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 .
Claims 1 – 20 are pending.
Priority
The earliest application to which the instant application claims priority that includes proper support for the claimed subject matter is provisional application 63/579,258 filed 28 August 2023. Therefore, the claims of the instant application are given an earliest effective filing date of 28 August 2023.
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
Applicant argues that amended claims 1, 8, and 15 are not directed to an abstract idea because the amended claims now require a practical application that provides a technological improvement in the operation of computer-implemented storage-system management. However, the Examiner respectfully disagrees. The amended claims do not integrate the abstract idea into a practical application. The gathering step is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The generative artificial intelligence (AI) model that performs the generating step is also recited at a high level of generality, and merely automates the generating step. Each of the limitations is no more than mere instructions to apply the exception using a generic computer component. The combination of these elements is no more than mere instructions to apply the exception using a generic computer component (the generative AI model). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Additionally, the additional elements are no more than what is well-understood, routine, and conventional in the field. The specification does not provide any indication that the additional elements are anything other than generic, off-the-shelf computer components. Accordingly, the additional elements are well-understood, routine, and conventional. Therefore, there is no inventive concept in the claims and they are thus ineligible.
The rejections of claims 2 – 7, 9 – 14, and 16 – 20 are also maintained because of similar reasoning to that set forth above.
Applicant argues that the prior art of record fails to teach the limitations of independent claims 1, 8, and 15. Specifically, Applicant argues that US Patent No. 10,726,930 (hereinafter Sarkar) does not teach generating a machine-generated characterization of a storage medium based on runtime activity associated with one or more workloads. However, the Examiner respectfully disagrees. Sarkar teaches a machine learning model used to determine whether a data storage system is likely to fail or not (Sarkar; Col 7 Lines 50 – 53). The determination is based on inputs provided to the machine learning model during running of the data storage system (Sarkar; Col 7 Lines 44 – 50). The inputs input to the machine learning model can be considered to be runtime activity associated with one or more workloads. The inputs are provided to the machine learning model in real-time or near real-time and provided during the running of the storage system (Sarkar; Col 7 Lines 44 – 50). The inputs are also used by the machine learning model to evaluate the health of the storage system while the storage system is operating and may be undergoing stress (Sarkar; Col 7 Lines 52 – 57). Therefore, the Examiner maintains that the prior art of record, specifically Sarkar, teaches all the limitation of independent claims 1, 8, and 15.
Applicant also argues that Sarkar does not teach simulating the runtime activity for the particular storage medium by replaying the one or more workloads, as required by dependent claims 4, 11, and 18. However, the Examiner respectfully disagrees. Sarkar teaches providing a stored data set to the model in the same order of time as which the data was generated (Sarkar; Col 7 Lines 22 – 40). The Examiner submits that providing the stored data set to the machine learning model could be considered simulating the runtime activity for a storage medium by replaying the one or more workloads, as the data set is provided in the same order of time as the data was generated, which would simulate a particular state of the system. Therefore, the Examiner maintains that the prior art of record, particularly Sarkar, teaches all the limitations of dependent claims 4, 11, and 18. Similar reasoning is also applied to Applicant’s arguments with respect to dependent claims 6, 13, and 20.
Applicant’s arguments with respect to dependent claims 5, 7, 12, 14, and 19 are persuasive.
Applicant also argues that Sarkar in combination with US Patent No. 10,956,059 (hereinafter Thakkar) does not teach the limitations of dependent claims 2, 3, 9, 10, 16, and 17. However, the Examiner respectfully disagrees. Thakkar teaches using a clustering algorithm to classify one or more storage systems into one of multiple predetermined classification groups (Thakkar; Col 6 Lines 15 – 26). The clustering algorithm classification is based on processed input data, which includes storage system configuration data, storage system operations data, storage system heuristic-based health scores, and/or user service request counts associated with the storage systems (Thakkar; Col 5 Line 65 – Col 6 Line 5). The classification of the storage system is equated to the claimed identification of the plurality of storage media. The classification is done based on the clustering algorithm, which is equated to the claimed cohort analysis. Therefore, the Examiner maintains that the teachings of Thakkar, when combined with the teachings of Sakar, teach all the limitations of the claimed invention.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
With respect to claim 1, under the Alice framework Step 1, the claim recites a method.
