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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 15 2021 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
101 CRM arguments
The previous 101 CRM rejections to claims 7-12 are withdrawn in view of the Applicant’s amendments and comments starting on page 8.
103 arguments
Applicant asserts:
Applicant argues, on page 9, that the prior art and suggested art does not teach the use of “subsets” and “based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, wherein the other dataset is a next oldest data subset (W-1); and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;”.
Examiner response:
Examiner respectfully disagrees. Examiner notes that arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner notes that the amended claim 1 is given a 103 rejection as shown below.
Applicant asserts:
Applicant further argues that the MetOffice mapped as the original version of a time series model is too broad and suggests the prior art as faulty.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the specifics of the “original time series model” is not claimed. With regards to Peacock Paragraph 0065, it shows that MetOffice is used as a base/original model to build interim models/small regression models that are improved upon using MetOffice’s forecasts as inputs.
All dependent claim incorporate all of the limitation of their respective base claims and are rejected for at least the same reasons.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 112b
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.
The term “strong” in claim 1 is a relative term which renders the claim indefinite. The term “strong” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner notes that the strength of the correlation is indefinite.
Claim Rejections - 35 USC § 101 – Abstract ideas
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 1-4, 6-10, 12-16, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally split the data into k subsets.
“iteratively updating the original version of the time series model by backwards data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by backwards data selection.
“computing residuals on the W data subset to determine new patterns of residuals;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could calculate residuals on the W data subset to determine new patterns of residuals.
“based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally note variables to indicate time location of pattern changes.
“updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by adding new predictors of pattern change to their evaluation.
“and evaluating the interim time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate their model.
“comparing accuracy of the interim time series model to a previous interim version of the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could compare the accuracy of the interim time series model to a previous interim version of the time series model.
“based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, wherein the other dataset is a next oldest data subset (W-1);” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model to interim model and evaluate it with another data subset.
“and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could not use the interim model and use the it to train the next mental model.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer-implemented method (CIM) comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer-implemented method (CIM) comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CIM of claim 1 further comprising: using the updated version of the time series model to predict values of newly incoming data in a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could predict values of newly incoming data in time series data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CIM of claim 1 further comprising: prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could build a mental model based evaluating the historical time series dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CIM of claim 3 further comprising: prior to updating the time series model by backwards data selection, evaluating the original version of the time series model based on the new data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the original version of the time series model on the new dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CIM of claim 1 further comprising: handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could handle a new exogenous variable and a created variable in newly received data of a time series data stream.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally split the data into k subsets.
“iteratively updating the original version of the time series model by backwards data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by backwards data selection.
“computing residuals on the W data subset to determine new patterns of residuals;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could calculate residuals on the W data subset to determine new patterns of residuals.
“based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally note variables to indicate time location of pattern changes.
“updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by adding new predictors of pattern change to their evaluation.
“and evaluating the interim time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate their model.
“comparing accuracy of the interim time series model to a previous interim version of the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could compare the accuracy of the interim time series model to a previous interim version of the time series model.
“based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, wherein the other dataset is a next oldest data subset (W-1);” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model to interim model and evaluate it with another data subset.
“and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could not use the interim model and use the it to train the next mental model.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer program product (CPP) comprising: a non-transitory tangible set of storage device(s); and computer code stored collectively in the non-transitory tangible set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer program product (CPP) comprising: a non-transitory tangible set of storage device(s); and computer code stored collectively in the non-transitory tangible set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): using the updated version of the time series model to predict values of newly incoming data in a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could predict values of newly incoming data in a time series data stream.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could build a mental model based evaluating the historical time series dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 10:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CPP of claim 9 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):prior to updating the time series model by backwards data selection, evaluating the original version of the time series model based on the new data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the original version of the time series model on the new dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 12:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could handle a new exogenous variable and a created variable in newly received data of a time series data stream.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally split the data into k subsets.
“iteratively updating the original version of the time series model by backwards data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by backwards data selection.
“computing residuals on the W data subset to determine new patterns of residuals;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could calculate residuals on the W data subset to determine new patterns of residuals.
“based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could mentally note variables to indicate time location of pattern changes.
“updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model by adding new predictors of pattern change to their evaluation.
“and evaluating the interim time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could evaluate their model.
“comparing accuracy of the interim time series model to a previous interim version of the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could compare the accuracy of the interim time series model to a previous interim version of the time series model.
“based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, wherein the other dataset is a next oldest data subset (W-1);” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could update their mental model to interim model and evaluate it with another data subset.
“and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could not use the interim model and use the it to train the next mental model.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer system (CS) comprising: a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer system (CS) comprising: a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving an original version of a time series model;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving a historical time series data set including historically observed time series data;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 14:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): using the updated version of the time series model to predict values of newly incoming data in a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could predict values of newly incoming data in a time series data stream.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could build a mental model based evaluating the historical time series dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CS of claim 15 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):prior to updating the time series model by backwards data selection, evaluating the original version of the time series model based on the new data set.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the original version of the time series model on the new dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could handle a new exogenous variable and a created variable in newly received data of a time series data stream.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
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.
