DETAILED ACTION
This action is in response to the Applicant Response filed 08 July 2026 for application 18/144,100 filed 05 May 2023.
Claim(s) 1, 10, 19, 21-22 is/are currently amended.
Claim(s) 8, 17 is/are cancelled.
Claim(s) 1-7, 9-16, 18-22 is/are pending.
Claim(s) 1-7, 9-16, 18-22 is/are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments regarding the objections to the claims have been fully considered and, in light of the amendments to the claims, are persuasive.
As applicant has selected to defer addressing the double patenting rejection until a later time, the double patenting rejection is maintained.
Applicants arguments regarding the 35 U.S.C. 101 rejection of claims 1-7, 9-16, 18-22 have been fully considered but are not persuasive. Applicant first asserts that the claims do not recite an abstract idea, but provides no evidence to support this assertion. As noted below, the claims do recite an abstract idea. Applicant next argues that the claims provide an improvement, specifically in the generating step. Examiner respectfully disagrees. First, Examiner notes that applicant argues limitations that are not included in claim 1. Further, as noted in the MPEP, it is important to note, the judicial exception alone cannot provide the improvement MPEP 2106.05(a), and an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Here, as noted below, the generating step is an abstract idea, and, therefore, any potential improvement is, at best, an improvement in the abstract idea. Therefore, claims 1-7, 9-16, 18-22 stand rejected under 35 U.S.C. 101.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7, 9-16, 18-22 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,669,754 (16/872,194). Although the claims at issue are not identical, they are not patentably distinct from each other because as noted in the table below, claims 1-7, 9-16, 18-22 of the instant application have similar limitations as recited in U.S. Patent No. 11,669,754 (claims 1-20) except for additional limitations included in U.S. Patent No. 11,669,754.
Application No. 17/900,522
U.S. Pat. No. 11,669,754 (16/872,194)
Claim 1
Claim 1
A method for training a machine learning model, the method comprising:
A method for training a machine learning model, the method comprising:
assigning, by a processor, one or more weighted values to data in a database, wherein the one or more weighted values are associated with an attribute of the data;
assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets;
generating, by the processor, a training dataset from the data, wherein an amount of data comprising the attribute of the data used to generate the training dataset is based on the one or more weighted values associated with the attribute of the data; and
generating, by the processor, a training dataset from the one or more datasets, wherein an amount of data generated from the one or more datasets is based on the one or more weighted values; and
training, by the processor, the machine learning model using the training dataset.
training, by the processor, the machine learning model using the training dataset.
Claim 2
Claim 2
wherein the machine learning model comprises a storage prediction model.
wherein the machine learning model comprises a solid-state drive (SSD) failure prediction model.
Claim 3
Claim 3
wherein data associated with a first attribute is assigned a first weighted value and data associated with a second attribute is assigned a second weighted value, wherein the first weighted value is different than the second weighted value.
wherein a most recent dataset from the one or more datasets is assigned a first weighted value and a least recent dataset from the one or more datasets is assigned a second weighted value, wherein the first weighted value is greater than the second weighted value.
Claim 4
Claim 4
wherein the one or more weighted values are changed by a set amount from the first weighted value to the second weighted value.
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
Claim 5
Claim 5
identifying, by the processor, a subset of data having a characteristic; and
identifying, by the processor, anomaly data in the dataset;
adding, by the processor, the subset of data to the training dataset.
adding, by the processor, the anomaly data to the training dataset.
Claim 6
Claim 6
wherein the subset of data comprises storage status data.
wherein the anomaly data comprises SSD failure data.
