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 .
Claims 1-18 are pending for examination. Claims 1, 13, and 18 are independent.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0189851, filed on 12/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) is submitted on 10/23/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because:
The view numbers must be larger than the numbers used for reference characters — see 37 C.F.R. 1.84(u)(2).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 18 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.
Claim 18 claims “A computer program combined with a computing device,” it is unclear what constitutes “combining” a computer program with a computing device. For the purpose of examination, Examiner interprets the claim as “A computer program stored on the computing device.”
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 1-11, 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 18 is additionally rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: The claim is a process, machine, manufacture, or composition of matter.
In the instant application, Claims 1-12 are directed to a process, Claims 13-17 are directed to a machine, and Claim 18 is directed to a computer-readable recording medium. Thus, Claims 1-17 are directed to one of the four statutory categories (i.e. process, machine, or composition of matter.)
With respect to Claim 18:
In the instant application, Claim 18 recites a “computer-readable recording medium”. However, the specification fails to provide clear support for the limitation. Without clear support for “computer-readable recording medium”, it is unclear if applicant intends to claim something broader than e.g., RAM, ROM, CD-ROM, disks, etc. and cover signals, carrier waves, and other forms of transmission media. Therefore, the limitation “computer-readable recording medium” is not limited to physical articles or objects which constitute a manufacture within the meaning of 35 U.S.C. 101 and enable any functionality of the instructions carried thereby to act as a computer component and realize their functionality. As such, the claim is not limited to statutory subject matter and is therefore non-statutory.
With respect to Claim 1:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
extracting a plurality of candidate training sets from the time series dataset; (This step of extracting a plurality of training sets from a dataset is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; (This step for evaluating suitability of training sets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
selecting a training set from the plurality of candidate training sets based on the evaluation result; (This step of selecting at least one training set from a plurality of training sets based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).) and
predicting future usage of the storage resource through the linear regression model trained with the training set. (This step of predicting an amount of resource usage is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2: The judicial exception is not integrated into a practical application.
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
The additional element as disclosed above alone or in combination does not integrate the judicial exception into practical application as it is an insignificant extra-solution activity used to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as a well-understood, routine, and conventional function, exemplary of receiving data — see MPEP 2106.05(d)(II)(i).)
The additional element as disclosed above alone or in combination does not recite significantly more than a judicial exception as it is a well-understood, routine, and conventional activity previously known to the industry used to perform the disclosed abstract idea above.
With respect to Claim 13:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
extracting a plurality of candidate training sets from the time series dataset; (This step of extracting a plurality of training sets from a dataset is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; (This step for evaluating suitability of training sets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
determining at least one training set from the plurality of candidate training sets based on the evaluation result; (This step of determining at least one training set from a plurality of training sets based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
predicting future usage of the storage resource through the linear regression model trained with the training set. (This step of predicting an amount of resource usage is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2: The judicial exception is not integrated into a practical application.
A system for predicting usage for a cloud storage service comprising: one or more processors; and a memory for storing instructions, wherein the one or more processors, by executing the stored instructions, perform operations comprising: (The one or more processors and memory are understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
The additional element as disclosed above alone or in combination does not integrate the judicial exception into practical application as it is an insignificant extra-solution activity in combination with generic implementation of a computer component as a tool used to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A system for predicting usage for a cloud storage service comprising: one or more processors; and a memory for storing instructions, wherein the one or more processors, by executing the stored instructions, perform operations comprising: (The one or more processors and memory are understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as a well-understood, routine, and conventional function, exemplary of receiving data — see MPEP 2106.05(d)(II)(i).)
The additional element as disclosed above alone or in combination does not recite significantly more than a judicial exception as it is a well-understood, routine, and conventional activity previously known to the industry in combination with generic implementation of a computer component as a tool used to perform the disclosed abstract idea above.
