DETAILED ACTION
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C 102 and 103 (or as subject to pre-AIA 35 U.S.C 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-20 are subject to review.
Information Disclosure Statement
Applicant's Information Disclosure Statements, filed on 9/30/2024, 5/20/2025, 9/30/2025, 7/15/2026 have been received, and entered into the record. However, it is impractical for the examiner to review the references thoroughly with the number of references cited in this case. By initializing each of the cited references on the accompanying 1449 forms, the examiner is merely acknowledging the submission of the cited references and merely indicating that only a cursory review has been made of the cited references. 4. MPEP § 2004.13 states: It is desirable to avoid the submission of long lists of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant's attention and/or are known to be of most significance. See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aft 'd, 479 F.2d 1338, 178 USPQ 577 (Sth Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995). 5. Further, it should be noted that an applicant's duty of disclosure of material and information is not satisfied by presenting a patent examiner with "a mountain of largely irrelevant material from which he is presumed to have been able, with his experience and with adequate time, to have found the critical [material]. It ignores the real world conditions under which examiners work." Rohm & Haas Co. v. Crystal Chemical co., 722 F.2d 1556, 1573 [220 USPQ 289] (Fed. Cir. 1983), cert. Denied, 469 U.S. 851 (1984). Patent applicant has a duty not just to disclose pertinent prior art references but to make a disclosure in such a way as not to "bury" it within other disclosures of less relevant prior art; see Golden Valley Microwave Foods Inc. v. Weaver Popcorn Co. Inc., 24 USPQ2d 180i (N~D. Ind. 1992); Molins PLC v. Textron Inc., 26 USPQ2d 1889, at 1899 (D.Del 1992); Penn Yan Boats, Inc. v. Sea Lark Boats, Inc. et al., 175 USPQ 260, at 272 (S.D. FI. 1972).
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claims 3, 4, 16, and 17 disclose a “seasonal classifier”. However, this term is not defined or otherwise described within the specification. Clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, 8, 17, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 9-13 are rejected as being dependent claims. Appropriate correction is required.
Claims 4 and 17 recite the following limitation that render the claims indefinite:
“wherein the second set of data points are outlier values from a time series dataset and the second seasonal classifier is an encoding associated with a long-term season that has a variable seasonal period.”
This limitation is unclear and indefinite. Specifically, it is unclear how exactly the classifier, which is interpreted to include a model or model component, is an encoding, which is interpreted to include a tag or label associated with data. It is unclear how the classifier itself is an encoding, when the encoding is used in a contextual manner to associate a long-term season that has a variable seasonal period. Clarification is required.
Claims 6 and 19 recite the limitation "the first value". There is insufficient antecedent basis for this limitation in the claim. Clarification is required.
Claim 8 recites the following limitations that render the claim indefinite:
“training at least one machine learning model to predict contributions of at least a first seasonal pattern and a second seasonal pattern to values of a time-series signal;
wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;
wherein the at least one machine learning model includes at least one of a short-term seasonal model or model component that is trained using the observed data in the first training dataset;
wherein the at least one machine learning model includes at least one of a long-term seasonal model or model component that is trained using the outlier data in the second training dataset”.
First, the limitation “wherein the at least one machine learning model includes at least one of a short-term seasonal model or model component” is unclear whether the one machine learning model includes a short-term seasonal model, or a model component (which it’s unclear what it is meant by this ‘model component’); or if the one machine learning model includes a short-term seasonal model or a ‘short-term seasonal model component’. The same issue is found with the last limitation “wherein the at least one machine learning model includes at least one of a long-term seasonal model or model component”, as it is unclear whether the one machine learning model includes a long-term seasonal model, or a model component (which it’s unclear what it is meant by this ‘model component’); or if the one machine learning model includes a long-term seasonal model or a ‘long-term seasonal model component’. Furthermore, it is unclear whether these “short-term seasonal model” and “long-term seasonal model” corresponds to the first seasonal pattern and second seasonal pattern, respectively. The metes and bounds of these limitations are unclear, as there are no such criteria to conclude if there are two different models, being a first model for taking care of the short-term seasonal data and another second model for taking care of the long-term seasonal data; or if there is only a single model that takes care of both seasonal data as being a single model including sub-components within the single model that each component executes the corresponding short-term data and long-term data. For purposes of examination, Examiner will interpret the at least one model as a singular model with components to handle both short- and long-term data. Clarification is required.
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 an abstract idea without reciting significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-7 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 8-13 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 14-20 are directed to a “non-transitory computer-readable media” which describes one of the four statutory categories of patentable subject matter, i.e., a manufacture.