Claim 7
Claims 7-8
wherein the subset of data is identified using a rule based method or by using a cluster based method.
wherein the anomaly data is identified using a rule based method. (Claim 7)
wherein the anomaly data is identified using a cluster based method. (Claim 8)
Claim 9
Claim 5, Claim 9
identifying, by the processor, a subset of data having a characteristic;
identifying, by the processor, anomaly data in the dataset; (Claim 5)
generating, by the processor, additional data having the characteristic; and
generating, by the processor, anomaly data; (Claim 9)
adding, by the processor, the generated additional data to the training dataset.
adding, by the processor, the anomaly data to the training dataset. (Claim 5)
adding, by the processor, the generated anomaly data to the training dataset. (Claim 9)
Claim 10
Claim 10
A data system comprising:
A data system comprising:
a database;
a database;
a processor coupled to the database; and
a processor coupled to the database; and
a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to:
a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to:
assign one or more weighted values to data in a database, wherein the one or more weighted values are associated with an attribute of the data;
assign one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets;
generate a training dataset from the data, wherein an amount of data comprising the attribute of the data used to generate the training dataset is based on the one or more weighted values associated with the attribute of the data; and
generate a training dataset from the one or more datasets, wherein an amount of data generated from the one or more datasets is based on the one or more weighted values; and
train a machine learning model using the training dataset.
train a machine learning model using the training dataset.
Claim 11
Claim 11
wherein the machine learning model comprises a storage prediction model.
wherein the machine learning model comprises a solid-state drive (SSD) failure prediction model.
Claim 12
Claim 12
wherein data associated with a first attribute is assigned a first weighted value and data associated with a second attribute is assigned a second weighted value, wherein the first weighted value is different than the second weighted value.
wherein a most recent dataset from the one or more datasets is assigned a first weighted value and a least recent dataset from the one or more datasets is assigned a second weighted value, wherein the first weighted value is greater than the second weighted value.
Claim 13
Claim 13
wherein the one or more weighted values are changed by a set amount from the first weighted value to the second weighted value.
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
Claim 14
Claim 14
identify a subset of data having a characteristic; and
identify anomaly data in the dataset;
add the subset of data to the training dataset.
add the anomaly data to the training dataset.
Claim 15
Claim 15
wherein the subset of data comprises storage status data.
wherein the anomaly data comprises SSD failure data.
Claim 16
Claim 16-17
wherein the subset of data is identified using a rule based method or by using a cluster based method.
wherein the anomaly data is identified using a rule based method. (Claim 16)
wherein the anomaly data is identified using a cluster based method. (Claim 17)
Claim 18
Claim 14, Claim 18
identify a subset of data having a characteristic;
identify anomaly data in the dataset; (Claim 14)
generate additional data having the characteristic; and
generate anomaly data; (Claim 18)
add the generated additional data to the training dataset.
add the anomaly data to the training dataset. (Claim 14)
add the generated anomaly data to the training dataset. (Claim 18)
Claim 19
Claim 19
A method for training a machine learning model, the method comprising:
A method for training a machine learning model, the method comprising:
generating, by a processor, training dataset from data in a database;
identifying, by a processor, anomaly data in a dataset from a database;
identifying, by the processor, a training dataset from the dataset;
retrieving, by the processor, the training dataset from dataset;
identifying, by the processor, a subset of data having a characteristic;
identifying, by a processor, anomaly data in a dataset from a database;
generating, by the processor, additional data having the characteristic, the additional data being in addition to the data in the database;
generating, by the processor, additional anomaly data;
adding, by the processor, the generated additional data to the training dataset; and
adding, by the processor, the generated anomaly data to the dataset;
training, by the processor, the machine learning model using the training dataset.
training, by the processor, the machine learning model using the training dataset.
Claim 20
Claim 20
wherein the machine learning model comprises a storage prediction model.
wherein the machine learning model comprises a solid-state (SSD) failure prediction model.