With respect to Claim 18:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
extracting a plurality of candidate training sets from the time series dataset; (This step of extracting a plurality of training sets from a dataset is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; (This step for evaluating suitability of training sets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
determining at least one training set from the plurality of candidate training sets based on the evaluation result; (This step of determining at least one training set from a plurality of training sets based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
predicting future usage of the storage resource through the linear regression model trained with the at least one training set. (This step of predicting an amount of resource usage is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2: The judicial exception is not integrated into a practical application.
A computer program combined with a computing device, wherein the computer program is stored on a computer-readable recording medium for executing steps comprising: (The computer program combined with a computing device is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
The additional element as disclosed above alone or in combination does not integrate the judicial exception into practical application as it is an insignificant extra-solution activity in combination with generic implementation of a computer component as a tool used to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
A computer program combined with a computing device, wherein the computer program is stored on a computer-readable recording medium for executing steps comprising: (The computer program combined with a computing device is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
obtaining a time series dataset through monitoring usage of storage resource; (Obtaining information is understood as a well-understood, routine, and conventional function, exemplary of receiving data — see MPEP 2106.05(d)(II)(i).)
The additional element as disclosed above alone or in combination does not recite significantly more than a judicial exception as it is a well-understood, routine, and conventional activity previously known to the industry in combination with generic implementation of a computer component as a tool used to perform the disclosed abstract idea above.
With respect to Claims 2 and 14:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the extracting the plurality of candidate training sets comprises:
dividing the time series dataset into a plurality of partial datasets; (This step of splitting a dataset into a plurality of partial datasets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
extracting the most recent partial dataset among the plurality of partial datasets as a first candidate training set; and (This step of extracting a partial dataset among a plurality of partial datasets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
extracting other partial dataset different from the most recent partial dataset as a second candidate training set. (This step of extracting a partial dataset among a plurality of partial datasets is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
With respect to Claim 3:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the other partial dataset is a neighboring dataset of the most recent partial dataset. (This specification of the other partial dataset is understood to be a field of use limitation. This limitation further specifies what the other partial dataset is limited to when extracting it from the plurality of partial datasets — see MPEP 2106.05(h).)
The additional element as disclosed above alone or in combination does not integrate the judicial exception into practical application nor does it recite significantly more than a judicial exception as it amounts to merely indicating a field of use in which to apply a judicial exception.
With respect to Claim 4:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the evaluating the suitability of the plurality of the candidate training sets comprises:
evaluating suitability of the first candidate training set using a linear regression model for evaluation trained with the first candidate training set; (This step for evaluating suitability of a training set is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
evaluating suitability of the second candidate training set using the additionally trained linear regression model. (This step for evaluating suitability of a training set is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2& 2B:
additionally training the linear regression model for evaluation with the other partial dataset; and (Training a model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
The additional element as disclosed above alone or in combination does not integrate the judicial exception into practical application nor does it recite significantly more than a judicial exception as it amounts to mere instructions to implement an abstract idea or other exception on a computer.
With respect to Claims 5 and 15:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein suitability of each of the plurality of candidate training sets is evaluated based on a determination coefficient of a linear regression model for evaluation trained with a candidate training set. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
With respect to Claims 6 and 16:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the evaluating the suitability of the plurality of candidate training sets comprises:
evaluating suitability of a specific candidate training set based on a residual of a linear regression model for evaluation for the specific candidate training set. (This step for evaluating suitability based on a residual (exemplified in the application specification [0092] as "the difference between the predicted value and the actual value") recites a mathematical concept (i.e. mathematical calculation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
With respect to Claim 7:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
calculating a residual of the linear regression model for evaluation using a second partial data set of the specific candidate training set different from the first partial data set. (This step for calculating a residual (exemplified in the application specification [0092] as "the difference between the predicted value and the actual value") recites a mathematical concept (i.e. mathematical calculation).)