Claim(s) 1- 4, 6-10, 12-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over PEACOCK; Andrew Duncan et al; US 20170131435 A1 (hereinafter “Peacock”) in view of Malizia Nicholas et al; WO 2021007352 A1; (hereinafter “Malizia”) in further view of MEHTA; Anil et al; US 20170161614 A1 (hereinafter “Mehta”) in further view of Jason Brownlee; “How to Model Residual Errors to Correct Time Series Forecasts with Python” (hereinafter “Jason”) in further view of Kohei Maruchi et al; US 20190012553 A1 (hereinafter “Kohei”).
Regarding Claim 1, Peacock teaches A computer-implemented method (CIM) comprising: (Peacock Paragraph 0029 “The weather prediction system 10 is a computer-based system that includes at least one computer processor and at least one memory.” Examiner notes that invention is computer implemented.);
receiving an original version of a time series model; (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models.” Peacock Paragraph 0034; “Broadly speaking, site identification is done via access to lists of weather resources that can be accessed on-line from sources such as the MetOffice and forecast.io. Once the sites are selected, weather observation variables and weather forecast variables are repeatedly retrieved and stored over time from the plurality of different weather data sources, as shown in FIG. 2.” Examiner notes the original time series model is the predictive model before it is rebuilt; The MetOffice model has weather forecast and weather observation which are used to build the first iteration of the invention model. MetOffice is also considered an original version of a time series model and is received online);
receiving a historical time series data set including historically observed time series data; (Peacock Paragraph 0029; “Each observations site 12 provides weather variable observations including current values and historical values, typically for the previous 24 hours. Each forecast site 14 provides weather variable forecasts in the hours ahead typically over 24 or 48 hour periods.” Examiner notes receiving data from observation sites; historical values as historical data of historically observed weather conditions);
iteratively updating the original version of the time series model by [backwards] data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data; (Peacock Paragraph 0053; “Once the sites are selected, the feature selection process begins. This uses a process known as forward selection, which is particularly applicable for large feature sets.” Peacock Paragraph 0059; “Whilst forward selection is described here, it will be appreciated that any suitable form of feature selection could be used. Feature selection techniques are well known in the art and so will not be described in detail.” Peacock Paragraph 0061; “As previously indicated, typically 24 specific values are forecast for each variable, covering from 1 to 24 hours ahead inclusive. To meet overall requirements, this means, altogether, 168 feature selection runs leading to 168 specific predictive models.” Peacock Paragraph 0039; "Re-building the models is done by re-doing the feature selection process on a current version of all of the stored data and then rebuilding the models based on the results of that feature selection.” Examiner notes data selection is performed to obtain an interim version of the time series models for each variable; as new data is collected in the current version of all the stored data, the model is iteratively updated by feature selection; current version of all the stored data is data subset that is closest to new data);
Peacock does not teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;
However, Malizia does teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter; (Malizia Paragraph 0070; "With the daily satellite and weather data matched to phenology periods and the subset of counties of interest identified, the process of creating features to be employed in the predictive model is begun." Malizia Paragraph 0009; "In some embodiments, dividing the predetermined time period into the one or more phenology periods comprises: sampling a plurality of pixels of the time series of satellite imagery; determining a time series of vegetation indices based on the sampled pixels; and locating peaks in the time series of vegetation indices." Malizia Paragraph 0115; "For example, in the early season, from the end of June to the beginning of July, the most important features are the year, and the historical average of the NDWI during the second phenology period" Examiner notes based on the number of peaks located/K, that is the input parameter for number of phenology periods/subsets; second phenology period indicates that phenology periods/subsets are divided into latest data subsets to oldest data subsets)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock and Malizia. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. One of ordinary skill would have motivation to combine Peacock and Malizia to break up the historical data into k number of subsets to narrow the focus of the data “The phenology periods themselves were defined via a mathematical optimization process that breaks up the growing season for each crop into segments such that summaries of the predictors across those segments offer the maximum correlation with end of season yields.” (Malizia Paragraph 0063).
Peacock does not teach the limitation of “updating the original version of the time series model by [backwards] data selection”. However, Mehta does teach “updating the [original version of the time series] model by backwards data selection” (Mehta Paragraph 0115; “First, an administrator may be prompted to guide the model update (adjustment of coefficients, intercepts and/or regeneration of model with different set of predictor variables, change data cut) (act 1138). Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” Examiner notes that the model is being updated with any variable selection or elimination method.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, and Mehta. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. One of ordinary skill would have motivation to substitute Peacock, Malizia, and Metha to incorporate backwards data selection in place of forward data selection because they both serve the purpose of eliminating one or more insignificant variables “Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” (Mehta Paragraph 0115).
