Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 3/17/2026 which provides amendments to claims 1-8, 10-11, 15, and 17-19 has been entered. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C § 101 filed 3/17/2026 (pages 11-12 of applicant’s arguments) have been fully considered but they are not persuasive.
Applicant argues that the claimed invention is directed to improving the function of machine learning model and is thus integrated into a practical application. The examiner respectfully disagrees. The applicant describes “the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model” (applicant’s argument page 12). Determining the forecasting error and the corrected value are seen as a mental process. Given an output and an actual value a human could determine the forecasting error. Similarly given the forecasting error a human could determine the corrected value. Having a generic machine learning model do this process does not make it any less of a mental process. Similarly the applicant describes “the updated training data is used to train the first machine learning model to generate an updated machine learning model with an improved measure of accuracy with respect to data predicted by the first machine learning model”. Once again, updating training data is a mental process as it is something a human could do using pen and paper. According to the MPEP 2106.05(a) the improvement cannot come from the judicial exception (abstract idea) alone. The additional elements are directed to obtaining data, using generic machine learning models, training those models, and causing resource allocation based on the output of the machine learning models. None of these show an improvement to machine learning. Thus the 101 rejection is maintained.
Applicant’s arguments with respect to 35 U.S.C § 103 filed 3/17/2026 (pages 13-15 of applicant’s arguments) have been fully considered but they are not persuasive.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant merely reproduces portions of the claim language and asserts, in a conclusory manner, that the cited references, individually or in combination, fail to disclose or suggest the recited limitations. The new limitations added to claim 1 mostly come from dependent claim 7, which applicant claims the prior art Siami-Namini (NPL: ‘The Performance of LSTM and BiLSTM in Forecasting Time Series’) nor Vishwakarma (US 20210034425 A1) teaches these elements. Siami-Namini does teach forecasting error as it determines the root mean square error (a type of forecasting error) the other limitations that have been added to claim 1 which were previously in claim 7 were rejected using Zhang (US 11,392,437 B1). Applicant fails to mention/argue why Zhang does not does these elements. Zhang does teach calculating a value, based off of predictive error, and then use this value to update training data. This teaches the corrected value that is now in amended claim 1.
Thus the 103 rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory (a process, machine, manufacture or composition of matter.) Claims 1-7 describe a process and 8-20 describes a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
generating, …, a first output relating to an event during a first period of time, wherein the first machine learning model is trained using training data; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
determining,…, a forecasting error value based on a difference between the first output and the actual data; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determining, …, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
updating,…, the training data using the corrected value to generate updated training data, wherein the updated training data is generated to improve the measure of accuracy of data predicted by the first machine learning model (This is an abstract idea of a "Mental Process." The "updating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generating, …, a second output relating to the event during a third period of time; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
using a first machine learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
obtaining actual data relating to the event during a second period of time that precedes the first period of time; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
By a second machine learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
training, using the updated training data, the first machine learning model to generate an updated machine learning mode with an improved measure of accuracy with respect to data predicted by the first machine learning model; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
using the updated machine learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
causing one or more resources to be allocated based on the second output. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “using a machine learning model”, “by a second machine learning model”, “training…” and “using the updated machine learning model” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “obtaining…” and “causing…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 1 is ineligible
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, recites an additional abstract idea:
determining a difference between the first output and the actual data; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determining could be done manually by an individual.)
determining whether the difference satisfies a threshold; and (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determining could be done manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training the first machine learning model using the updated training data based on determining whether the difference satisfies the threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 3, recites an additional abstract idea:
determining that the difference satisfies the threshold; and (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determining could be done manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training the first machine learning model using the updated training data based on determining that the difference satisfies the threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training a deep learning model using the updated training data. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training a bi-directional long-short term memory (Bi-LSTM) model using the updated training data. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, recites an additional abstract idea:
generating first time series forecasting regarding the event; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generating second time series forecasting regarding the event. (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
converting the training data to a one timestep input sequence; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
training the first machine learning model using the one timestep input sequence prior to generating the first output; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “converting…” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “training the machine learning model…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 1, recites an additional abstract idea:
determining,…, the corrected value based on the forecasting error value satisfying a threshold; and (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
By the second machine learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 7 is ineligible.