Under the Alice framework Step 2A prong 1 analysis, claim 1 recites an abstract idea in the grouping of mental process. The claim recites “generating, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” which could be performed in the human mind. Specifically, a human could view attributes of a storage device and come up with a characterization of the storage device in their mind. Therefore, the claim recites an abstract idea.
Under the Alice framework Step 2A prong 2 analysis, claim 1 recites additional elements of "a plurality of storage media," "a particular storage system," and “a generative artificial intelligence (AI) model.” These elements are recited at a high level of generality and fail to include limitations that detail the structure of the claimed elements, or how they function. Accordingly, these elements fail to provide a meaningful limitation on the claimed steps, and amount to no more than mere instructions to apply the exception using generic computer components. Claim 1 additionally recites “gathering data describing characteristics of a plurality of storage media” which merely adds insignificant extra-solution activity to the judicial exception, and therefore does not integrate the judicial exception into a practical application. The limitation of "gathering data describing characteristics of a plurality of storage media" is merely data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (See MPEP 2106.05(g)). The additional element of "a generative artificial intelligence (AI) model" recited in claim 1 is merely instructions to implement the abstract idea on a computer and/or merely using a computer as a tool to perform the abstract idea. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception 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 of “generating, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” is performed “by a generative artificial intelligence (AI) model.” The generative AI model is used to generally apply the abstract idea without placing any limits on how the generative AI model functions. Rather, these limitations only recite the outcome of “generating based on the data and one or more attributes of a particular storage medium, a characterization of the particular storage medium” and do not include any details about how the “generating” step is accomplished. See MPEP 2106.05(f). The recitation of “by a generative artificial intelligence (AI) model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “by a generative artificial intelligence (AI) model” limits the identified judicial exception “generating, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” this type of limitation merely confines the use of the abstract idea to a particular technological environment (generative AI model) and thus fails to add an inventive concept to the claim. Additionally, see the Examiner’s “Response to Arguments” presented above.
Under the Alice framework Step 2B analysis, the claimed limitation of "gathering data describing characteristics of a plurality of storage media" is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and are well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). The additional elements of "a plurality of storage media" and "a particular storage medium" amount to no more than mere instructions to apply the judicial exception using generic computer components. Additionally, the additional element of "by a generative artificial intelligence (AI) model" is an instruction to "apply" the abstract idea, which cannot provide an inventive concept (See MPEP 2106.05(f)). Additionally, see the Examiner’s “Response to Arguments” presented above.
Therefore, claim 1 is directed to a judicial exception and is not patent eligible.
Claims 2 – 7 are rejected for at least the reasons provided with respect to claim 1. Claims 2 – 7 contain no further additional elements beyond claim 1 that would require consideration under Step 2A prong 2 or Step 2B. Additionally, see the Examiner’s “Response to Arguments” presented above.
With respect to claim 8, under the Alice framework Step 1, the claim recites a system.
Under the Alice framework Step 2A prong 1 analysis, claim 8 recites an abstract idea in the grouping of mental process. The claim recites “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” which could be performed in the human mind. Specifically, a human could view attributes of a storage device and come up with a characterization of the storage device in their mind. Therefore, the claim recites an abstract idea.
Under the Alice framework Step 2A prong 2 analysis, claim 8 recites additional elements of “a memory,” “a processing device,” "a plurality of storage media," and "a particular storage system." These elements are recited at a high level of generality and fail to include limitations that detail the structure of the claimed elements, or how they function. Accordingly, these elements fail to provide a meaningful limitation on the claimed steps, and amount to no more than mere instructions to apply the exception using generic computer components. Claim 8 additionally recites “gather data describing characteristics of a plurality of storage media” which merely adds insignificant extra-solution activity to the judicial exception, and therefore does not integrate the judicial exception into a practical application. The limitation of "gather data describing characteristics of a plurality of storage media" is merely data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (See MPEP 2106.05(g)). The additional element of "a generative artificial intelligence (AI) model" recited in claim 8 is merely instructions to implement the abstract idea on a computer and/or merely using a computer as a tool to perform the abstract idea. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception 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 of “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” is performed “by a generative artificial intelligence (AI) model.” The generative AI model is used to generally apply the abstract idea without placing any limits on how the generative AI model functions. Rather, these limitations only recite the outcome of “generate based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” and do not include any details about how the “generate” step is accomplished. See MPEP 2106.05(f). The recitation of “by a generative artificial intelligence (AI) model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “by a generative artificial intelligence (AI) model” limits the identified judicial exception “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (generative AI model) and thus fails to add an inventive concept to the claim. Additionally, see the Examiner’s “Response to Arguments” presented above.