Regarding Claim 1
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
“generating, by at least one machine learning model, a forecast predicting a behavior of at least one computing resource;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a forecast, which is a value, and is considered to be a mathematical calculation.
“wherein generating, by the at least one machine learning model, the forecast includes: estimating, for a timestamp in the forecast, a first contribution of a first seasonal pattern having a first seasonal period;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a contribution, which is a value, and is considered to be a mathematical calculation.
“estimating, for the timestamp in the forecast, a second contribution of a second seasonal pattern having a second seasonal period that is longer than the first seasonal period; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a contribution, which is a value, and is considered to be a mathematical calculation.
“estimating, using the first contribution and the second contribution, a value for the forecast at a point in time corresponding to the timestamp; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a value, and is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.” – is merely indicating a field of use or technological environment directed towards the technology of model compression/distillation (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.” – is merely indicating a field of use or technological environment and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 2 recites an abstract idea, substantially as follows:
“The method of Claim 1, wherein the first contribution is estimated using a first machine learning model and the second contribution is estimated using a second machine learning model; wherein the first machine learning model is trained using non-outlier values from a time-series dataset and the second machine learning model is trained using outlier values from the time-series dataset.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing estimating a contribution, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 2 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 3
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 3 recites an abstract idea, substantially as follows:
“The method of Claim 1, wherein estimating the first contribution includes clustering a first set of data points assigned to a first seasonal classifier; and determining a first seasonal trend based on the first set of data points; wherein estimating the second contribution includes clustering a second set of data points assigned to a second seasonal classifier; and determining a second seasonal trend based on the second set of data points.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing estimating contributions and determining trends, which are considered to be mathematical calculations.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 4
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 4 recites an abstract idea, substantially as follows:
“wherein the second set of data points are outlier values from a time series dataset and the second seasonal classifier is an encoding associated with a long-term season that has a variable seasonal period.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing encodings which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 5
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 5 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the value represents a demand predicted for at least one computing resource, wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the value represents a demand predicted for at least one computing resource, wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 6
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 6 recites an abstract idea, substantially as follows:
“determining an uncertainty associated with the first value based on the first contribution and the second contribution.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing determining an uncertainty, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 7
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 7 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein performing the at least one operation includes configuring the at least one computing resource based on the behavior of the at least one computing resource predicted by the forecast.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein performing the at least one operation includes configuring the at least one computing resource based on the behavior of the at least one computing resource predicted by the forecast.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 8
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“training at least one machine learning model to predict contributions of at least a first seasonal pattern and a second seasonal pattern to values of a time-series signal;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing predicting contributions, which is considered to be a mathematical calculation.
“wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;”– is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing lengths between patterns which is considered to be a mathematical relationship
“wherein the at least one machine learning model includes at least one of a short-term seasonal model or model component that is trained using the observed data in the first training dataset;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing a mathematical calculation.
“wherein the at least one machine learning model includes at least one of a long-term seasonal model or model component that is trained using the outlier data in the second training dataset.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“generating a plurality of training datasets including a first training dataset comprised of observed data from the time-series signal and a second training dataset comprised of outlier data from the time-series signal; and” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“generating a plurality of training datasets including a first training dataset comprised of observed data from the time-series signal and a second training dataset comprised of outlier data from the time-series signal; and” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 9
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 9 recites an abstract idea, substantially as follows:
“wherein training the long-term seasonal model or model component includes mapping outlier values from the second training dataset to one or more encodings and selecting a subset of the outlier values to train the long-term seasonal model or model component based on the mapping.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing mapping values to encodings, which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 10
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 10 recites an abstract idea, substantially as follows:
“wherein the subset of the outlier values is selected based on how frequently timestamps for outlier values have been mapped to different encodings for different long-term season types.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing values mapped to encodings, which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 11
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“wherein training the long-term seasonal model or model component includes clustering the outlier values based on the mapping;” - is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing clustering values according to encodings, which is considered to be a mathematical relationship.
“wherein a first cluster includes a subset of outlier values assigned a first encoding and a second cluster assigned a second encoding;” - is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing values mapped to encodings, which is considered to be a mathematical relationship.
“and merging two or more clusters based on a similarity between different encodings.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing clustering values according to encodings, which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 12
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 12 recites an abstract idea, substantially as follows:
“wherein training the short-term seasonal model or model component includes determining, based on the observed data, a first seasonal factor and training the long-term seasonal model or model component includes determining, based on the outlier data, a second seasonal factor that is different than the first seasonal factor.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing determining factors, which are values, and is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 13
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 13 recites an abstract idea, substantially as follows:
“generating a forecast predicting a behavior of at least one computing resource using the at least one machine learning model.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a forecast, which is a value, and is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 14
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 14 recites an abstract idea, substantially as follows:
“generating, by at least one machine learning model, a forecast predicting a behavior of at least one computing resource;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a forecast, which is a value, and is considered to be a mathematical calculation.