Peacock in view of Malizia in further view of Mehta does not teach computing residuals on the W data subset to determine new patterns of residuals;
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables,
However, Jason does teach computing residuals on the W data subset to determine new patterns of residuals; (Jason Section "Model of residual errors" Paragraph 2; "Just like the input observations themselves, the residual errors from a time series can have temporal structure like trends, bias, and seasonality. Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model." Examiner notes that the residuals are computed on the W data subset to determine new patterns of residuals/temporal structure)
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes; (Jason Section "Model of residual errors" Paragraph 3; "Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model. An ideal model would leave no structure in the residual error, just random fluctuations that cannot be modeled. Structure in the residual error can also be modeled directly. There may be complex signals in the residual error that are difficult to directly incorporate into the model. Instead, you can create a model of the residual error time series and predict the expected error for your model." Examiner notes that based on there being a strong correlation between the new patterns of residuals and a new trend in the new patterns of residuals/any temporal structure in the timer series of residual forecast errors, a model is used to predict/create a variable/expected error to indicate a time location of pattern changes; expected errors that are not 0 means that there is a pattern change)
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables, (Jason Section "Correct predictions with a model of residual errors" Paragraph 1; "A model of forecast residual error is interesting, but it can also be useful to make better predictions. With a good estimate of forecast error at a time step, we can make better predictions. For example, we can add the expected forecast error to a prediction to correct it and in turn improve the skill of the model." Jason Section "Autoregression of residual error" Paragraph 6; "Predict the residual error using the autoregression model.
The autoregression model requires the residual error of the 15 previous time steps. Therefore, we must keep these values handy." Examiner notes that the time series model is updated to an interim time series model by adding new predictors of pattern change/add expected forecast error to model prediction; predicted error is new created outlier indicator and prediction takes account/include residual error of the 15 previous steps/newly created exogenous variables)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, and Jason. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, and Jason to utilize the information shown from the residuals to make a model to predict residual errors, which can be used to correct the forecast model “Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts.” (Jason Paragraph 2).
Peacock in view of Malizia in further view of Mehta in further view of Jason does not teach and evaluating the interim time series model;
comparing accuracy of the interim time series model to a previous interim version of the time series model;
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);]
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;
However, Kouhei does teach and evaluating the interim time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that comparing the accuracy of the new model/interim time series model with the previous model is evaluating the interim time series model)
comparing accuracy of the interim time series model to a previous interim version of the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that the new model/interim time series model is compared to the previous model/previous interim version of the time series model)
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);] (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that the based on the interim time series model being more accurate than the previous interim version/if the accuracy is improved, update/replace and evaluate the interim times series model/additionally evaluated using the estimation error and the abnormality detection rate/another data subset; other data subset can be the next oldest data subset/first phenology period found in Malizia)
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that based on the interim time series model not being more accurate than the previous interim version/if the accuracy is not improved, no additional steps are taken and the interim time series model and only data subsets used to train the time series model becomes the time series model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, and Kouhei. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, and Kouhei to ensure that the current model is the most accurate “When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model.” (Kohei Paragraph 0159).
Regarding Claim 2, Peacock teaches The CIM of claim 1 further comprising:
using the updated version of the time series model to predict values of newly incoming
data in a time series data stream. (Peacock Paragraph 0064; “In the case of Findhorn, 24 predictive models were built, respectively for wind-speed 1 hr, 2 hrs, and so on, up to 24 hours ahead.” Examiner notes the predictive models as updated time series model is built to predict upcoming weather data in 1 hour increments)
Regarding Claim 3, Peacock teaches The CIM of claim 1 further comprising:
prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set. (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models” Examiner notes the original time series model that is being rebuilt is based on historical time data of the weather)
Regarding Claim 4, Peacock teaches The CIM of claim 3 further comprising:
prior to updating the time series model by backwards data selection, evaluating the
original version of the time series model based on the new data set. (Peacock Paragraph 0067; “FIG. 4 shows wind-speed forecasts at Findhorn from 1 hr ahead to 24 hrs ahead, measured on unseen data, comparing the forecasts based on the invention (squares) with forecasts for the same location derived directly from MetOffice forecasts (crosses). From left to right are plotted the 1 hr, 2 hrs . . . up to 24 hrs ahead models, where the vertical axis shows mean absolute error in metres per second. FIG. 5 provides a closer look at how both the forecasts of the invention and the MetOffice forecasts track actual wind-speed, in both the 1 hr ahead and 24 hrs ahead cases. In this case, actual the wind-speed at Findhorn is show as a solid line, compared with the invention forecasts marked by circles and Met-office based forecasts marked by crosses. The plot shows 24 hr ahead forecasts.” Examiner notes MetOffice model is comparing its forecasted wind speed to actual wind speed in Fig. 4)
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Regarding Claim 6, Peacock teaches The CIM of claim 1 further comprising: handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream. (Peacock Paragraph 0070; “The approach is capable of providing forecasts of many meteorological variables at arbitrary locations around the globe.” Peacock Paragraph 0075; “the invention could be used to predict variables that are dependent on the weather, but not usually considered a weather variable. For example, the invention could be applied to pollution and/or pollen count. In this case, at least one observational site for pollution and/or pollen count would have to be included in the plurality of different sources.” Examiner notes the model can handle new data of interest as exogenous variable; The interest/exogenous variable needs a created variable associated in the model to it to be forecasted.)