With respect to claim 8:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
Generate,…, a first output relating to an event during a first period of time, wherein the Bi-LSTM model is trained using training data; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
determine,… , a forecasting error value based on a difference between the first output and the actual data; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine,…, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
update, …, the training data using the corrected value to generate updated training data, wherein the updated training data is generated to improve the measure of accuracy of data predicted by the first machine learning model; (This is an abstract idea of a "Mental Process." The "update" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generate, …, a second output relating to the event during a third period of time; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using a bi-directional long-short term memory (Bi-LSTM) model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
obtain actual data relating to the event during a second period of time that precedes the first period of time; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
By an agent learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
train, using the updated training data, the Bi-LSTM model to generate an updated Bi-LSTM model with improved measure of accuracy; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
using the updated Bi-LSTM model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
provide the second output to cause one or more resources to be allocated. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “using a bi-direction…”, “by an agent learning model”, “train…”, and “using the updating Bi-LSTM model” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “obtain…” and “provide…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 8 is ineligible.
With respect to claim 9:
Step 2A Prong 1: claim 9, which incorporates the rejection of claim 8, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
generate the Bi-LSTM model based on a one timestep input sequence; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
convert the training data to a timestep input sequence; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
train the Bi-LSTM model using the timestep input sequence prior to generating the first output. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “generate…” and “train…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional element “convert…” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 9 is ineligible.
With respect to claim 10:
Step 2A Prong 1: claim 10, which incorporates the rejection of claim 8, recites an additional abstract idea:
Incrementally insert accurate forecasts data points in the training data to generate the updated training data based on the forecasting error value. (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: claim 10 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 10 does not recite an additional element.
Therefore, claim 10 is ineligible.
With respect to claim 11:
Step 2A Prong 1: claim 11, which incorporates the rejection of claim 8, recites an additional abstract idea:
determine,…, the corrected value for the first output based on the forecasting error value satisfying a threshold; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using the agent learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 11 is ineligible.
With respect to claim 12:
Step 2A Prong 1: claim 12, which incorporates the rejection of claim 8, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
forecast a first timestep output sequence regarding the event; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
forecast a second timestep output sequence regarding the event. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 12 is ineligible.
With respect to claim 13:
Step 2A Prong 1: claim 13, which incorporates the rejection of claim 8, recites an additional abstract idea:
determine a difference between the first output and the actual data; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine whether the difference satisfies a threshold; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
train the Bi-LSTM model using the updated training data based on determining whether the difference satisfies the threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 13 is ineligible.
With respect to claim 14:
Step 2A Prong 1: claim 14, which incorporates the rejection of claim 13, recites an additional abstract idea:
determine that the difference satisfies the threshold; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
train the Bi-LSTM model using the updated training data based on determining that the difference satisfies the threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 14 is ineligible.
With respect to claim 15:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
generate,…, a first output relating to an event during a first period of time, wherein the Bi-LSTM model is trained using training data; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
determine,… , a forecasting error value based on a difference between the first output and the actual data; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine,…, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
update, …, the training data using the corrected value to generate updated training data based on the training data and the actual data; (This is an abstract idea of a "Mental Process." The "update" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
generate,…, a second output relating to the event during a third period of time; and (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using a bi-directional long-short term memory (Bi-LSTM) model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
obtain actual data relating to the event during a second period of time that precedes the first period of time; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
By an agent learning model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
train, using the updated training data, the Bi-LSTM model to generate an updated Bi-LSTM model; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
using the updated Bi-LSTM model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
cause one or more resources to be allocated based on the second output. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “using a bi-directional…”, “by an agent learning model” “train…”, and “using the updated Bi-LSTM model” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “obtain…” and “cause one or more resources…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 15 is ineligible.
With respect to claim 16:
Step 2A Prong 1: claim 16, which incorporates the rejection of claim 15, recites an additional abstract idea:
generate first time series forecasting regarding the event; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be done manually by an individual.)
generate second time series forecasting regarding the event (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be done manually by an individual.)
Step 2A Prong 2: claim 16 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 16 does not recite an additional element.
Therefore, claim 16 is ineligible.
With respect to claim 17:
Step 2A Prong 1: claim 17, which incorporates the rejection of claim 15, recites an additional abstract idea:
incrementally insert accurate forecasts data points in the training data to generate the updated training data based on the forecasting error value. (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: claim 17 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 17 does not recite an additional element.
Therefore, claim 17 is ineligible.