“wherein generating, by the at least one machine learning model, the forecast includes: estimating, for a timestamp in the forecast, a first contribution of a first seasonal pattern having a first seasonal period;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a contribution, which is a value, and is considered to be a mathematical calculation.
“estimating, for the timestamp in the forecast, a second contribution of a second seasonal pattern having a second seasonal period that is longer than the first seasonal period; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing generating a contribution, which is a value, and is considered to be a mathematical calculation.
“estimating, using the first contribution and the second contribution, a value for the forecast at a point in time corresponding to the timestamp; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a value, and is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.” – is merely indicating a field of use or technological environment directed towards the technology of model compression/distillation (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.” – is merely indicating a field of use or technological environment and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 15
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 15 recites an abstract idea, substantially as follows:
“wherein the first contribution is estimated using a first machine learning model and the second contribution is estimated using a second machine learning model; wherein the first machine learning model is trained using non-outlier values from a time-series dataset and the second machine learning model is trained using outlier values from the time-series dataset.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing estimating a contribution, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 16
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 16 recites an abstract idea, substantially as follows:
“wherein estimating the first contribution includes clustering a first set of data points assigned to a first seasonal classifier; and determining a first seasonal trend based on the first set of data points; wherein estimating the second contribution includes clustering a second set of data points assigned to a second seasonal classifier; and determining a second seasonal trend based on the second set of data points.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing estimating contributions and determining trends, which are considered to be mathematical calculations.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 17
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 17 recites an abstract idea, substantially as follows:
“wherein the second set of data points are outlier values from a time series dataset and the second seasonal classifier is an encoding associated with a long-term season that has a variable seasonal period.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing encodings which is considered to be a mathematical relationship.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 18
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 18 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the value represents a demand predicted for at least one computing resource, wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the value represents a demand predicted for at least one computing resource, wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 19
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 19 recites an abstract idea, substantially as follows:
“determining an uncertainty associated with the first value based on the first contribution and the second contribution.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing determining an uncertainty, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 20
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 20 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein performing the at least one operation includes configuring the at least one computing resource based on the behavior of the at least one computing resource predicted by the forecast.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein performing the at least one operation includes configuring the at least one computing resource based on the behavior of the at least one computing resource predicted by the forecast.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims(s) 1, 3, 4, 6, 7, 14, 16, 17, 19, and 20 are rejected under 35. U.S.C. 103 as being unpatentable over Garvey et al. (US 20170249648 A1), hereinafter referred to as Garvey, in view of Anderson et al. (US 20150317589 A1), hereinafter referred to as Anderson, and further in view of Aykin, Turgut (US 7987106 B1) hereinafter referred to as Aykin.
Regarding Claim 1
Garvey discloses:
“generating, by […], a forecast predicting a behavior of at least one computing resource;” (Garvey at [0033] Forecasting logic 136 may use the output of seasonal analytic logic 134 to generate a forecast of future values for the input time series, as described in further detail below; Garvey at [0122] For instance, the forecast may indicate whether there is a high risk of exhaustion for the resource, a high risk for exhaustion for batch (sparse) jobs but low risk for typical (dense) workloads, a low risk for exhaustion but increasing trend, a consistent, predictable scheduled load, a decreasing trend in load, etc.)
“wherein generating, by […], the forecast includes: estimating, for a timestamp in the forecast, a first contribution of a first seasonal pattern having a first seasonal period;” (Garvey at [0038] The process may further determine the seasonal factors [Examiner Note: mapped to contributions] and shape for the classified seasonal patterns at this block as well; Garvey at [0020] Seasonal patterns may be detected by analyzing data points collected across different seasonal periods (also referred to herein as “samples”) within the time series.)
“estimating, for the timestamp in the forecast, a second contribution of a second seasonal pattern having a second seasonal period […]; and” (Garvey at [0038] The process may further determine the seasonal factors [Examiner Note: mapped to contributions] and shape for the classified seasonal patterns at this block as well; Garvey at [0020] Seasonal patterns may be detected by analyzing data points collected across different seasonal periods (also referred to herein as “samples”) within the time series)
“estimating, using the first contribution and the second contribution, a value for the forecast at a point in time corresponding to the timestamp; and” (Garvey at [0039] The seasonal forecast may then be generated by adding or multiplying seasonal factors [Examiner Note: contributions] to the trend line.)