2A Prong 2 & 2B:
wherein the evaluating the suitability of the specific candidate training set comprises:
training the linear regression model for evaluation using a first partial dataset of the specific candidate training set; and (Training a model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
With respect to Claim 8:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
selecting a second training set from a second time series dataset obtained through monitoring up to a second time point after the first time point, wherein the second time series dataset comprises additional dataset generated through monitoring after the first time point; (This step for selecting a second training set from a dataset through monitoring is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
wherein the training set is a first training set selected from a first time series dataset generated through monitoring up to a first time point, wherein the linear regression model is a first linear regression model for predicting future usage after the first time point (This step for selecting a first training set from a dataset through monitoring is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
predicting future usage after the second time point through a second linear regression model trained with the second training set. (This step of predicting an amount of resource usage is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
With respect to Claim 9:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
selecting the second training set by evaluating suitability of candidate training sets using the updated parameters; (This step of selecting a training set by evaluation of suitability is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).) and
2A Prong 2 The judicial exception is not integrated into a practical application.
wherein learned parameters of a linear regression model for evaluation obtained during a process of determining the first training set are stored in a storage, wherein the selecting the second training set comprises:
updating the learned parameters by learning the additional dataset; (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation).)
storing the updated parameters in the storage. (Storing data is understood as insignificant extra-solution activity) — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein learned parameters of a linear regression model for evaluation obtained during a process of determining the first training set are stored in a storage, wherein the selecting the second training set comprises:
updating the learned parameters by learning the additional dataset; (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation).)
storing the updated parameters in the storage. (This step for storing the updated parameters in the storage is understood as well-understood, routine, conventional activity, exemplary of storing and retrieving information — see MPEP 2106.05(d)(II)(iv).)
With respect to Claim 10:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the predicting the future usage comprises:
predicting usage of the storage resource at a future time point by inputting a value indicating the future time point in to the trained linear regression model. (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement).)
With respect to Claim 11:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the predicting the future usage comprises:
predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model. (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement).)
With respect to Claim 17:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the predicting the future usage comprises:
predicting usage of the storage resource at a future time point by inputting a value indicating the future time point in to the trained linear regression model. (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement).)
predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model. (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement).)
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 5-6, 10-11, 13, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al (US 8,244,651 B1), hereinafter "Lin", in view of Fu et al. ("An Intelligent Analysis and Prediction Model for On-demand Cloud Computing Systems"), hereinafter “Fu”.
With respect to Claim 1:
Lin teaches:
extracting a plurality of candidate training sets from the ([Col. 1 Lines 47-64] Lin discloses how the training examples are identified from the training data and may be ranked for usefulness. [Col. 5 Lines 59-60] discloses receiving a plurality of training examples that may have been extracted from the training data based on their usefulness ranking.
evaluating suitability of the plurality of candidate training sets to a linear regression([Col. 2 Lines 1-11] Lin discloses training predictive models with training examples and ranking the training examples based on a computed suggestion score. [Col. 3 Lines 10-13] clarifies that a prediction model could be a regression model.)
selecting a training set from the plurality of candidate training sets based on the evaluation result; and ([Col. 2 Lines 17-19] Lin discloses “A number of highest-ranked training examples is provided in response to a request.”)
predicting future usage ([Col. 1 Lines 19-20, 26-30] Lin discloses that the specification of the invention relates to predictive models, wherein “Predicting an output can include predicting future trends or behavior patterns,” and that the predictive model is trained with training data. [Col. 2 Lines 1-11, 17-46] Lin discloses how the training examples are scored based on the performance of the trained model, meaning the highest-ranked examples are used to train more accurate predictive models. Such scores include a difficulty score that measures the confidence of the training example associated with the correctness of a prediction made by the predictive model.)
Lin does not explicitly teach:
A method for predicting usage for cloud storage service performed by at least one computing device, the method comprising:
obtaining a time series dataset through monitoring usage of storage resource;
extracting a plurality of candidate training sets from the time series dataset;
wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource;
However, Fu teaches in the same field of endeavor:
A method for predicting usage for cloud storage service performed by at least one computing device, the method comprising: ([Pg. 1036 Col. 2 ¶2] discloses using a cloud computing system to help predict system storage demand for on-demand cloud storage.)
obtaining a time series dataset through monitoring usage of storage resource; ([Pg. 1037 Col. 1 ¶2, Tables 1-2] discloses how data including storage usage is collected within a specified time frame in intervals of 30 minutes.)
extracting([Pg. 1037 Col. 1 ¶2, Tables 1-2] illustrate system-level storage usage history and user-level storage usage history respectively with time attributes, extracted from the data in 30-minute intervals. [Pg. 1039 Last Para] discloses “given the present and historical time computer system storage usage, the neural network model can be trained to learn longer forward time prediction patterns.”)