With respect to claim 18:
Step 2A Prong 1: claim 18, which incorporates the rejection of claim 15, recites an additional abstract idea:
determine the corrected value based on the forecasting error value satisfying a threshold; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: claim 18 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 18 does not recite an additional element.
Therefore, claim 18 is ineligible.
With respect to claim 19:
Step 2A Prong 1: claim 19, which incorporates the rejection of claim 15, recites an additional abstract idea:
determine whether the difference satisfies a threshold; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine the corrected value for the first output based on based on determining whether the difference satisfies the threshold. (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: claim 19 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 19 does not recite an additional element.
Therefore, claim 19 is ineligible.
With respect to claim 20:
Step 2A Prong 1: claim 20, which incorporates the rejection of claim 19, recites an additional abstract idea:
determine that the difference satisfies the threshold; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
include the corrected value in the updated training data; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
train the Bi-LSTM model using the updated training data based on determining that the difference satisfies the threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “include…” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “train the Bi-LSTM” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 20 is ineligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Siami-Namini (NPL: ‘The Performance of LSTM and BiLSTM in Forecasting Time Series’) in view of Vishwakarma (US 20210034425 A1) and Zhang (US 11,392,437 B1)..
Regarding claim 1, Siami-Namini teaches:
A method by a device, (Abstract)
generating, using a first machine learning model, a first output relating to an event during a first period of time, wherein the first machine learning model is trained using training data; (Listing 1: Here the algorithm shows that it is making “one-step forecast” (outputs) and is trained using training data).
obtaining actual data relating to the event during a second period of time that precedes the first period of time; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10. Where y_i is the actual data).
Determining, by a second machine learning model, a forecasting error value based on a difference between the first output and the actual data; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10.)
Updating, by the second machine learning model, the training data using the corrected value to generate updated training data, wherein the updated training data is generated to improve the measure of accuracy of data predicted by the first machine learning model; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).” And end of section III. “Applying the LSTM twice leads to improve learning long-term dependencies and thus consequently will improve the accuracy of the model”)
training, using the updated training data, the first machine learning model to generate an updated machine learning model with an improved measure of accuracy with respect to data predicted by the first machine learning model; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).”)
Siami-Namini does not teach:
determining, by the second machine learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
generating, using the updated machine learning model, a second output relating to the event during a third period of time; and
causing one or more resources to be allocated based on the second output.
However, Vishwakarma teaches some of these:
generating, using the updated machine learning model, a second output relating to the event during a third period of time; and ([0034] “In Step 302, a predictive model for the background service task is generated. In one embodiment of the invention, the predictive model may refer to an optimized statistical and/or machine learning model directed to forecasting outcomes (e.g., background service task durations) given a set of predictors (e.g., a feature set).”)
causing one or more resources to be allocated based on the second output. ([0053] “Accordingly, the backup storage system resources, allocated to the background service task, may change based on and may be proportional to the predicted availability of these backup storage system resources reflected in the forecast time-series.”)
Siami-Namini and Vishwakarma are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the data and output of Vishwakarma. One would want to do this for a more effective way of allocating resources.
Neither Siami-Namini or Vishwakarma teach:
determining, by the second machine learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
However, Zhang does:
determining, by the second machine learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp” also Zhang updates training data based on the corrected value: Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Siami-Namini, Vishwakarma and Zhang are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the output of Vishwakarma and the training data updating method of Zhang. One would want to do this to have data values converge faster (Zhang Col 3 lines 20-26).
Regarding claim 2, Siami-Namini in view of Vishwakarma and Zhang teaches claim 1 as outlined above. Zhang further teaches:
determining a difference between the first output and the actual data; (Col 15 lines 19-30 “The time-series model is built using the training data, and setting x equal to a value between the observed value of the server metric and the predicted value includes: compute the difference between the observed value of the server metric and the predicted value; initialize i to L; compute a regularizing factor that approaches zero as i increases; determine standard deviation based on the training data; compute an adjustment value based on the difference, regularizing factor, and standard deviation; and add the adjustment value to the predicted value to obtain the value between the observed value and the predicted value.”)
determining whether the difference satisfies a threshold; and (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
training the first machine learning model using the updated training data based on determining whether the difference satisfies the threshold. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Regarding claim 3, Siami-Namini in view of Vishwakarma and Zhang teaches claim 2 as outlined above. Zhang further teaches:
determining that the difference satisfies the threshold; and (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
training the first machine learning model using the updated training data based on determining that the difference satisfies the threshold. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Regarding claim 4, Siami-Namini in view of Vishwakarma and Zhang teaches claim 1 as outlined above. Siami-Namini further teaches:
training a deep learning model using the updated training data. (Section V. The Algorithms “The deep-bidirectional LSTMs (BiLSTM) networks [25] are a variation of normal LSTMs (Figure 1(d)), in which the desired model is trained not only from inputs to outputs, but also from outputs to inputs.”)