“performing at least one operation based on the forecast predicting the behavior of the at least one computing resource.” (Garvey at [0120] In an embodiment, the projected values [Examiner note: mapped to forecast] may be used to plan, recommend, and/or order resources to handle increased resource usage.)
However, Garvey does not disclose:
“at least one machine learning model”
“a second seasonal period that is longer than the first seasonal period”
On the other hand, Anderson discloses:
“at least one machine learning model” (Anderson at [0133] In an example embodiment, the MLFS 213 can then predict the forecast of package volumes for the next day, week, month, and season with one or more machine learning models.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey with the above teachings of Anderson by using a method of estimating contributions of seasonal pattens to generate a forecast, as taught by Garvey, and using one more machine learning models to generate a forecast, as taught by Anderson. The modification would have been obvious because one of ordinary skill in the art would be motivated to mitigate concept drift and improve forecasting as suggested by Anderson at [0145] “FIG. 15 is a flow graph that illustrates an exemplary method to improve forecasting using, for example, ensemble methods and unsupervised learning. In the exemplary embodiment, to deal with concept drift, different models can be used for different points in the data, for example, for different seasons, to improve the forecasting system. It can be understood that this exemplary method can be used to improve forecasting of the MLFS or any other system.”
However, the combination of Garvey and Anderson does not disclose:
“a second seasonal period that is longer than the first seasonal period”
On the other hand, Aykin discloses:
“a second seasonal period that is longer than the first seasonal period” (Aykin at Col 7, Lines 59-65: FIG. 3A and FIG. 3B. shows a flowchart of the preferred embodiment for the seasonal sub-series method of the invention. In step 20, the number of seasonal patterns and the number of time periods in each cycle of a seasonal pattern are inputted. In the illustrative case considered, the cycle length [Examiner note: mapped to seasonal period] of Seasonal Pattern 1 is 7 days, and Seasonal Pattern 2 is 52 weeks.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey and Anderson with the above teachings of Aykin by using one or more machine learning models to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey and Anderson, and using a forecast method where one seasonal pattern is longer than another seasonal pattern, as taught by Aykin. The modification would have been obvious because one of ordinary skill in the art would be motivated to use a forecasting method that can be used with multiple seasonal patterns as suggested by Aykin at Col 2, Lines 39-46: “Given the limited availability of the forecasting methods that can be used for time series with multiple seasonal patterns, an objective of the present invention is to provide a new method for this type of time series using the additive version of the exponential smoothing method. Another objective of the present invention is to provide two general methods for time series and forecast processing that can be used with any forecasting method to capture multiple seasonal patterns.”
As per Claim 14, this is a computer readable media claim corresponding to method Claim 1, and is rejected for similar reasons.
Regarding Claim 3
The combination of Garvey, Anderson, and Aykin further discloses “The method of Claim 1” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“wherein estimating the first contribution includes clustering a first set of data points assigned to a first seasonal classifier;” (Anderson [0146] To prepare the data for identifying trends in the data, the data can be segregated [Examiner Note: mapped to data points assigned] into one or more clustering models [Examiner Note: mapped to seasonal classifiers] (1503). Some examples of the clustering models include, but are not limited to, segregating the data using an hour-by-hour model, a Week clustering model, or the like) [Examiner Note, the data is segregated into one or more models, which is equivalent to different sets of data points assigned to different models]
“and determining a first seasonal trend based on the first set of data points;” (Anderson [0146] To prepare the data for identifying trends in the data, the data can be segregated into one or more clustering models (1503). Some examples of the clustering models include, but are not limited to, segregating the data using an hour-by-hour model, a Week clustering model, or the like. Anderson at [0147] In an exemplary embodiment, the data is then analyzed to determine trends in the data (1505). In one example, after the data is segregated, different ML models can be used to identify trends in the data)
“wherein estimating the second contribution includes clustering a second set of data points assigned to a second seasonal classifier;” (Anderson at [0146] To prepare the data for identifying trends in the data, the data can be segregated [Examiner Note: mapped to data points assigned] into one or more clustering models [Examiner Note: mapped to seasonal classifiers] (1503). Some examples of the clustering models include, but are not limited to, segregating the data using an hour-by-hour model, a Week clustering model, or the like) [Examiner Note, the data is segregated into one or more models, which is equivalent to different sets of data points assigned to different models]
“and determining a second seasonal trend based on the second set of data points.” (Anderson at [0146] To prepare the data for identifying trends in the data, the data can be segregated into one or more clustering models (1503). Some examples of the clustering models include, but are not limited to, segregating the data using an hour-by-hour model, a Week clustering model, or the like. Anderson at [0147] In an exemplary embodiment, the data is then analyzed to determine trends in the data (1505). In one example, after the data is segregated, different ML models can be used to identify trends in the data)
The same motivation utilized for combining Garvey with Anderson, as set forth in Claim 1, is equally applicable to Claim 3.
