to the Specification regarding the term “actively replacing,” where the language is directed to an intended result:
Through the above description with reference to FIGS. 1 to 3, the technical solutions according to the embodiments of the present disclosure have many advantages over conventional solutions. For example, with the technical solution of the present disclosure, a remaining life of a disk can be predicted, so that the disk can be actively replaced before it fails. This not only can increase the reliability of a storage system, but also can reduce the time taken to reconstruct the storage system, thereby improving the user experience of a user of the storage system.
(see Specification ¶ 0049 (emphasis added by Examiner)). The disclosure, however, does not explain how such action would be taken, other than given a condition precedent of receiving a prediction of remaining life of a disk need occur. Such receipt, under a reasonable broadest interpretation, includes “broadcasting and/or announcing a predictive result of the model,” as set out above in detail.
Accordingly, as set out above in detail, claims 1-3, 5-9, 11-15, 17, and 18 are subject-matter ineligible.
Claim Rejections – 35 U.S.C. § 103
12. Applicant submits that “Applicant respectfully submits that the combination of Kocberber, Lyu, and/or Xu, fails to teach, disclose, or even suggest
* * *
[(b)] acquiring a set of parameters related to a remaining life of a target disk, wherein the set of parameters indicate usage information of the target disk when it is used, and include one or more of:
command timeout,
load cycle count,
currently pending sector,
offline uncorrectable,
total logical block address writes, and
total logical block address reads; and
* * *
However, Applicant notes that the subject action does not provide any detailed rejection and mapping for the set of parameters to ’[(b.2)] include one or more of: command timeout, load cycle count, currently pending sector, offline uncorrectable, total logical block address writes, and total logical block address reads‘ with respect to the Xu reference. Further, Applicant respectfully submits that while Xu may teach certain attributes, the combination of Kocberber, Lyu, and Xu does not appear to teach, disclose, or even suggest ’[(b)] acquiring a set of parameters related to a remaining life of a target disk, [(b.1)] wherein the set of parameters indicate usage information of the target disk when it is used, and [(b.2)] include one or more of: command timeout, load cycle count, currently pending sector, offline uncorrectable, total logical block address writes, and total logical block address reads.’ As such, Applicant respectfully submits that the combination of Kocberber, Lyu, and Xu fails to disclose, or even suggest the claimed feature as recited by amended independent claim 1.” (Response at p. 11 (emphasis added by Applicant)).
Examiner’s Response:
Examiner agrees that neither Kocberber and/or Lyu teach the features relating to the “set of parameters” of the instant claims.
Examiner relies upon the teachings of Ma for this limitation, as is set out above in detail.
Conclusion
13. 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.
14. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20170131948 to Huang et al.) teaches methods and systems to calculate and show the effect of software, firmware or load on a solid state drive. A solid state drive can have flash memories, which can be degraded and replaced after a certain time in operation. For example, the operation time of a solid state drive can be directly related to a maximum number of program/erase cycles (sometimes called erase counts) of the flash memories.
(Pinheiro et al., "Failure Trends in a Large Disk Drive Population," UseNIX (2007)) teaches Our analysis identifies several parameters from the drive’s self-monitoring facility (SMART) that correlate highly with failures. Despite this high correlation, we conclude that models based on SMART parameters alone are unlikely to be useful for predicting individual drive failures.
15. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 References to the limitations are used for the limited purpose of aiding the subject matter evaluation under Section 101.