Regarding claim 5, Siami-Namini in view of Vishwakarma and Zhang teaches claim 1 as outlined above. Siami-Namini further teaches:
training a bi-directional long-short term memory (Bi-LSTM) model using the updated training data. (Section V. The Algorithms “The deep-bidirectional LSTMs (BiLSTM) networks [25] are a variation of normal LSTMs (Figure 1(d)), in which the desired model is trained not only from inputs to outputs, but also from outputs to inputs.”)
Regarding claim 6, Siami-Namini in view of Vishwakarma and Zhang teaches claim 1 as outlined above. Siami-Namini further teaches:
converting the training data to a one timestep input sequence; and (Section III. Background “This special RNNs memory is called recurrent hidden states and gives the RNNs the ability to predict what input is coming next in the sequence of input data. In theory, RNNs are able to leverage previous sequential information for arbitrary long sequences. In practice, however, due to RNNs’ memory limitations, the length of the sequential information is limited to only a few steps back.”)
training the first machine learning model using the one timestep input sequence prior to generating the first output; (Section V. The Algorithms “On the other hand, the Recurrent-based Neural Networks (RNNs) remember parts of the past data through a methodology, called feedback, in which the training takes place not only from input to output (as feed-forward), but also it utilizes a loop in the network to preserve some information and thus functions like a memory (Figure 1(b)).”)
generating first time series forecasting regarding the event; and (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
generating second time series forecasting regarding the event. (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
Regarding claim 7, Siami-Namini in view of Vishwakarma and Zhang teaches claim 1 as outlined above.
Zhang teaches:
determining, by the second machine learning model, a corrected value based on the forecasting error value satisfying a threshold; and (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp”)
generating the updated training data based on the corrected value. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Regarding claim 8, Siami-Namini teaches:
generate, using a bi-directional long-short term memory (Bi-LSTM) model, a first output relating to an event during a first period of time, wherein the Bi-LSTM model is trained using training data; (Listing 1: Here the algorithm shows that it is making “one-step forecast” (outputs) and is trained using training data).
obtain actual data relating to the event during a second period of time that precedes the first period of time; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10. Where y_i is the actual data).
determine, by an agent learning model, a forecasting error value based on a difference between the first output and the actual data; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10.)
update, by the agent learning model, the training data using the corrected value to generate updated training data wherein the updated training data is generated to improve the measure of accuracy of data predicted by the first machine learning model; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).”)
train, using the updated training data, the Bi-LSTM model to generate an updated Bi-LSTM model with an improved measure of accuracy; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).”)
Siami-Namini does not teach:
A device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
generate, using the updated Bi-LSTM model, a second output relating to the event during a third period of time; and
provide the second output to cause one or more resources to be allocated.
However, Vishwakarma does teach some of these:
A device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: ([0023] “More specifically, a compute resource may pertain to a physical device hardware), a logical intelligence (i.e., software), or a combination thereof, which may provide computing and/or processing functionality on the backup storage system (102). Central processing units (CPU), graphics processing units (GPU), and/or memory (e.g., random access memory (RAM)) may exemplify compute resources residing on the backup storage system (102).”)
generate, using the updated Bi-LSTM model, a second output relating to the event during a third period of time; and ([0034] “In Step 302, a predictive model for the background service task is generated. In one embodiment of the invention, the predictive model may refer to an optimized statistical and/or machine learning model directed to forecasting outcomes (e.g., background service task durations) given a set of predictors (e.g., a feature set).” And Siami-Namini teaches the Bi-LSTM model)
provide the second output to cause one or more resources to be allocated. ([0053] “Accordingly, the backup storage system resources, allocated to the background service task, may change based on and may be proportional to the predicted availability of these backup storage system resources reflected in the forecast time-series.”)
Siami-Namini and Vishwakarma are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the data and output of Vishwakarma. One would want to do this for a more effective way of allocating resources.