Claim 21
Claim 4
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
Claim 22
Claim 13
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-7, 9-16, 18-22 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of assigning ... one or more weighted values to data in a database, wherein the one or more weighted values are associated with an attribute of the data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating ... a training dataset from the data, wherein an amount of data comprising the attribute of the data used to generate the training dataset is based on the one or more weighted values associated with the attribute of the data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – processor, database. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training ... the machine learning model using the training dataset which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
processor, database amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the machine learning model comprises a storage prediction model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the machine learning model comprises a storage prediction model which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – storage prediction model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
storage prediction model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of wherein data associated with a first attribute is assigned a first weighted value and data associated with a second attribute is assigned a second weighted value, wherein the first weighted value is different than the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of wherein the one or more weighted values are changed by a set amount from the first weighted value to the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of identifying ... a subset of data having a characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of adding ... the subset of data to the training dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model. The Step 2A Prong One Analysis for claim 5 is applicable here since claim 6 carries out the method of claim 5 but for the recitation of additional element(s) of wherein the subset of data comprises storage status data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of wherein the subset of data is identified using a rule based method or by using a cluster based method, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of identifying ... a subset of data having a characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating ... additional data having the characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of adding ... the generated additional data to the training dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of assign one or more weighted values to data in a database, wherein the one or more weighted values are associated with an attribute of the data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate a training dataset from the data, wherein an amount of data comprising the attribute of the data used to generate the training dataset is based on the one or more weighted values associated with the attribute of the data, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – data system, database, processor, memory, instructions. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites train a machine learning model using the training dataset which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
data system, database, processor, memory, instructions amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system. The Step 2A Prong One Analysis for claim 10 is applicable here since claim 11 carries out the system of claim 10 but for the recitation of additional element(s) of wherein the machine learning model comprises a storage prediction model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the machine learning model comprises a storage prediction model which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – storage prediction model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
storage prediction model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of wherein data associated with a first attribute is assigned a first weighted value and data associated with a second attribute is assigned a second weighted value, wherein the first weighted value is different than the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of wherein the one or more weighted values are changed by a set amount from the first weighted value to the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of identify a subset of data having a characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of add the subset of data to the training dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system. The Step 2A Prong One Analysis for claim 14 is applicable here since claim 15 carries out the system of claim 14 but for the recitation of additional element(s) of wherein the subset of data comprises storage status data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of wherein the subset of data is identified using a rule based method or by using a cluster based method, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of identify a subset of data having a characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate additional data having the characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of add the generated additional data to the training dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of generating ... training dataset from data in a database, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of identifying ... a subset of data having a characteristic, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating ... additional data having the characteristic, the additional data being in addition to the data in the database, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of adding ... the generated additional data to the training dataset, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – processor, database. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites training ... the machine learning model using the training dataset which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
processor, database amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 20 carries out the method of claim 19 but for the recitation of additional element(s) of wherein the machine learning model comprises a storage prediction model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the machine learning model comprises a storage prediction model which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – storage prediction model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
storage prediction model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 21, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for training a machine learning model.
The limitation of wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 22, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a system with a processor, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) data system.
The limitation of wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-4, 10, 12-13, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klinkenberg et al. (Concept Drift and the Importance of Examples, hereinafter referred to as “Klinkenberg”).
Regarding claim 1 (Currently Amended), Klinkenberg teaches a method for training a machine learning model (Klinkenberg, section 4 – teaches a method for training a machine learning model to handle concept drift; see also Klinkenberg, section 5), the method comprising:
assigning, by a processor, one or more weighted values to data in a database (Klinkenberg, section 5 – teaches performing the methods on computer-stored data), wherein the one or more weighted values are associated with an attribute of the data (Klinkenberg, section 4.2 – teaches weighting the training data wherein the weight values are determined based on the time step in which the data element falls);
generating, by the processor, a training dataset from the data, wherein an amount of data comprising the attribute of the data used to generate the training dataset is based on the one or more weighted values associated with the attribute of the data (Klinkenberg, section 4.2 – teaches weighting the training data based on time step using a formula that determines at what point in history the data is no longer included in the training dataset); and
training, by the processor, the machine learning model using the training dataset (Klinkenberg, section 4.2 – teaches using the data to training machine learning models; see also Klinkenberg, sections 4.3, 5).
Regarding claim 3 (Previously Presented), Klinkenberg teaches all of the limitations of the method of claim 1 as noted above. Klinkenberg further teaches wherein data associated with a first attribute is assigned a first weighted value and data associated with a second attribute is assigned a second weighted value, wherein the first weighted value is different than the second weighted value (Klinkenberg, section 4.2 – teaches weight values associated with each time step using a formula wherein each time step [attribute] is weighted differently).
Regarding claim 4 (Previously Presented), Klinkenberg teaches all of the limitations of the method of claim 3 as noted above. Klinkenberg further teaches wherein the one or more weighted values are changed by a set amount from the first weighted value to the second weighted value (Klinkenberg, section 4.2 – teaches weight values associated with each time step using a formula wherein each time step [attribute] is weighted differently and the formula changes by a set amount based on the time step).