As per Claim 16, this is a computer readable media claim corresponding to method Claim 3, and is rejected for similar reasons.
Regarding Claim 4
The combination of Garvey, Anderson, and Aykin discloses “The method of Claim 3” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“wherein the second set of data points are outlier values from a time series dataset and” (Garvey at [0055] In some embodiments, a time series is decomposed into a noise signal [mapped to outlier values] and a dense signal where the noise signal, also referred to herein as a sparse signal or sparse component, captures the sparse distribution of data in a time series that otherwise has a dense distribution)
“the second seasonal classifier is an encoding associated with a long-term season that has a variable seasonal period.” (Anderson [0146] To prepare the data for identifying trends in the data, the data can be segregated into one or more clustering models [Examiner Note: mapped to seasonal classifier] (1503). Some examples of the clustering models include, but are not limited to, segregating the data using an hour-by-hour model, a Week clustering model, [Examiner Note: long term season that has a variable seasonal period] or the like.)
The same motivation utilized for combining Garvey with Anderson, as set forth in Claim 1, is equally applicable to Claim 4.
As per Claim 17, this is a computer readable media claim corresponding to method Claim 4, and is rejected for similar reasons.
Regarding Claim 6
The combination of Garvey, Anderson, and Aykin discloses “The method of Claim 1” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“further comprising: determining an uncertainty associated with the first value based on the first contribution and the second contribution.” (Garvey at [0094] Uncertainty analytic logic 138 determines a set of one or more uncertainty values for a given forecast.) [Examiner Note: the value is associated with the forecast, which was generated using seasonal factors (contributions), and is used to determine an uncertainty for that forecast]
As per Claim 19, this is a computer readable media claim corresponding to method Claim 6, and is rejected for similar reasons.
Regarding Claim 7
The combination of Garvey, Anderson, and Aykin discloses “The method of Claim 1” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“wherein performing the at least one operation includes configuring the at least one computing resource based on the behavior of the at least one computing resource predicted by the forecast.” (Garvey [0120] In an embodiment, the projected values [Examiner Note: mapped to forecast] may be used to plan, recommend, and/or order resources to handle increased resource usage. Garvey at [0121] In another embodiment, the projected values may be used to consolidate resources.)
As per Claim 20, this is a computer readable media claim corresponding to method Claim 7, and is rejected for similar reasons.
Claim(s) 2 and 15 are rejected under 35. U.S.C. 103 as being unpatentable over Garvey in view of Anderson in view of Aykin and further in view of Cretu et al. (US 20080262985 A1) hereinafter referred to as Cretu.
Regarding Claim 2
The combination of Garvey, Anderson, and Aykin further discloses “The method of Claim 1” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“wherein the first contribution is estimated using a first machine learning model and the second contribution is estimated using a second machine learning model; (Garvey at [0077] As previously indicated, seasonal analytic logic 134 may output seasonal factors for different instances [Examiner Note: mapped to a first and second contribution] of a season; Anderson at [0133] In an example embodiment, the MLFS 213 can then predict the forecast of package volumes for the next day, week, month, and season with one or more machine learning models.)
“a time series dataset”(Garvey at [0033] Forecasting logic 136 may use the output of seasonal analytic logic 134 to generate a forecast of future values for the input time series)
However, the combination of Garvey, Anderson, and Aykin does not disclose:
“wherein the first machine learning model is trained using non-outlier values from a time-series dataset and the second machine learning model is trained using outlier values from the time-series dataset.”
On the other hand, Cretu discloses:
“wherein the first machine learning model is trained using non-outlier values from a […] dataset and the second machine learning model is trained using outlier values from the […] dataset.” (Cretu at [0026] If a remote model of the models 320 indicates a hit on a data item (in this case, if a model 320 indicates a data item is anomalous), it can be added to anomalous data set 340. If a remote model of the models 320 does not indicate a hit on a data item (in this case, if a model 320 indicates a data item is normal), it can be added to normal data set 350. Models 360 and 370 can be generated, at 355 and 345, based on their corresponding dataset of datasets 350 or 340.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey, Anderson, and Aykin with the above teachings of Cretu by using one or more machine learning models to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey, Anderson, and Aykin, and using a method where one model is training on an anomalous dataset and the other model is trained on a non-anomalous dataset, as taught by Cretu. The modification would have been obvious because one of ordinary skill in the art would be motivated to use such a method for the purposes of data sanitization, as suggested by Cretu at [0026]: “For example, FIG. 3 illustrates a method for sanitizing a local normal training data set 300 based on remote abnormal detection models 320 to generate a local normal sanitized anomaly detection model 360 and a local abnormal anomaly detection model 370”
As per Claim 15, this is a computer readable media claim corresponding to method Claim 2, and is rejected for similar reasons.