Neither Siami-Namini or Vishwakarma teach:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
However, Zhang does:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp” also Zhang updates training data based on the corrected value: Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Siami-Namini, Vishwakarma and Zhang are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the output of Vishwakarma and the training data updating method of Zhang. One would want to do this to have data values converge faster (Zhang Col 3 lines 20-26).
Regarding claim 9, Siami-Namini in view of Vishwakarma and Zhang teaches claim 8 as outlined above. Siami-Namini further teaches:
generate the Bi-LSTM model based on a one timestep input sequence; (Section V. The Algorithms “The deep-bidirectional LSTMs (BiLSTM) networks [25] are a variation of normal LSTMs (Figure 1(d)), in which the desired model is trained not only from inputs to outputs, but also from outputs to inputs. More precisely, given the input sequence of data, a BiLSTM model first feed input data to an LSTM model (feedback layer), and then repeat the training via another LSTM model but on the reverse order of the sequence of the input data”)
convert the training data to a timestep input sequence; and (Section III. Background “This special RNNs memory is called recurrent hidden states and gives the RNNs the ability to predict what input is coming next in the sequence of input data. In theory, RNNs are able to leverage previous sequential information for arbitrary long sequences. In practice, however, due to RNNs’ memory limitations, the length of the sequential information is limited to only a few steps back.”)
train the Bi-LSTM model using the timestep input sequence prior to generating the first output. (Section V. The Algorithms “On the other hand, the Recurrent-based Neural Networks (RNNs) remember parts of the past data through a methodology, called feedback, in which the training takes place not only from input to output (as feed-forward), but also it utilizes a loop in the network to preserve some information and thus functions like a memory (Figure 1(b)).”)
Regarding claim 10, Siami-Namini in view of Vishwakarma and Zhang teaches claim 8 as outlined above. Siami-Namini further teaches:
incrementally insert accurate forecasts data points in the training data to generate the updated training data based on the forecasting error value. (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).” This is an algorithm and is repeated multiple times)
Regarding claim 11, Siami-Namini in view of Vishwakarma and Zhang teaches claim 8 as outlined above. Zhang further teaches:
determine, using the agent learning model, the corrected value for the first output based on the forecasting error value satisfying a threshold; and (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the
Regarding claim 12, Siami-Namini in view of Vishwakarma and Zhang teaches claim 8 as outlined above. Siami-Namini further teaches:
forecast a first timestep output sequence regarding the event; and (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
forecast a second timestep output sequence regarding the event. (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
Regarding claim 13, Siami-Namini in view of Vishwakarma and Zhang teaches claim 8 as outlined above. Zhang further teaches:
determining a difference between the first output and the actual data; (Col 15 lines 19-30 “The time-series model is built using the training data, and setting x equal to a value between the observed value of the server metric and the predicted value includes: compute the difference between the observed value of the server metric and the predicted value; initialize i to L; compute a regularizing factor that approaches zero as i increases; determine standard deviation based on the training data; compute an adjustment value based on the difference, regularizing factor, and standard deviation; and add the adjustment value to the predicted value to obtain the value between the observed value and the predicted value.”)
determining whether the difference satisfies a threshold; and (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
train the Bi-LSTM model using the updated training data based on determining whether the difference satisfies the threshold. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.” Where Siami-Namini teaches the Bi-LSTM model.)
Regarding claim 14, Siami-Namini in view of Vishwakarma and Zhang teaches claim 13 as outlined above. Zhang further teaches:
determine that the difference satisfies the threshold; and (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
train the Bi-LSTM model using the updated training data based on determining that the difference satisfies the threshold. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.” Where Siami-Namini teaches the Bi-LSTM model.)
Regarding claim 15, Siami-Namini teaches:
generate, using a bi-directional long-short term memory (Bi-LSTM) model, a first output relating to an event during a first period of time, wherein the Bi-LSTM model is trained using training data; (Listing 1: Here the algorithm shows that it is making “one-step forecast” (outputs) and is trained using training data).
obtain actual data relating to the event during a second period of time that precedes the first period of time; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10. Where y_i is the actual data).
determine, by an agent learning model, a forecasting error value based on a difference between the first output and the actual data; (Section IV. LSTM vs BiLSTM: An Experimental Study; C. Assessment Metrics they describe calculating Root Mean Square Error in equation 10.)
update, by the agent learning model, the training data using the corrected value to generate updated training data based on the training data and the actual data; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).”)
train, using the updated training data, the Bi-LSTM model to generate an updated Bi-LSTM model; (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).”)