Regarding claim 10 (Currently Amended), it is the data system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Klinkenberg further teaches a data system comprising:
a database (Klinkenberg, section 5 – teaches performing the methods on computer-stored data);
a processor coupled to the database (Klinkenberg, section 5 – teaches performing the methods on computer-stored data); and
a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to (Klinkenberg, section 5 – teaches performing the methods on computer-stored data) …
Regarding claim 12 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg for the reasons set forth in the rejection of claim 3.
Regarding claim 13 (Previously Presented), the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg for the reasons set forth in the rejection of claim 4.
Regarding claim 21 (Currently Amended), Klinkenberg teaches all of the limitations of the method of claim 4 as noted above. Klinkenberg further teaches wherein the one or more weighted values decrease by a set amount from the first weighted value to the second weighted value (Klinkenberg, section 4.2 – teaches weight values associated with each time step using a formula wherein each time step [attribute] is weighted differently and the formula changes by a set amount based on the time step which decreases as the time steps progress backwards in history).
Regarding claim 22 (Currently Amended), the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg for the reasons set forth in the rejection of claim 21.
Claim(s) 2, 5-7, 9, 11, 14-16, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klinkenberg in view of Cheng et al. (US 2020/0050182 A1 – Automated Anomaly Precursor Detection, hereinafter referred to as “Cheng”).
Regarding claim 2 (Previously Presented), Klinkenberg teaches all of the limitations of the method of claim 1 as noted above. However, Klinkenberg does not explicitly teach wherein the machine learning model comprises a storage prediction model.
Cheng teaches wherein the machine learning model comprises a storage prediction model (Cheng, [0003] - teaches monitoring and predicting anomalies in datacenters; Cheng, [0023] - teaches anomalies as non-responsiveness of computer components; Cheng, [0028] - teaches that computer components such as memory can be monitored).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Klinkenberg with the teachings of Cheng in order to timely detect anomalies in time series data of a monitored system in the field of machine learning training dataset generation (Cheng, [0003] – “Large, complex systems, such as chemical production systems, powerplants, datacenters, etc., may need constant monitoring to ensure that system uptime remains at acceptable levels and avoid system failures. Currently, such systems are provided with various sensors that provide operational information to a technician, operator, or information technology officer, who is tasked with monitoring and initiating any corrective action to maintain operation of the system within preset parameters. Monitoring behaviors of these large-scale systems generates massive time series data, such as the readings of sensors distributed in a power plant, and the flow intensities of system logs from the cloud computing facilities. The unprecedented growth of monitoring data increases the demand for automatic and timely detection of incipient anomalies as well as precise discovery of precursor symptoms.”).
Regarding claim 5 (Previously Presented), Klinkenberg teaches all of the limitations of the method of claim 1 as noted above. However, Klinkenberg does not explicitly teach identifying, by the processor, a subset of data having a characteristic; and adding, by the processor, the subset of data to the training dataset.
Cheng teaches
identifying, by the processor, a subset of data having a characteristic (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data); and
adding, by the processor, the subset of data to the training dataset (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Klinkenberg with the teachings of Cheng in order to timely detect anomalies in time series data of a monitored system in the field of machine learning training dataset generation (Cheng, [0003] – “Large, complex systems, such as chemical production systems, powerplants, datacenters, etc., may need constant monitoring to ensure that system uptime remains at acceptable levels and avoid system failures. Currently, such systems are provided with various sensors that provide operational information to a technician, operator, or information technology officer, who is tasked with monitoring and initiating any corrective action to maintain operation of the system within preset parameters. Monitoring behaviors of these large-scale systems generates massive time series data, such as the readings of sensors distributed in a power plant, and the flow intensities of system logs from the cloud computing facilities. The unprecedented growth of monitoring data increases the demand for automatic and timely detection of incipient anomalies as well as precise discovery of precursor symptoms.”).