Claim(s) 5 and 18 are rejected under 35. U.S.C. 103 as being unpatentable over Garvey in view of Anderson in view of Aykin and further in view of Cao et al. (US 20180204171 A1) hereinafter referred to as Cao.
Regarding Claim 5
The combination of Garvey, Anderson, and Aykin discloses “The method of Claim 1” and the limitations are shown in the rejection above.
The combination of Garvey, Anderson, and Aykin further discloses:
“wherein the value represents a demand predicted for at least one computing resource,” (Garvey at [0122] For instance, the forecast [Examiner Note: mapped to value] may indicate whether there is a high risk of exhaustion for the resource, a high risk for exhaustion for batch (sparse) jobs but low risk for typical (dense) workloads, a low risk for exhaustion but increasing trend, a consistent, predictable scheduled load, a decreasing trend in load, etc.)
“seasonal pattern” (Garvey at [0040] Seasonal analytic logic 134 may analyze seasons of a single duration or of varying duration to detect seasonal patterns. As an example, the time series data may be analyzed for daily patterns, weekly patterns, monthly patterns, quarterly patterns, yearly patterns, etc.)
However, the combination of Garvey, Anderson, and Aykin does not disclose:
“wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.”
On the other hand, Cao discloses:
“wherein the first seasonal pattern represents a short-term seasonal pattern and the second seasonal pattern represents a long-term seasonal pattern in the demand.” (Cao at [0036] Capacity engine 102 in accordance with a further embodiment can predict the future processing capacity value for a processing node device in one or more future time periods by considering long-term/short-term patterns that can have been determined from the historical data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey, Anderson, and Aykin with the above teachings of Cao by using one or more machine learning models to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey, Anderson, and Aykin, and using a method where seasonal patterns represent short/long patterns in a demand of a computing resource, as taught by Cao. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve the accuracy of forecasting of capacity values for computing resources, as suggested by Cao at Cao at [0032] “Further, in an additional embodiment, utilization of capacity engine 102 can improve the accuracy of the determinations of processing capacity values for each node device (or, in some embodiments, one or more node devices) comprising the network of processing node devices, and therefore can additionally make backlog determination more accurate.”
As per Claim 18, this is a computer readable media claim corresponding to method Claim 5, and is rejected for similar reasons.
Claim(s) 8, 12, and 13 are rejected under 35. U.S.C. 103 as being unpatentable over Garvey in view of Cretu and further in view of Aykin.
Regarding Claim 8
Garvey discloses:
“generating a plurality of […] datasets including a first […] dataset comprised of observed data from the time-series signal and a second […] dataset comprised of outlier data from the time-series signal; and” (Garvey at [0021] In some embodiments, forecasts are generated to account for seasonal pattern classifications. When generating a forecast, the time series is decomposed into a set of signals by seasonal class [Examiner Note: mapped to generating a plurality of datasets]; Garvey at [0052] In some embodiments, a dense signal [Examiner Note: mapped to observed data from the time-series signal] represents a plurality of instances of time-series data that (1) significantly represents an entire period or sub-period of data and (2) exclude a relatively small portion (e.g., 1%, 5%, or some other threshold) of the data as outliers that are not the subject of the fitted signal. A sparse signal [Examiner Note: mapped to outlier data from the time-series signal] may represent data points that are excluded from the dense class of data points as outliers.)
“predict contributions of at least a first seasonal pattern and a second seasonal pattern to values of a time-series signal;” (Garvey at 0038] If a seasonal pattern has been detected, then at block 208, the process classifies the seasonal patterns within the time series data. Example classes/sub-classes may include, without limitation, sparse high, high, sparse low, and/or low. The process may further determine the seasonal factors and shape for the classified seasonal patterns at this block as well.)
However, Garvey does not disclose:
“training data”
“training at least one machine learning model[…]”
“wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;”
“wherein the at least one machine learning model includes at least one of a short-term seasonal model or model component that is trained using the observed data in the first training dataset;”
“wherein the at least one machine learning model includes at least one of a long-term seasonal model or model component that is trained using the outlier data in the second training dataset.”