Regarding Claim 7, Peacock teaches A computer program product (CPP) comprising: a non-transitory tangible set of storage device(s); and computer code stored collectively in the non-transitory tangible set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations: (Peacock Paragraph 0029 “The weather prediction system 10 is a computer-based system that includes at least one computer processor and at least one memory.” Examiner notes that invention is computer implemented.);
receiving an original version of a time series model; (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models.” Peacock Paragraph 0034; “Broadly speaking, site identification is done via access to lists of weather resources that can be accessed on-line from sources such as the MetOffice and forecast.io. Once the sites are selected, weather observation variables and weather forecast variables are repeatedly retrieved and stored over time from the plurality of different weather data sources, as shown in FIG. 2.” Examiner notes the original time series model is the predictive model before it is rebuilt; The MetOffice model has weather forecast and weather observation which are used to build the first iteration of the invention model. MetOffice is also considered an original version of a time series model and is received online);
receiving a historical time series data set including historically observed time series data; (Peacock Paragraph 0029; “Each observations site 12 provides weather variable observations including current values and historical values, typically for the previous 24 hours. Each forecast site 14 provides weather variable forecasts in the hours ahead typically over 24 or 48 hour periods.” Examiner notes receiving data from observation sites; historical values as historical data of historically observed weather conditions);
iteratively updating the original version of the time series model by [backwards] data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data; (Peacock Paragraph 0053; “Once the sites are selected, the feature selection process begins. This uses a process known as forward selection, which is particularly applicable for large feature sets.” Peacock Paragraph 0059; “Whilst forward selection is described here, it will be appreciated that any suitable form of feature selection could be used. Feature selection techniques are well known in the art and so will not be described in detail.” Peacock Paragraph 0061; “As previously indicated, typically 24 specific values are forecast for each variable, covering from 1 to 24 hours ahead inclusive. To meet overall requirements, this means, altogether, 168 feature selection runs leading to 168 specific predictive models.” Peacock Paragraph 0039; "Re-building the models is done by re-doing the feature selection process on a current version of all of the stored data and then rebuilding the models based on the results of that feature selection.” Examiner notes data selection is performed to obtain an interim version of the time series models for each variable; as new data is collected in the current version of all the stored data, the model is iteratively updated by feature selection; current version of all the stored data is data subset that is closest to new data);
Peacock does not teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;
However, Malizia does teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter; (Malizia Paragraph 0070; "With the daily satellite and weather data matched to phenology periods and the subset of counties of interest identified, the process of creating features to be employed in the predictive model is begun." Malizia Paragraph 0009; "In some embodiments, dividing the predetermined time period into the one or more phenology periods comprises: sampling a plurality of pixels of the time series of satellite imagery; determining a time series of vegetation indices based on the sampled pixels; and locating peaks in the time series of vegetation indices." Malizia Paragraph 0115; "For example, in the early season, from the end of June to the beginning of July, the most important features are the year, and the historical average of the NDWI during the second phenology period" Examiner notes based on the number of peaks located/K, that is the input parameter for number of phenology periods/subsets; second phenology period indicates that phenology periods/subsets are divided into latest data subsets to oldest data subsets)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock and Malizia. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. One of ordinary skill would have motivation to combine Peacock and Malizia to break up the historical data into k number of subsets to narrow the focus of the data “The phenology periods themselves were defined via a mathematical optimization process that breaks up the growing season for each crop into segments such that summaries of the predictors across those segments offer the maximum correlation with end of season yields.” (Malizia Paragraph 0063).
Peacock does not teach the limitation of “updating the original version of the time series model by [backwards] data selection”. However, Mehta does teach “updating the [original version of the time series] model by backwards data selection” (Mehta Paragraph 0115; “First, an administrator may be prompted to guide the model update (adjustment of coefficients, intercepts and/or regeneration of model with different set of predictor variables, change data cut) (act 1138). Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” Examiner notes that the model is being updated with any variable selection or elimination method.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, and Mehta. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. One of ordinary skill would have motivation to substitute Peacock, Malizia, and Metha to incorporate backwards data selection in place of forward data selection because they both serve the purpose of eliminating one or more insignificant variables “Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” (Mehta Paragraph 0115).