Under the Alice framework Step 2B analysis, the claimed limitation of "gather data describing characteristics of a plurality of storage media" is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and are well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). The additional elements of “a memory,” “a processing device,” "a plurality of storage media," and "a particular storage medium" amount to no more than mere instructions to apply the judicial exception using generic computer components. Additionally, the additional element of "by a generative artificial intelligence (AI) model" is an instruction to "apply" the abstract idea, which cannot provide an inventive concept (See MPEP 2106.05(f)). Additionally, see the Examiner’s “Response to Arguments” presented above.
Therefore, claim 8 is directed to a judicial exception and is not patent eligible.
Claims 9 – 14 are rejected for at least the reasons provided with respect to claim 8. Claims 9 – 14 contain no further additional elements beyond claim 1 that would require consideration under Step 2A prong 2 or Step 2B. Additionally, see the Examiner’s “Response to Arguments” presented above.
With respect to claim 15, under the Alice framework Step 1, the claim recites a non-transitory computer readable medium.
Under the Alice framework Step 2A prong 1 analysis, claim 15 recites an abstract idea in the grouping of mental process. The claim recites “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” which could be performed in the human mind. Specifically, a human could view attributes of a storage device and come up with a characterization of the storage device in their mind. Therefore, the claim recites an abstract idea.
Under the Alice framework Step 2A prong 2 analysis, claim 15 recites additional elements of “a processing device,” "a plurality of storage media," and "a particular storage system." These elements are recited at a high level of generality and fail to include limitations that detail the structure of the claimed elements, or how they function. Accordingly, these elements fail to provide a meaningful limitation on the claimed steps, and amount to no more than mere instructions to apply the exception using generic computer components. Claim 15 additionally recites “gather data describing characteristics of a plurality of storage media” which merely adds insignificant extra-solution activity to the judicial exception, and therefore does not integrate the judicial exception into a practical application. The limitation of "gather data describing characteristics of a plurality of storage media" is merely data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (See MPEP 2106.05(g)). The additional element of "a generative artificial intelligence (AI) model" recited in claim 15 is merely instructions to implement the abstract idea on a computer and/or merely using a computer as a tool to perform the abstract idea. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception 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 of “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” is performed “by a generative artificial intelligence (AI) model.” The generative AI model is used to generally apply the abstract idea without placing any limits on how the generative AI model functions. Rather, these limitations only recite the outcome of “generate based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads” and do not include any details about how the “generate” step is accomplished. See MPEP 2106.05(f). The recitation of “by a generative artificial intelligence (AI) model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “by a generative artificial intelligence (AI) model” limits the identified judicial exception “generate, by a generative artificial (AI) model, based on the data and one or more attributes of a particular storage medium, a machine-generated characterization of the particular storage medium, the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (generative AI model) and thus fails to add an inventive concept to the claim. Additionally, see the Examiner’s “Response to Arguments” presented above.
Under the Alice framework Step 2B analysis, the claimed limitation of "gather data describing characteristics of a plurality of storage media" is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and are well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). The additional elements of “a processing device,” "a plurality of storage media," and "a particular storage medium" amount to no more than mere instructions to apply the judicial exception using generic computer components. Additionally, the additional element of "by a generative artificial intelligence (AI) model" is an instruction to "apply" the abstract idea, which cannot provide an inventive concept (See MPEP 2106.05(f)). Additionally, see the Examiner’s “Response to Arguments” presented above.
Therefore, claim 15 is directed to a judicial exception and is not patent eligible.
Claims 16 – 20 are rejected for at least the reasons provided with respect to claim 15. Claims 16 – 20 contain no further additional elements beyond claim 1 that would require consideration under Step 2A prong 2 or Step 2B. Additionally, see the Examiner’s “Response to Arguments” presented above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 6, 8, 11, 13, 15, 18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent No. 10,726,930 (hereinafter Sarkar).
