DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 18, 2026 has been entered.
In response to the application filed on September 4, 2026, claims 1-2, 4-9, 11-16, 18-21 are now pending for examination in the application.
Response to Arguments
Applicant’s arguments:
In regards to claim 1 on Page(s) 11, applicant argues “Applicant submits that the claimed limitations cannot practically be performed in the human mind and are therefore, not directed to an abstract idea. Specifically, forecasting systems cannot be performed in the human mind.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. If a claim limitation, under its broadest reasonable interpretation, covers a mental process (e.g. forecasting and pattern recognition)), then it falls within the “Mental process” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea. The examiner notes that the computer as recited in the claims are being used for pre-processing time series data (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Applicant’s arguments:
In regards to claim 1 on Page(s) 16, applicant argues “Accordingly, the claim as a whole is directed to an improvement to existing computer technology or to the technology of season identification in a time series, and the claim integrates the abstract idea into a practical application, and is therefore not "directed to" the judicial exception.”
Examiner’s Reply:
The pre-processing of seasonal data is a step that is done with a human using a computer as a tool Regardless of the execution, Identifying a variety of seasons using mathematical techniques (whether or not data is preprocessed) does not improve the functioning of a computing system.
Applicant’s arguments:
In regards to claim 1 on Page(s) 11, applicant argues “However, for purposes of completeness, Step 2B asks the Examiner to "[d]etermine whether any element, or combination of elements, in the claim is sufficient to ensure that the claim amounts to significantly more than the judicial exception." (Id. at 74624, col. 1).”
Examiner’s Reply:
Forecasting is well-understood, routine, and conventional. There are several forecasting system that use seasonal information in understanding customer’s behavior such as Amazon’s models, etc. The additional pre-processing step does not make the claim patent eligible.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-2, 4-9, 11-16,18-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1-2, 4-9, 11-16,18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the devices and methods of claims 1-2, 4-9, 11-16,18-21 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mathematical concepts and mental processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 8, and 15 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim 1, 8, and 15 recites the following limitations directed towards a Mental Processes:
pre-processing, by a pre-processing unit, the time series, wherein the pre-processing comprises cleaning the time series and converting the time series into a matrix for input to a Fourier unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to cleaning and converting time series data);
causing, by the computing device, the autocorrelation unit to identify a first plurality of seasons based at least in part on processing the time series using the autocorrelation technique, wherein a season comprises an identifiable and repeating pattern in the time series, wherein the first plurality of seasons have a first time length with common factors (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for identifying a season);
causing, by the computing device and in parallel with identifying the first plurality of seasons, the Fourier unit to identify a second plurality of seasons in a frequency-domain based at least in part on processing the time series using the Fourier technique, wherein a season comprises an identifiable and repeating pattern in the time series, wherein the first plurality of seasons have a first time length with common factors (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for identifying a season);
generating, by the computing device, a third plurality of seasons in the time- domain based at least in part on the second plurality of seasons, wherein the first plurality of seasons identified using the autocorrelation technique and the third plurality of seasons are grouped to form a fourth plurality of seasons in the time domain (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for forecast modeling);
identifying, by the computing device, a first season and a second season based at least in part on the fourth plurality of seasons, wherein a length of the first season is a factor of a length of the second season; estimating, by the computing device, a Fourier order for the first season based at least in part on the length of the first season and the length of the second season (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for forecast modeling);
estimating, by the computing device, a seasonality mode of the first season based at least in part on the length of the first season and the length of the second season (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for forecast modeling);
selecting, by the computing device, a forecasting model to forecast a value of a future time step of the time series based at least in part on the Fourier order and the seasonality mode (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for forecast modeling); and
forecasting, by the computing device and using the selected forecasting model, the value of the future time step of the time series (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for forecast modeling).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 8, and 15:
receiving, by a computing device, a time series comprising a plurality of data points, wherein each data point of the time series comprises a time associated with the data point and a value (recites insignificant extra solution activity that amounts to mere data gathering);
providing, by the computing device, the time series to an autocorrelation unit and a Fourier unit, the autocorrelation unit for using an autocorrelation technique, and the Fourier unit for using a Fourier technique (recites insignificant extra solution activity that amounts to providing time series data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 8, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 9, and 16:
Step 2A, prong one of the 2019 PEG:
transforming the second plurality of seasons from the frequency-domain to the time-domain (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by forecast modeling).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4, 11, 18:
Step 2A, prong one of the 2019 PEG:
analyzing the time series using a mean squared error (MSE) regression analysis; identifying the first season based at least in part on the MSE regression analysis (The limitation recites a mental process of observation, evaluation capable of being performed by the human mind by forecast modeling).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5, 12, 19:
wherein estimating the Fourier order comprises dividing the length of the identified second season by the length of the identified first season to obtain a quotient, wherein the estimated Fourier order is based at least in part on the quotient (The limitation recites a mathematical concept; estimating).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 13, 20:
determining a trend of the time series; and estimating the seasonality mode based at least in part on the trend (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by forecast modeling).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 14:
wherein the seasonality mode comprises an additive seasonality or a multiplicative seasonality (The limitation recites a mental process of observation, evaluation capable of being performed by the human mind by forecast modeling).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 21:
method according to claim 1, wherein the Fourier order comprises a value indicative of a frequency or strength the first season, wherein a higher Fourier order correlates to a higher frequency and strength between the first season and the time series (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by detecting seasonal patterns for identifying a season).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of forecast modeling.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
The claims are not rejected over prior art
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-Pub. No. 20200125988 is directed Methods and Systems for a Database: [0038] machine-learning of variable seasonal patterns is providing a solution that is highly scalable. One approach is to train an explicit model, such as a Holt-Winters model, for each specific type of season. For example, one model may be trained for monthly seasons, another for quarterly seasons, another for bi-annual seasons, etc. The seasonal factors for each model may then be analyzed to determine if there is a season of the specific type associated with the model. This approach would involve a significant amount of processing overhead, especially where a large number of different types of seasons may be present. Dozens or more models may need to be trained, even in cases where only one season is present, which is both computationally expensive and inefficient. In addition, the Holt-Winters models are not tailored to detecting irregularity even for a specific type of season or to detect overlapping seasons, such as long-term seasons overlaid on weekly seasons. As a result, training separate Holt-Winters models for each specific type of season is not highly accurate or scalable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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