Peacock in view of Malizia in further view of Mehta does not teach computing residuals on the W data subset to determine new patterns of residuals;
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables,
However, Jason does teach computing residuals on the W data subset to determine new patterns of residuals; (Jason Section "Model of residual errors" Paragraph 2; "Just like the input observations themselves, the residual errors from a time series can have temporal structure like trends, bias, and seasonality. Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model." Examiner notes that the residuals are computed on the W data subset to determine new patterns of residuals/temporal structure)
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes; (Jason Section "Model of residual errors" Paragraph 3; "Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model. An ideal model would leave no structure in the residual error, just random fluctuations that cannot be modeled. Structure in the residual error can also be modeled directly. There may be complex signals in the residual error that are difficult to directly incorporate into the model. Instead, you can create a model of the residual error time series and predict the expected error for your model." Examiner notes that based on there being a strong correlation between the new patterns of residuals and a new trend in the new patterns of residuals/any temporal structure in the timer series of residual forecast errors, a model is used to predict/create a variable/expected error to indicate a time location of pattern changes; expected errors that are not 0 means that there is a pattern change)
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables, (Jason Section "Correct predictions with a model of residual errors" Paragraph 1; "A model of forecast residual error is interesting, but it can also be useful to make better predictions. With a good estimate of forecast error at a time step, we can make better predictions. For example, we can add the expected forecast error to a prediction to correct it and in turn improve the skill of the model." Jason Section "Autoregression of residual error" Paragraph 6; "Predict the residual error using the autoregression model.
The autoregression model requires the residual error of the 15 previous time steps. Therefore, we must keep these values handy." Examiner notes that the time series model is updated to an interim time series model by adding new predictors of pattern change/add expected forecast error to model prediction; predicted error is new created outlier indicator and prediction takes account/include residual error of the 15 previous steps/newly created exogenous variables)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, and Jason. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, and Jason to utilize the information shown from the residuals to make a model to predict residual errors, which can be used to correct the forecast model “Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts.” (Jason Paragraph 2).
Peacock in view of Malizia in further view of Mehta in further view of Jason does not teach and evaluating the interim time series model;
comparing accuracy of the interim time series model to a previous interim version of the time series model;
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);]
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;
However, Kouhei does teach and evaluating the interim time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that comparing the accuracy of the new model/interim time series model with the previous model is evaluating the interim time series model)
comparing accuracy of the interim time series model to a previous interim version of the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that the new model/interim time series model is compared to the previous model/previous interim version of the time series model)
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);] (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that the based on the interim time series model being more accurate than the previous interim version/if the accuracy is improved, update/replace and evaluate the interim times series model/additionally evaluated using the estimation error and the abnormality detection rate/another data subset; other data subset can be the next oldest data subset/first phenology period found in Malizia)
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that based on the interim time series model not being more accurate than the previous interim version/if the accuracy is not improved, no additional steps are taken and the interim time series model and only data subsets used to train the time series model becomes the time series model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, and Kouhei. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, and Kouhei to ensure that the current model is the most accurate “When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model.” (Kohei Paragraph 0159).
Regarding Claim 8, Peacock teaches The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): using the updated version of the time series model to predict values of newly incoming data in a time series data stream. (Peacock Paragraph 0064; “In the case of Findhorn, 24 predictive models were built, respectively for wind-speed 1 hr, 2 hrs, and so on, up to 24 hours ahead.” Examiner notes the predictive models as updated time series model is built to predict upcoming weather data in 1 hour increments)
Regarding Claim 9, Peacock teaches The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set. (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models” Examiner notes the original time series model that is being rebuilt is based on historical time data of the weather)
Regarding Claim 10, Peacock teaches The CPP of claim 9 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, evaluating the original version of the time series model based on the new data set. (Peacock Paragraph 0067; “FIG. 4 shows wind-speed forecasts at Findhorn from 1 hr ahead to 24 hrs ahead, measured on unseen data, comparing the forecasts based on the invention (squares) with forecasts for the same location derived directly from MetOffice forecasts (crosses). From left to right are plotted the 1 hr, 2 hrs . . . up to 24 hrs ahead models, where the vertical axis shows mean absolute error in metres per second. FIG. 5 provides a closer look at how both the forecasts of the invention and the MetOffice forecasts track actual wind-speed, in both the 1 hr ahead and 24 hrs ahead cases. In this case, actual the wind-speed at Findhorn is show as a solid line, compared with the invention forecasts marked by circles and Met-office based forecasts marked by crosses. The plot shows 24 hr ahead forecasts.” Examiner notes MetOffice model is comparing its forecasted wind speed to actual wind speed in Fig. 4)
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Regarding Claim 12, Peacock teaches The CPP of claim 7 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream. (Peacock Paragraph 0070; “The approach is capable of providing forecasts of many meteorological variables at arbitrary locations around the globe.” Peacock Paragraph 0075; “the invention could be used to predict variables that are dependent on the weather, but not usually considered a weather variable. For example, the invention could be applied to pollution and/or pollen count. In this case, at least one observational site for pollution and/or pollen count would have to be included in the plurality of different sources.” Examiner notes the model can handle new data of interest as exogenous variable; The interest/exogenous variable needs a created variable associated in the model to it to be forecasted.)