On the other hand, Cretu discloses:
“training data” (Cretu at [0026] For example, FIG. 3 illustrates a method for sanitizing a local normal training data set 300 based on remote abnormal detection models 320 to generate a local normal sanitized anomaly detection model 360 and a local abnormal anomaly detection model 370)
“training at least one machine learning model” (Cretu at [0026] For example, FIG. 3 illustrates a method for sanitizing a local normal training data set 300 based on remote abnormal detection models 320 to generate a local normal sanitized anomaly detection model 360 and a local abnormal anomaly detection model 370)
“wherein the at least one machine learning model includes at least one of […] model or model component that is trained using the observed data in the first training dataset;” (Cretu at [0026] If a remote model of the models 320 indicates a hit on a data item (in this case, if a model 320 indicates a data item is anomalous), it can be added to anomalous data set 340. If a remote model of the models 320 does not indicate a hit on a data item (in this case, if a model 320 indicates a data item is normal), it can be added to normal data set 350. Models 360 and 370 can be generated, at 355 and 345, based on their corresponding dataset of datasets 350 or 340)
“wherein the at least one machine learning model includes at least one of […] model or model component that is trained using the outlier data in the second training dataset.” (Cretu at [0026] If a remote model of the models 320 indicates a hit on a data item (in this case, if a model 320 indicates a data item is anomalous), it can be added to anomalous data set 340. If a remote model of the models 320 does not indicate a hit on a data item (in this case, if a model 320 indicates a data item is normal), it can be added to normal data set 350. Models 360 and 370 can be generated, at 355 and 345, based on their corresponding dataset of datasets 350 or 340)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey with the above teachings of Cretu by using a method to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey and using a method where one model is training on an anomalous dataset and the other model is trained on a non-anomalous dataset, as taught by Cretu. The modification would have been obvious because one of ordinary skill in the art would be motivated to use such a method for the purposes of data sanitization, as suggested by Cretu at [0026]: “For example, FIG. 3 illustrates a method for sanitizing a local normal training data set 300 based on remote abnormal detection models 320 to generate a local normal sanitized anomaly detection model 360 and a local abnormal anomaly detection model 370”
However, the combination of Garvey and Cretu does not disclose:
“wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;”
“a short-term seasonal model or model component”
“a long-term seasonal model or model component”
On the other hand, Aykin discloses:
“wherein the second seasonal pattern has a longer seasonal period than the first seasonal pattern;” (Aykin at Col 7, Lines 59-65: FIG. 3A and FIG. 3B. shows a flowchart of the preferred embodiment for the seasonal sub-series method of the invention. In step 20, the number of seasonal patterns and the number of time periods in each cycle of a seasonal pattern are inputted. In the illustrative case considered, the cycle length [Examiner note: mapped to seasonal period] of Seasonal Pattern 1 is 7 days, and Seasonal Pattern 2 is 52 weeks.)
“a short-term seasonal model or model component” (Aykin at Col 7, Lines 59-65: FIG. 3A and FIG. 3B. shows a flowchart of the preferred embodiment for the seasonal sub-series method of the invention. In step 20, the number of seasonal patterns and the number of time periods in each cycle of a seasonal pattern are inputted. In the illustrative case considered, the cycle length of Seasonal Pattern 1 is 7 days, [Examiner Note: mapped to short-term] and Seasonal Pattern 2 is 52 weeks.)
“a long-term seasonal model or model component” (Aykin at Col 7, Lines 59-65: FIG. 3A and FIG. 3B. shows a flowchart of the preferred embodiment for the seasonal sub-series method of the invention. In step 20, the number of seasonal patterns and the number of time periods in each cycle of a seasonal pattern are inputted. In the illustrative case considered, the cycle length of Seasonal Pattern 1 is 7 days, and Seasonal Pattern 2 is 52 weeks [Examiner Note: mapped to long-term].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey and Cretu with the above teachings of Aykin by using one or more machine learning models to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey and Cretu, and using a forecast method where one seasonal pattern is longer than another seasonal pattern, as taught by Aykin. The modification would have been obvious because one of ordinary skill in the art would be motivated to use a forecasting method that can be used with multiple seasonal patterns as suggested by Aykin at Col 2, Lines 39-46: “Given the limited availability of the forecasting methods that can be used for time series with multiple seasonal patterns, an objective of the present invention is to provide a new method for this type of time series using the additive version of the exponential smoothing method. Another objective of the present invention is to provide two general methods for time series and forecast processing that can be used with any forecasting method to capture multiple seasonal patterns.”