([Pg. 1039 Col. 1 ¶4-6, Figs. 4a-b] illustrate a linear regression model graph of system storage usage where the independent variable on the x axis is a variable spanning the time between April to November and the dependent variable on the y axis is a usage of storage resource per the axis labels. [Pg. 1038 Col. 1 ¶1, Eq. 1] discloses the regression model equation wherein U--S is the dependent output of the cloud computing system utilization and time t is an independent input representing a time variable.);
predicting future usage of the storage resource through the linear regression model trained with the training set. ([Pg. 1039 Table 3, Pg. 1040 Figs. 5a-b] illustrates a predicted future usage of a storage resource determined. [Pg. 1039 Col. 2 ¶3-4] discloses the multi-layer perception neural network model used to predict future storage usage.)
Lin and Fu are both analogous art to the present invention because both are from the same field of endeavor directed towards using a subset of gathered data to train and test a predictive model such as linear regression.
It would have been obvious for one of ordinary skill in the art prior to the effective filing data of the claimed invention to modify Lin’s teachings by using the time series data to train the linear regression model to predict cloud storage usage as taught by Fu. One would have been motivated to make this modification in order to prevent resource failures and adapt to greater demands from increasing users (Fu Pg. 1036 §1 ¶2).
With respect to Claim 13:
Lin in view of Fu teaches:
A system for predicting usage for a cloud storage service comprising: one or more processors; and a memory for storing instructions, ([Col. 11 Lines 4-6] Lin discloses “The processes and logic flows described in this specification can be performed by one or more programmable processors,” [Col. 11 Lines 15-17] Lin discloses “a processor will receive instructions and data from a read-only memory or a random access memory or both.”)
wherein the one or more processors, by executing the stored instructions, perform operations comprising: (Claim 13 is a system claim that corresponds to Claim 1 and the rest of the limitations are rejected on the same ground.)
With respect to Claim 18:
Lin in view of Fu teaches:
A computer program combined with a computing device, ([Col. 10 Lines 12-17] Lin discloses implementing the method as a computer method encoded on a computing device.)
wherein the computer program is stored on a computer-readable recording medium for executing steps comprising: (Claim 18 is a non-statutory program-product claim that corresponds to Claim 1 and the rest of the limitations are rejected on the same ground.)
With respect to Claim 5:
Lin in view of Fu teaches: The method of Claim 1,
Lin further teaches: wherein suitability of each of the plurality of candidate training sets is evaluated based on a determination coefficient of a linear regression model for evaluation trained with a candidate training set. ([Col. 6 Lines 53-55] Lin discloses “The system computes a suggestion score for each training example according to each respective machine learning algorithm.” [Col. 5 Lines 27-39, Col. 6 Lines 17-31] Lin describes how the model scorers measure performance of the model for the suggestion score by testing on holdout data and determining the fitting of the training examples by comparing each measure to the overall average measured threshold, similarly to how the determination coefficient is used to indicate the model fit between predictor and outcome.)
With respect to Claim 15:
Claim 15 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.
With respect to Claim 6:
Lin in view of Fu teaches: The method of Claim 1, wherein the evaluating the suitability of the plurality of candidate training sets comprises:
Lin further teaches: evaluating suitability of a specific candidate training set based on a residual of a linear regression model for evaluation for the specific candidate training set. ([Col.7 Lines 45-62] Lin describes a difficulty score as a factor of the suggestion score used for suitability of the training examples, in which the difficulty score is measured as the difference between the predicted result and the actual result, similarly to how a linear regression model residual is commonly calculated.)
With respect to Claim 16:
Claim 16 recites analogous limitations to Claim 6 and therefore is rejected on the same ground as Claim 6.