As per claims 1, 8, and 15, Sarkar teaches a method comprising: gathering data describing characteristics of a plurality of storage media (Sarkar; Col 5 Lines 53 – 67); and generating, by a generative artificial intelligence (AI) model, based on the data (Sarkar; Col 5 Lines 53 – 67) and one or more attributes of a particular storage medium (Sarkar; Col 6 Lines 1 – 28), a machine-generated characterization of the particular storage medium (Sarkar; Col 7 Lines 58 – 63), the machine-generated characterization comprising one or more characteristics of the particular storage medium determined based on runtime activity associated with one or more workloads (Sarkar; Col 7 Lines 41 – 57) (see Response to Arguments presented above).
As per claims 4, 11, and 18, Sarkar also teaches wherein generating the machine-generated characterization of the particular storage medium comprises simulating runtime activity for the particular storage medium by replaying one or more workloads (Sarkar; Col 7 Lines 22 – 40) (see Response to Arguments presented above).
As per claims 6, 13, and 20, Sarkar also teaches wherein determining the one or more characteristics of the particular storage medium comprises: simulating runtime activity for a particular workload associated with the particular storage medium (Sarkar; Col 7 Lines 22 – 40) (see Response to Arguments presented above).
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.
Claim(s) 2, 3, 9, 10, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,726,930 (hereinafter Sarkar) in view of US Patent No. 10,956,059 (hereinafter Thakkar).
As per claims 2, 9, and 16, Sarkar teaches the invention as described per claims 1, 8, and 15 (see rejection of claims 1, 8, and 15 above).
Sarkar does not teach wherein gathering the data describing the characteristics of the plurality of storage media comprises identifying the plurality of storage media based on a cohort analysis for the particular storage media.
However, Thakkar teaches gathering characteristics of a plurality of storage devices including identifying the plurality of storage devices based on a cohort analysis (“clustering algorithm”) for the particular storage media (Thakkar; Col 5 Line 64 – Col 6 Line 26) (see Response to Arguments presented above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sarkar to include the cohort analysis because doing so allows for providing storage system insights (Thakkar; Col 7 Lines 17 – 20).
As per claims 3, 10, and 17, Thakkar also teaches wherein the cohort analysis is based on the one or more attributes of the particular storage medium and another one or more attributes of the plurality of storage media (Thakkar; Col 5 Line 64 – Col 6 Line 26).
Claim(s) 5, 7, 12, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,726,930 (hereinafter Sarkar) in view of US Patent No. 10,698,460 (hereinafter Ping).
As per claims 5, 12, and 19, Sarkar teaches the invention as described per claims 1, 8, and 15 (see rejection of claims 1, 8, and 15 above).
Sarkar does not teach wherein the machine-generated characterization further comprises one or more estimated characteristics of the particular storage medium, and wherein the method further comprises: deriving the one or more estimated characteristics of the particular storage medium from one or more characteristics of the particular storage medium.
However, Ping teaches a system in which an AI model generates a characterization of the storage device including one or more estimated characteristics of the storage device (Ping; Col 5 Lines 21 – 41) including deriving the one or more estimated characteristics of the storage device from one or more characteristics of the storage device (Ping; Col 5 Lines 21 – 41).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sarkar to include the estimated characteristics because doing so allows for making quick decisions regarding storage device control (Ping; Col 3 Line 66 – Col 4 Line 3).
As per claims 7 and 14, Sarkar teaches the invention as described per claims 1 and 8 (see rejection of claims 1 and 8 above).
Sarkar does not teach wherein the machine-generated characterization further comprises one or more projected characteristics of the particular storage medium, and wherein the method further comprises: deriving the one or more projected characteristics of the particular storage medium from one or more characteristics of the particular storage medium.
However, Ping teaches a system in which an AI model generates a characterization of the storage device including one or more projected characteristics of the storage device (Ping; Col 5 Lines 21 – 41) including deriving the one or more projected characteristics of the storage device from one or more characteristics of the storage device (Ping; Col 5 Lines 21 – 41).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sarkar to include the projected characteristics because doing so allows for making quick decisions regarding storage device control (Ping; Col 3 Line 66 – Col 4 Line 3).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD B FRANKLIN whose telephone number is (571)272-0669. The examiner can normally be reached M-F 8:30am-5pm.
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/RICHARD B FRANKLIN/ Examiner, Art Unit 2181
/IDRISS N ALROBAYE/ Supervisory Patent Examiner, Art Unit 2181
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