Regarding Claim 13, Peacock teaches A computer system (CS) comprising: a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations: (Peacock Paragraph 0029 “The weather prediction system 10 is a computer-based system that includes at least one computer processor and at least one memory.” Examiner notes that invention is computer implemented.);
receiving an original version of a time series model; (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models.” Peacock Paragraph 0034; “Broadly speaking, site identification is done via access to lists of weather resources that can be accessed on-line from sources such as the MetOffice and forecast.io. Once the sites are selected, weather observation variables and weather forecast variables are repeatedly retrieved and stored over time from the plurality of different weather data sources, as shown in FIG. 2.” Examiner notes the original time series model is the predictive model before it is rebuilt; The MetOffice model has weather forecast and weather observation which are used to build the first iteration of the invention model. MetOffice is also considered an original version of a time series model and is received online);
receiving a historical time series data set including historically observed time series data; (Peacock Paragraph 0029; “Each observations site 12 provides weather variable observations including current values and historical values, typically for the previous 24 hours. Each forecast site 14 provides weather variable forecasts in the hours ahead typically over 24 or 48 hour periods.” Examiner notes receiving data from observation sites; historical values as historical data of historically observed weather conditions);
iteratively updating the original version of the time series model by [backwards] data selection to obtain an interim version of the time series model using a data subset (W) that is closest to new data; (Peacock Paragraph 0053; “Once the sites are selected, the feature selection process begins. This uses a process known as forward selection, which is particularly applicable for large feature sets.” Peacock Paragraph 0059; “Whilst forward selection is described here, it will be appreciated that any suitable form of feature selection could be used. Feature selection techniques are well known in the art and so will not be described in detail.” Peacock Paragraph 0061; “As previously indicated, typically 24 specific values are forecast for each variable, covering from 1 to 24 hours ahead inclusive. To meet overall requirements, this means, altogether, 168 feature selection runs leading to 168 specific predictive models.” Peacock Paragraph 0039; "Re-building the models is done by re-doing the feature selection process on a current version of all of the stored data and then rebuilding the models based on the results of that feature selection.” Examiner notes data selection is performed to obtain an interim version of the time series models for each variable; as new data is collected in the current version of all the stored data, the model is iteratively updated by feature selection; current version of all the stored data is data subset that is closest to new data);
Peacock does not teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter;
However, Malizia does teach splitting data of the historical time series data set into k number of subsets from a latest data subset to an oldest data subset, wherein k is an input parameter; (Malizia Paragraph 0070; "With the daily satellite and weather data matched to phenology periods and the subset of counties of interest identified, the process of creating features to be employed in the predictive model is begun." Malizia Paragraph 0009; "In some embodiments, dividing the predetermined time period into the one or more phenology periods comprises: sampling a plurality of pixels of the time series of satellite imagery; determining a time series of vegetation indices based on the sampled pixels; and locating peaks in the time series of vegetation indices." Malizia Paragraph 0115; "For example, in the early season, from the end of June to the beginning of July, the most important features are the year, and the historical average of the NDWI during the second phenology period" Examiner notes based on the number of peaks located/K, that is the input parameter for number of phenology periods/subsets; second phenology period indicates that phenology periods/subsets are divided into latest data subsets to oldest data subsets)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock and Malizia. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. One of ordinary skill would have motivation to combine Peacock and Malizia to break up the historical data into k number of subsets to narrow the focus of the data “The phenology periods themselves were defined via a mathematical optimization process that breaks up the growing season for each crop into segments such that summaries of the predictors across those segments offer the maximum correlation with end of season yields.” (Malizia Paragraph 0063).
Peacock does not teach the limitation of “updating the original version of the time series model by [backwards] data selection”. However, Mehta does teach “updating the [original version of the time series] model by backwards data selection” (Mehta Paragraph 0115; “First, an administrator may be prompted to guide the model update (adjustment of coefficients, intercepts and/or regeneration of model with different set of predictor variables, change data cut) (act 1138). Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” Examiner notes that the model is being updated with any variable selection or elimination method.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, and Mehta. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. One of ordinary skill would have motivation to substitute Peacock, Malizia, and Metha to incorporate backwards data selection in place of forward data selection because they both serve the purpose of eliminating one or more insignificant variables “Any known method for variable selection or elimination may be utilized such as forward selection, backwards elimination and stepwise regression, etc.” (Mehta Paragraph 0115).
Peacock in view of Malizia in further view of Mehta does not teach computing residuals on the W data subset to determine new patterns of residuals;
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes;
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables,
However, Jason does teach computing residuals on the W data subset to determine new patterns of residuals; (Jason Section "Model of residual errors" Paragraph 2; "Just like the input observations themselves, the residual errors from a time series can have temporal structure like trends, bias, and seasonality. Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model." Examiner notes that the residuals are computed on the W data subset to determine new patterns of residuals/temporal structure)
based on there being a strong correlation between the new patterns of residuals and a new exogenous variable, a new trend in the new patterns of residuals, or outlier indicators of a shift in the data subset, creating variables to indicate a time location of pattern changes; (Jason Section "Model of residual errors" Paragraph 3; "Any temporal structure in the time series of residual forecast errors is useful as a diagnostic as it suggests information that could be incorporated into the predictive model. An ideal model would leave no structure in the residual error, just random fluctuations that cannot be modeled. Structure in the residual error can also be modeled directly. There may be complex signals in the residual error that are difficult to directly incorporate into the model. Instead, you can create a model of the residual error time series and predict the expected error for your model." Examiner notes that based on there being a strong correlation between the new patterns of residuals and a new trend in the new patterns of residuals/any temporal structure in the timer series of residual forecast errors, a model is used to predict/create a variable/expected error to indicate a time location of pattern changes; expected errors that are not 0 means that there is a pattern change)
updating the time series model to an interim time series model by adding new predictors of pattern change, wherein the new predictors include newly created outlier indicators and newly created exogenous variables, (Jason Section "Correct predictions with a model of residual errors" Paragraph 1; "A model of forecast residual error is interesting, but it can also be useful to make better predictions. With a good estimate of forecast error at a time step, we can make better predictions. For example, we can add the expected forecast error to a prediction to correct it and in turn improve the skill of the model." Jason Section "Autoregression of residual error" Paragraph 6; "Predict the residual error using the autoregression model.