With respect to Claim 10:
Lin in view of Fu teaches: The method of Claim 1, wherein the predicting the future usage comprises:
Fu further teaches: predicting usage of the storage resource at a future time point by inputting a value indicating the future time point into the trained linear regression model. ([Pg. 1039 Eq. 6, Col. 2 ¶3-4] Fu discloses the equation used for the system storage prediction calculation of the linear regression model, wherein t would be a time variable input indicating a future time point. [Pg. 1038 Col. 1 ¶2] Fu discloses Eq. 1 where future time point t can be plugged in to the equation to output the predicted storage resource usage U.)
With respect to Claim 11:
Lin in view of Fu teaches: The method of Claim 1, wherein the predicting the future usage comprises:
Fu further teaches: predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model. ([Pg. 1040 Fig. 5a-b] Fu illustrates prediction graphs for system storage usage. [Pg. 1039 Col. 2 ¶4] Fu discloses for the neural network predictor is used to predict system storage usage at a forward or later time.)
With respect to Claim 17:
Lin in view of Fu teaches: The method of Claim 1, wherein the predicting the future usage comprises:
Fu further teaches:
predicting usage of the storage resource at a future time point by inputting a value indicating the future time point into the trained linear regression model. ([Pg. 1039 Col. 2 ¶3, Eq. 6] Fu discloses the equation used for the system storage prediction calculation of the linear regression model, wherein t would be a time variable input indicating a future time point. [Pg. 1038 Col. 1 ¶2] Fu discloses Eq. 1, “designed for estimating the main trend of system utilization. At time t, the main trend of the cloud computing system utilization US is estimated with only the number of users of past D days as inputs.”)
predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model. ([Pg. 1040 Fig. 5a-b] Fu illustrates prediction graphs for system storage usage. [Pg. 1039 Col. 2 ¶4] Fu discloses “The time-ahead prediction results of system storage usage are shown in Fig. 5 (a) and (b). The neural network predictor can adapt to predict the system usage at longer forward time,”)
Claims 2-4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Fu and Chen et al. (US 2022/0036246 A1), hereinafter “Chen”.
With respect to Claim 2:
Lin in view of Fu teaches: The method of claim 1, wherein the extracting the plurality of candidate training sets comprises:
Lin in view of Fu does not teach:
dividing the time series dataset into a plurality of partial datasets;
extracting the most recent partial dataset among the plurality of partial datasets as a first candidate training set; and
extracting other partial dataset different from the most recent partial dataset as a second candidate training set.
However, Chen teaches in the same field of endeavor:
dividing the time series dataset into a plurality of partial datasets; ([0056] Chen discloses selecting subsets of the time series data to be used for purposes disclosed in the patent application.)
extracting the most recent partial dataset among the plurality of partial datasets as a first candidate training set; and ([0065] Chen discloses “For example, most recent data of the time series data can be included in the first subset,” [0056] Chen discloses “subsets of the time series data can be selected from newest to oldest” meaning the first subset may be the newest or most recent dataset of the time series data.)
extracting other partial dataset different from the most recent partial dataset as a second candidate training set. ([0065] Chen discloses “whereupon the formation of each subsequent subset includes the incremental addition of older and older data (e.g., as compared to the most recent data of the first subset”, meaning the other partial datasets will extract data that is different from the most recent partial dataset used in the first subset.)
Lin, Fu, and Chen are all analogous art to the present invention because they are from the same field of endeavor directed towards using a subset of gathered time-series data to train and test a machine learning model.
It would have been obvious for one of ordinary skill in the art prior to the effective filing data of the claimed invention to modify Lin in view of Fu’s teachings by using recent, neighboring data for the multiple training sets used in training the predictive model as taught by Chen. One would have been motivated to make this modification in order to keep data consistent and improve prediction performance of the model.
With respect to Claim 14:
Claim 14 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.
With respect to Claim 3:
Lin in view of Fu and Chen teaches: The method of Claim 2,
Chen further teaches: wherein the other partial dataset is a neighboring dataset of the most recent partial dataset. ([0030, 0056, 0065] Chen indicates that the data subsets may be sequentially selected from the time series data by chronological order.)