Siami-Namini does not teach:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
generate, using the updated Bi-LSTM model, a second output relating to the event during a third period of time; and
cause one or more resources to be allocated based on the second output.
However, Vishwakarma does teach some of these:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: ([0057] “Software instructions in the form of computer readable program code to perform embodiments of the invention may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium”)
generate, using the updated Bi-LSTM model, a second output relating to the event during a third period of time; and ([0034] “In Step 302, a predictive model for the background service task is generated. In one embodiment of the invention, the predictive model may refer to an optimized statistical and/or machine learning model directed to forecasting outcomes (e.g., background service task durations) given a set of predictors (e.g., a feature set).” And Siami-Namini teaches the Bi-LSTM model)
cause one or more resources to be allocated based on the second output. ([0053] “Accordingly, the backup storage system resources, allocated to the background service task, may change based on and may be proportional to the predicted availability of these backup storage system resources reflected in the forecast time-series.”)
Siami-Namini and Vishwakarma are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the data and output of Vishwakarma. One would want to do this for a more effective way of allocating resources.
Neither Siami-Namini or Vishwakarma teach:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model
However, Zhang does:
Determine, by the agent learning model, a corrected value for the first output based on the forecasting error value, wherein the forecasting error value and the corrected value are determined to improve a measure of accuracy of data predicted by the first machine learning model (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp” also Zhang updates training data based on the corrected value: Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.”)
Siami-Namini, Vishwakarma and Zhang are considered analogous art to the claimed invention because they are in the same field of endeavor being time-series data forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system and models of Siami-Namini with the output of Vishwakarma and the training data updating method of Zhang. One would want to do this to have data values converge faster (Zhang Col 3 lines 20-26).
Regarding claim 16, Siami-Namini in view of Vishwakarma and Zhang teaches claim 15 as outlined above. Siami-Namini further teaches:
generate first time series forecasting regarding the event; and (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
generate second time series forecasting regarding the event. (Section V. The Algorithms describes the algorithm used to generated time series forecasting.)
Regarding claim 17, Siami-Namini in view of Vishwakarma and Zhang teaches claim 15 as outlined above. Siami-Namini further teaches:
incrementally insert accurate forecasts data points in the training data to generate the updated training data based on the forecasting error value. (Section V. The Algorithm “Hence, once a prediction is performed and its value is compared with the actual value, the value is added to the training set (line 26), and the model is re-trained (line 27).” This is an algorithm and is repeated multiple times).
Regarding claim 18, Siami-Namini in view of Vishwakarma and Zhang teaches claim 17 as outlined above. Zhang further teaches:
determine the corrected value based on the forecasting error value satisfying a threshold; and (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp”)
Regarding claim 19, Siami-Namini in view of Vishwakarma and Zhang teaches claim 15 as outlined above. Zhang further teaches:
determining whether the difference satisfies a threshold; and (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
determine the corrected value for the first output based on based on determining whether the difference satisfies the threshold. (Col 11 lines 52-58 “As can be seen in the graph on the bottom FIG. 6a, the value v.sub.i of the target metric, as presently observed at time t.sub.1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward. Thus, x.sub.i is set to a value between the observed value v.sub.i and the predicted value p.sub.i and the corresponding time stamp”)
Regarding claim 20, Siami-Namini in view of Vishwakarma and Zhang teaches claim 19 as outlined above. Zhang further teaches:
determine that the difference satisfies the threshold; (Col 11 lines 52-56 “As can be seen in the graph on the bottom FIG. 6a, the value v_i of the target metric, as presently observed at time t_1 for the added server 203 is within the failure threshold but outside the expected threshold, as depicted with a solid black triangle that points upward”)
include the corrected value in the updated training data; and (Col 11 lines 63-65 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x.sub.i, and the time-series model is updated at 323 or 517 using the updated training data”)
train the Bi-LSTM model using the updated training data based on determining that the difference satisfies the threshold. (Col 11-12 lines 63-67 and lines 1-2 “The training data {tilde over (X)} is updated at 321 or 515 to include the resulting value for x_i, and the time-series model is updated at 323 or 517 using the updated training data {tilde over (X)}. Note that subsequent predicted values, upper and lower failure thresholds, and upper and lower expected thresholds can change with each iteration of the model, based on the last addition of x_i to training data {tilde over (X)}.” Where Siami-Namini teaches the Bi-LSTM model.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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