The autoregression model requires the residual error of the 15 previous time steps. Therefore, we must keep these values handy." Examiner notes that the time series model is updated to an interim time series model by adding new predictors of pattern change/add expected forecast error to model prediction; predicted error is new created outlier indicator and prediction takes account/include residual error of the 15 previous steps/newly created exogenous variables)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, and Jason. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, and Jason to utilize the information shown from the residuals to make a model to predict residual errors, which can be used to correct the forecast model “Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts.” (Jason Paragraph 2).
Peacock in view of Malizia in further view of Mehta in further view of Jason does not teach and evaluating the interim time series model;
comparing accuracy of the interim time series model to a previous interim version of the time series model;
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);]
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model;
However, Kouhei does teach and evaluating the interim time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that comparing the accuracy of the new model/interim time series model with the previous model is evaluating the interim time series model)
comparing accuracy of the interim time series model to a previous interim version of the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved" Examiner notes that the new model/interim time series model is compared to the previous model/previous interim version of the time series model)
based on the interim time series model being more accurate than the previous interim version of the time series model, updating and evaluating the interim time series model using another data subset, [wherein the other dataset is a next oldest data subset (W-1);] (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that the based on the interim time series model being more accurate than the previous interim version/if the accuracy is improved, update/replace and evaluate the interim times series model/additionally evaluated using the estimation error and the abnormality detection rate/another data subset; other data subset can be the next oldest data subset/first phenology period found in Malizia)
and based on the interim time series model not being more accurate than the previous interim version of the time series model, the interim time series model and only data subsets used to train the interim time series model are the time series model; (Kouhei Paragraph 0159; "When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model…Alternatively, the accuracy of an updated diagnosis model may be evaluated by using an estimated error and an anomaly detection rate to check whether the accuracy of the updated model is improved as compared to that of the original diagnosis model." Examiner notes that based on the interim time series model not being more accurate than the previous interim version/if the accuracy is not improved, no additional steps are taken and the interim time series model and only data subsets used to train the time series model becomes the time series model)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, and Kouhei. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, and Kouhei to ensure that the current model is the most accurate “When the accuracy of a new model is compared with that of the previous model to find that the accuracy is improved, the existing model is replaced with the new model.” (Kohei Paragraph 0159).
Regarding Claim 14, Peacock teaches The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): using the updated version of the time series model to predict values of newly incoming data in a time series data stream. (Peacock Paragraph 0064; “In the case of Findhorn, 24 predictive models were built, respectively for wind-speed 1 hr, 2 hrs, and so on, up to 24 hours ahead.” Examiner notes the predictive models as updated time series model is built to predict upcoming weather data in 1 hour increments)
Regarding Claim 15, Peacock teaches The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, building the original version of the time series model based on the historical time series data set. (Peacock Paragraph 0030; “By continuously capturing and storing forecast-based and observation based inputs and using these to continuously rebuild predictive models” Examiner notes the original time series model that is being rebuilt is based on historical time data of the weather)
Regarding Claim 16, Peacock teaches The CS of claim 15 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, evaluating the original version of the time series model based on the new data set. (Peacock Paragraph 0067; “FIG. 4 shows wind-speed forecasts at Findhorn from 1 hr ahead to 24 hrs ahead, measured on unseen data, comparing the forecasts based on the invention (squares) with forecasts for the same location derived directly from MetOffice forecasts (crosses). From left to right are plotted the 1 hr, 2 hrs . . . up to 24 hrs ahead models, where the vertical axis shows mean absolute error in metres per second. FIG. 5 provides a closer look at how both the forecasts of the invention and the MetOffice forecasts track actual wind-speed, in both the 1 hr ahead and 24 hrs ahead cases. In this case, actual the wind-speed at Findhorn is show as a solid line, compared with the invention forecasts marked by circles and Met-office based forecasts marked by crosses. The plot shows 24 hr ahead forecasts.” Examiner notes MetOffice model is comparing its forecasted wind speed to actual wind speed in Fig. 4)
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Regarding Claim 18, Peacock teaches The CS of claim 13 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): handling, by the updated version of the time series model, a new exogenous variable and a created variable in newly received data of a time series data stream. (Peacock Paragraph 0070; “The approach is capable of providing forecasts of many meteorological variables at arbitrary locations around the globe.” Peacock Paragraph 0075; “the invention could be used to predict variables that are dependent on the weather, but not usually considered a weather variable. For example, the invention could be applied to pollution and/or pollen count. In this case, at least one observational site for pollution and/or pollen count would have to be included in the plurality of different sources.” Examiner notes the model can handle new data of interest as exogenous variable; The interest/exogenous variable needs a created variable associated in the model to it to be forecasted.)