Regarding Claim 12
The combination of Garvey, Cretu, and Aykin discloses “The method of Claim 8,” and the limitations are shown in the rejection above.
The combination of Garvey, Cretu, and Aykin further discloses:
“wherein training the short-term seasonal model or model component includes determining, based on the observed data, a first seasonal factor and training the long-term seasonal model or model component includes determining, based on the outlier data, a second seasonal factor that is different than the first seasonal factor” (Cretu at [0026] If a remote model of the models 320 indicates a hit on a data item (in this case, if a model 320 indicates a data item is anomalous), it can be added to anomalous data set 340. [Examiner Note: based on outlier data] If a remote model of the models 320 does not indicate a hit on a data item (in this case, if a model 320 indicates a data item is normal), it can be added to normal data set 350. Models 360 and 370 can be generated [Examiner Note: mapped to training the models], at 355 and 345, based on their corresponding dataset of datasets 350 or 340); Garvey at [0077] As previously indicated, seasonal analytic logic 134 may output seasonal factors for different instances of a season [Examiner Note: mapped to a second seasonal factor that is different than the first seasonal factor].)
The same motivation utilized for combining Garvey, Cretu, and Aykin, as set forth in Claim 8, is equally applicable to Claim 12.
Regarding Claim 13
The combination of Garvey, Cretu, and Aykin discloses “The method of Claim 8,” and the limitations are shown in the rejection above.
The combination of Garvey, Cretu, and Aykin further discloses:
“further comprising: generating a forecast predicting a behavior of at least one computing resource using the at least one machine learning model.” (Garvey at [0033] Forecasting logic 136 may use the output of seasonal analytic logic 134 to generate a forecast of future values for the input time series, as described in further detail below; Garvey at [0122] For instance, the forecast may indicate whether there is a high risk of exhaustion for the resource, a high risk for exhaustion for batch (sparse) jobs but low risk for typical (dense) workloads, a low risk for exhaustion but increasing trend, a consistent, predictable scheduled load, a decreasing trend in load, etc)
Claim 9 is rejected under 35. U.S.C. 103 as being unpatentable over Garvey in view of Cretu in view of Aykin and further in view of Horvitz, Eric J. (US 7519564 B2) hereinafter referred to as Horvitz.
Regarding Claim 9
The combination of Garvey, Cretu, and Aykin discloses “The method of Claim 8,” and the limitations can be found in the rejection above.
However, the combination of Garvey, Cretu, and Aykin does not disclose:
“wherein training the long-term seasonal model or model component includes mapping outlier values from the second training dataset to one or more encodings and selecting a subset of the outlier values to train the […] model or model component based on the mapping.” (Horvitz at Col 10, Lines 46-56: For example, for the observer's model, the means and variances associated with a few basic contextual pieces of evidence can be considered, such as time of day and day of week, [Examiner Note: mapped to encodings] and the consideration of subtle sets of findings that may interact to produce atypical flows can be removed. To pick anomalous cases for training, cases associated with very high and very low flows at different points in the future can be selected, and all observational variables at a current time in a training set for machine learning of models that can predict atypical instances can be noted)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Garvey, Cretu, and Aykin with the above teachings of Horvitz by using one or more machine learning models to estimate contributions of seasonal pattens to generate a forecast, as taught by Garvey, Cretu, and Aykin, and using contextual data to select data for training purposes, as taught by Horvitz. The modification would have been obvious because one of ordinary skill in the art would be motivated to use contextual data to improve predictive models as suggested by Horvitz at Col 7, Lines 19-22: “Furthermore, the contextual data can be stored in the data store 110 upon receipt thereof to enable utilization of such data at a later time (and thus improve operability of the predictive model component 102”
Allowable Subject Matter
Claim 10 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The closest prior art of record, Horvitz discloses anomalous data that are associated with contextual data such as time and date. Additionally, Horvitz recites selecting certain anomalous data for training based on flows for points in time in the future, associated with the contextual data.
However, the examiner has found that the distinct feature of the applicant’s claimed invention over the prior art is the explicit claiming of the aforementioned limitations as specified in Claim 10. When viewed individually or in as a combination with other prior art of record, the limitations specified in Claim 10 are distinct.
Claim 11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The closest prior art of record, Horvitz discloses anomalous data that are associated with contextual data such as time and date. Additionally, Horvitz recites the data being associated with different flows at points in time.
However, the examiner has found that the distinct feature of the applicant’s claimed invention over the prior art is the explicit claiming of the aforementioned limitations as specified in Claim 11. When viewed individually or in as a combination with other prior art of record, the limitations specified in Claim 11 are distinct.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMIYAH KABIR whose telephone number is (571)270-0722. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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