With respect to Claim 4:
Lin in view of Fu and Chen teaches: The method of Claim 2, wherein the evaluating the suitability of the plurality of candidate training sets comprises:
Lin further teaches:
evaluating suitability of the first candidate training set using a linear regression model for evaluation trained with the first candidate training set; ([Col. 4 Ln. 18] Lin discloses “The models can be trained in an online fashion, such that the model can be updated after each individual training example is received. After the models are trained, a suggestion scorer can compute a suggestion score for each received training example.”)
additionally training the linear regression model for evaluation with the other partial dataset; and ([Col. 4 Lines 18-20] Lin discloses how the model can be updated after receiving each training example, meaning other datasets can be used to train the model.)
evaluating suitability of the second candidate training set using the additionally trained linear regression model. ([Col. 4 Ln. 20-22] Lin discloses that a suggestion scorer can compute a score for each received training example, showing that the model may be evaluated again after receiving subsequent training sets.)
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Fu and Achin et al (US 2018/0046926 A1), hereinafter “Achin”.
With respect to Claim 7:
Lin in view of Fu teaches: The method of Claim 6, wherein the evaluating the suitability of the specific candidate training set comprises:
Lin in view of Fu does not explicitly teach:
training the linear regression model for evaluation using a first partial dataset of the specific candidate training set; and
calculating a residual of the linear regression model for evaluation using a second partial data set of the specific candidate training set different from the first partial data set.
However, Achin teaches in the same field of endeavor:
training the linear regression model for evaluation using a first partial dataset of the specific candidate training set; and ([0015] Achin discloses “(e) generating training data from the time-series data, wherein the training data include a first subset of the observations of at least one of the data sets… (g) fitting a predictive model to the training data;” [0400] discloses “Examples of predictive modeling families can include linear regression techniques,”)
calculating a residual of the linear regression model for evaluation using a second partial data set of the specific candidate training set different from the first partial data set. ([0074-0078] Achin discloses generating and comparing accuracy scores for a model trained with the second partition of the data set, wherein the accuracy score defined as [0078] “an accuracy with which the fitted model predicts outcomes of one or more prediction problems” correlates to the Applicant’s Specification [0092] defining residual as “the difference between the predicted value and the actual value.”)
Lin, Fu, and Achin are all analogous art to the present invention because they are from the same field of endeavor directed towards using time-series data to train and test a predictive model such as linear regression.
It would have been obvious for one of ordinary skill in the art prior to the effective filing data of the claimed invention to modify Lin’s teachings in view of Fu’s teachings by using the time series data to extract a first and second training set to support a predictive model as taught by Achin. One would have been motivated to make this modification in order to improve the training parameters of the predictive model to generate a more informed and accurate prediction.
With respect to Claim 8:
Lin in view of Fu and Achin teaches: The method of Claim 1,
Achin further teaches:
wherein the training set is a first training set selected from a first time series dataset generated through monitoring up to a first time point, ([0015] Achin discloses generating training data that includes a first subset that encompasses time ranges of training-input and training-output collections, the time ranges indicating an end time point.)
wherein the linear regression model is a first linear regression model for predicting future usage after the first time point, ([0015] Achin discloses “(g) fitting a predictive model to the training data;” [0400] Achin discloses “Examples of predictive modeling families can include linear regression techniques,”)
the method further comprises:
selecting a second training set from a second time series dataset obtained through monitoring up to a second time point after the first time point, wherein the second time series dataset comprises additional dataset generated through monitoring after the first time point; and ([0028] Achin discloses “each observation included in the first subset is associated with a time within the first range of training times, the third subset of observations corresponds to the sliding training window covering a second range of training times and each observation included in the third subset is associated with a time within the second range of training times, and an earliest time in the first range of training times is earlier than an earliest time in the second range of training times.”)
predicting future usage after the second time point through a second linear regression model trained with the second training set. ([0032] Achin discloses “(j) fitting the predictive model to the second training data to obtain a second fitted model;” [0045] discusses obtaining the accuracy score in which the fitted model is used to “predict one or more outcomes of the initial prediction problem.”)