Regarding claim 6 (Previously Presented), Klinkenberg in view of Cheng teaches all of the limitations of the method of claim 5 as noted above. Cheng further teaches wherein the subset of data comprises storage status data (Cheng, [0003] - teaches monitoring and predicting anomalies in datacenters; Cheng, [0023] - teaches anomalies as non-responsiveness of computer components; Cheng, [0028] - teaches that computer components such as memory can be monitored).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Klinkenberg and Cheng in order to identify data to timely detect anomalies in time series data of a monitored system (Cheng, [0003]).
Regarding claim 7 (Previously Presented), Klinkenberg in view of Cheng teaches all of the limitations of the method of claim 5 as noted above. Cheng further teaches wherein the subset of data is identified using a rule based method or by using a cluster based method (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data [iterative rule based] to identify a precursor event and using the subset of data as training data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Klinkenberg and Cheng in order to identify data to timely detect anomalies in time series data of a monitored system (Cheng, [0003]).
Regarding claim 9 (Previously Presented), Klinkenberg teaches all of the limitations of the method of claim 1 as noted above. However, Klinkenberg does not explicitly teach identifying, by the processor, a subset of data having a characteristic; generating, by the processor, additional data having the characteristic; and adding, by the processor, the generated additional data to the training dataset.
Cheng teaches
identifying, by the processor, a subset of data having a characteristic (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data);
generating, by the processor, additional data having the characteristic (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data); and
adding, by the processor, the generated additional data to the training dataset (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Klinkenberg with the teachings of Cheng in order to timely detect anomalies in time series data of a monitored system in the field of machine learning training dataset generation (Cheng, [0003] – “Large, complex systems, such as chemical production systems, powerplants, datacenters, etc., may need constant monitoring to ensure that system uptime remains at acceptable levels and avoid system failures. Currently, such systems are provided with various sensors that provide operational information to a technician, operator, or information technology officer, who is tasked with monitoring and initiating any corrective action to maintain operation of the system within preset parameters. Monitoring behaviors of these large-scale systems generates massive time series data, such as the readings of sensors distributed in a power plant, and the flow intensities of system logs from the cloud computing facilities. The unprecedented growth of monitoring data increases the demand for automatic and timely detection of incipient anomalies as well as precise discovery of precursor symptoms.”).
Regarding claim 11 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg in view of Cheng for the reasons set forth in the rejection of claim 2.
Regarding claim 14 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg in view of Cheng for the reasons set forth in the rejection of claim 5.
Regarding claim 15 (Previously Presented), the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg in view of Cheng for the reasons set forth in the rejection of claim 6.
Regarding claim 16 (Previously Presented), the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg in view of Cheng for the reasons set forth in the rejection of claim 7.
Regarding claim 18 (Previously Presented), the rejection of claim 10 is incorporated herein. Further, the limitations in this claim are taught by Klinkenberg in view of Cheng for the reasons set forth in the rejection of claim 9.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (US 2020/0050182 A1 – Automated Anomaly Precursor Detection, hereinafter referred to as “Cheng”).
Regarding claim 19 (Currently Amended), Cheng teaches a method for training a machine learning model (Cheng, [0054] – teaches training the model using the data containing the precursor events), the method comprising:
generating, by a processor, training dataset from data in a database (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data);
identifying, by the processor, a subset of data having a characteristic (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data);
generating, by the processor, additional data having the characteristic, the additional data being in addition to the data in the database (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data [In accordance of paragraph 0066 of the specification, interpreting the original bag as the database, expanding the bag to include more data generates additional data not in the original bag database]);
adding, by the processor, the generated additional data to the training dataset (Cheng, [0048]-[0051] - teaches identifying an anomaly in the data and identifying a subset of data of potentially containing precursor events preceding the anomaly, wherein, if no precursor events exist in the subset, expanding the subset to include more historical data to identify a precursor event and using the subset of data as training data); and
training, by the processor, the machine learning model using the training dataset (Cheng, [0054] – teaches training the model using the data containing the precursor events).
Regarding claim 20 (Previously Presented), Cheng teaches all of the limitations of the method of claim 19 as noted above. Cheng further teaches wherein the machine learning model comprises a storage prediction model (Cheng, [0003] - teaches monitoring and predicting anomalies in datacenters; Cheng, [0023] - teaches anomalies as non-responsiveness of computer components; Cheng, [0028] - teaches that computer components such as memory can be monitored).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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