Claim(s) 5, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over PEACOCK; Andrew Duncan et al; US 20170131435 A1 (hereinafter “Peacock”) in view of Malizia Nicholas et al; WO 2021007352 A1; (Hereinafter “Malizia”) in further view of MEHTA; Anil et al; US 20170161614 A1 (hereinafter “Mehta”) in further view of Jason Brownlee; “How to Model Residual Errors to Correct Time Series Forecasts with Python” (hereinafter “Jason”) in further view of Kohei Maruchi et al; US 20190012553 A1 (hereinafter “Kohei”) in further view of “IBM SPSS Forecasting” [2012] (hereinafter “IBM”).
Regarding Claim 5, Peacock fails to teach The CIM of claim 4 further comprising: prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model.
However, IBM teaches The CIM of claim 4 further comprising: prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model. (IBM Page 6 Paragraph 2; “TSAPPLY Apply saved models to new or updated data: • Simultaneously apply models from multiple XML files created with TSMODEL • Re-estimate model parameters and goodness-of-fit measures from the data, or load them from the saved model file • Selectively choose saved models to apply • Override the periodicity (seasonality) of the active dataset • Choose from the same output, fit measure, statistics, and options as TSMODEL • Export re-estimated models to an XML file. Examiner notes that TSAPPLY feature allows the user to update the model with new data.” Examiner notes that TSAPPLY feature allows the user to update the model with new data.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, Kouhei and IBM. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. IBM teaches receiving a user request to update the original version of the time series model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, Kouhei, and IBM to allow the user to efficiently generate updated models “Efficiently generate and update models Instead of laboriously building forecasts by re-setting parameters and re-estimating models, variable by variable, you can speed through the process with SPSS Forecasting.” (See IBM Page 3 Paragraph 1)
Regarding Claim 11, Peacock fails to teach the The CPP of claim 10 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model.However, IBM teaches The CPP of claim 10 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model. (IBM Page 6 Paragraph 2; “TSAPPLY Apply saved models to new or updated data: • Simultaneously apply models from multiple XML files created with TSMODEL • Re-estimate model parameters and goodness-of-fit measures from the data, or load them from the saved model file • Selectively choose saved models to apply • Override the periodicity (seasonality) of the active dataset • Choose from the same output, fit measure, statistics, and options as TSMODEL • Export re-estimated models to an XML file. Examiner notes that TSAPPLY feature allows the user to update the model with new data.” Examiner notes that TSAPPLY feature allows the user to update the model with new data.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, Kouhei and IBM. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. IBM teaches receiving a user request to update the original version of the time series model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, Kouhei, and IBM to allow the user to efficiently generate updated models “Efficiently generate and update models Instead of laboriously building forecasts by re-setting parameters and re-estimating models, variable by variable, you can speed through the process with SPSS Forecasting.” (See IBM Page 3 Paragraph 1)
Regarding Claim 17, Peacock fails to teach The CS of claim 16 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model.
However, IBM teaches The CS of claim 16 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): prior to updating the time series model by backwards data selection, receiving a user request to update the original version of the time series model. (IBM Page 6 Paragraph 2; “TSAPPLY Apply saved models to new or updated data: • Simultaneously apply models from multiple XML files created with TSMODEL • Re-estimate model parameters and goodness-of-fit measures from the data, or load them from the saved model file • Selectively choose saved models to apply • Override the periodicity (seasonality) of the active dataset • Choose from the same output, fit measure, statistics, and options as TSMODEL • Export re-estimated models to an XML file. Examiner notes that TSAPPLY feature allows the user to update the model with new data.” Examiner notes that TSAPPLY feature allows the user to update the model with new data.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Peacock, Malizia, Mehta, Jason, Kouhei and IBM. Peacock teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through a data selection method. Malizia teaches a crop yield forecasting model from satellite imagery covers during predetermined time period. Mehta teaches a predictive model that uses historical time data to make predictions of variables of interest and updates the model based on new data provided that have been through backwards data selection. Jason teaches how to utilize residual errors to improve predictions of a model. Kouhei teaches model selection between a current model and previous model based on their accuracy. IBM teaches receiving a user request to update the original version of the time series model. One of ordinary skill would have motivation to combine Peacock, Malizia, Metha, Jason, Kouhei, and IBM to allow the user to efficiently generate updated models “Efficiently generate and update models Instead of laboriously building forecasts by re-setting parameters and re-estimating models, variable by variable, you can speed through the process with SPSS Forecasting.” (See IBM Page 3 Paragraph 1)
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.
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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