With respect to Claim 9:
Lin in view of Fu and Achin teaches: The method of claim 8,
Achin further teaches:
wherein learned parameters of a linear regression model for evaluation obtained during a process of determining the first training set are stored in a storage, ([0231] Achin discloses how the exploration engine stores the state of the model after fitting, correlating to Applicant specification [0100] wherein the learned parameters may be understood as storing a model or snapshot of a model.)
wherein the selecting the second training set comprises:
updating the learned parameters by learning the additional dataset; ([0105] Achin discloses updating predictive models with new data.)
selecting the second training set by evaluating suitability of candidate training sets using the updated parameters; and ([0038] Achin discloses refreshing a model already fitted to a first time-series data by applying it to a second time-series data separate from the first data)
storing the updated parameters in the storage. ([0231] Achin discloses how the exploration engine stores the state of the model after fitting, in which “fitting” may be any fitting from first fitting with the first training set to any subsequent fittings with any subsequent training sets.)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Fu in view of Frey et al. (“Cloud Storage Prediction with Neural Networks”, CLOUD COMPUTING 2015: The Sixth International Conference on Cloud Computing, GRIDs, and Virtualization, Pages 52-56), hereinafter “Frey”.
With respect to Claim 12:
Lin in view of Fu teaches: The method of Claim 1,
Fu further teaches:
wherein the training set is a dataset for a specific client, wherein the predicting the future usage comprises: ([Pg. 1037 Col. 1 ¶2, Table 2] Fu illustrates how the second dataset used represents data at the user-level with specified user ID and data/time information pertinent to that specific user.)
predicting a time point when future usage of the storage resource allocated ([Pg. 1038 Col. 2 ¶1, Eq. 3] Fu discloses the utilization prediction UR(t) from the regression model that uses active user utilization data A(t) and the number of users in the system N(t) as an input, wherein one may use this equation to predict the storage utilization of a specific user by adjusting the parameters as such i.e. number of users N(t) = 1 and A(t) only represents that specific user’s utilization data.)
allocating additional storage([Pg. 1038 Col. 2 Last Para.] Fu discloses how the user can request computational resources such as storage, [Pg. 1039 Col. 1 ¶3] Fu discloses how the system receives its prediction results and will subsequently adjust the existing planning of the cloud computing system, i.e., “demand of users will be met promptly without delay,” which may include additional storage allocation)
Lin in view of Fu does not teach: storage resource allocated to the specific client
However, Frey teaches in the same field of endeavor: storage resource allocated to the specific client ([Pgs. 54-55 Last Para of Pg. 54 - First Para of Pg. 55] Frey describes a scenario in which storage is allocated to one individual customer, [Pg. 55 Fig. 6, Col. 1 ¶2-4] Frey shows how the amount of storage allocated to the individual user in the scenario is adjusted based on the prediction results of the neural network solution.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lin in view of Fu by predicting the future time of storage usage of a specific user and subsequently allocating additional storage to a specific user as taught by Frey. Performing prediction analysis and taking resulting action on an individual basis further reduces wasted resources or over-utilization of resources compared to a general/average system-wide analysis and action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fairbank et al. (US 10,417,556 B1) discloses using time-series data segmented into training data sets to be used to train a neural network in predicting future outcomes.
Huang et al. (US 2022/0114460 A1) discloses a model training device that assesses each item of training time series data to evaluate performance and identify the most suitable representation for each item of the training time series data.
Hu et al. (US 2018/0097744 A1) discloses forecasting a number of servers required in a cloud-based system at a future date based on a prediction model using time series data.
Koezuka et al. (US 2019/0294989 A1) discloses selecting a plurality of training segments from among the plurality of segments and generating a decision model based on the selected plurality of training segments.
Serita (US 2022/0405161 A1) discloses evaluating a time-series first and second data set to the suitability of a failure prediction model.
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/BRIAN D. BUI/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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