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 .
THIS ACTION IS MADE FINAL.
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 extension fee 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.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 06/05/2026, Applicant, on 08/26/2026.
Status of Claims
Claims 1, 6, 10, and 18 are currently amended.
Claims 2-5, 8, and 11-16 are canceled.
Claims 1, 6-7, 9-10, and 17-18 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered and found not to be persuasive.
The examiner respectfully disagrees.
The Examiner submits that the present claim as amended recites mathematical concepts: aggregating redeemed reward points into time-series data, using an FFT to detect seasonality, performing seasonality decomposition, using moving-average and autoregressive components, using SARIMA, supplying exogenous and seasonality variables, training a prediction model, predicting future reward liability, and comparing predicted liability against upper and lower thresholds.
In addition, forecasting reward-program liabilities and modifying reward rules based on budget thresholds can be viewed as certain method of organizing human activity.
Further, accessing historical reward data, aggregating the data, identifying yearly/weekly seasonality, FFT, seasonality decomposition, SARIMA, training the model, and predicting future reward liability essentially constitute the mathematical/data-analysis process itself, in addition to reciting “by a server system” it doesn't meaningfully limit the mathematical analysis to a technological implementation. Generic computer implementation generally isn't enough.
The claimed limitation, “automatically modifying, by the server system, at least one reward rule associated with one or more of the plurality of reward programs based, at least in part, on a comparison of the predicted future reward liability data against one or more reward liability criteria comprising at least an upper reward liability budget threshold and a lower reward liability budget threshold” indicates that the thing being modified is a “reward rule” That's fundamentally a rule governing a commercial rewards program, rather than operation of the computer itself or another technological system. The claim doesn't specify, for example, modifying processor operation, network traffic, storage architecture, a physical machine, or another technological process. So, the Examiner asserts that the final step is simply using mathematical prediction to make/implement a business decision.
As a result, the additional elements do not integrate the exception into a practical application,
The Examiner submits also that under Step 2B, the server itself appears generic, and the ultimate result is still the administration of a reward program with no inventive concept identified.
Applicant’s arguments on controls the reward-program system by automatically changing its operating rules (pages 9-11) does not really specify how technically the automatic modification is implemented. Other arguments provided by Applicant (solves the concrete problem of how to capture multiple seasonality components of differing periods within a single SARIMA-based prediction architecture, page 9, specific portioning of input, page 10) are not convincing in view of the Examiner’ eligibility evaluation of the claims under 35 USC § 101.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
Response to Arguments - 35 USC § 103
The arguments have been fully considered and found to be persuasive.
None of the cited documents by the Examiner, taken individually or in combination, discloses or suggests the features in the independent claims as amended, nor could a person skilled in the art easily conceive of such features even in the light of common technical knowledge at the time of filing. The pending claims 1, 6-7, 9-10, and 17-18 are therefore distinguished from the prior arts cited by the Examiner.
In conclusion, the Examiner withdraws the rejections of the pending claims under 35 USC § 103 in the present office action.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 6-7, 9-10, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1, 6-7, 9-10, and 17-18 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1, 6-7, 9-10, and 17-18 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of predicting reward liability using machine learning.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method, comprising: accessing historical reward related data associated with a plurality of reward programs administered by a reward program provider of a plurality of reward program providers, the historical reward related data representative of past redeemed reward points by a plurality of cardholders, on a daily basis, over a period of years; aggregating the past redeemed reward points, for each reward program, on at least a daily basis into a time-series dataset, wherein the aggregated past redeemed reward points are aggregated irrespective of when the reward points were earned; identifying based on a fast-Fourier transform (FFT), a first seasonality pattern, which is a yearly seasonality pattern, and a second seasonality pattern, which is a weekly seasonality pattern, included in the time-series dataset; wherein identifying the first seasonality pattern includes: detecting seasonality trends within the historical reward related data of each reward program provider based on the FFT; and identifying the first seasonality pattern within the historical reward related data based on a seasonality decomposition of the seasonality trends; wherein identifying the second seasonality pattern within the time-series dataset is based on seasonal lags in moving average and auto-regressive components of a seasonal auto-regressive integrated moving average (SARIMA) time-series model; and training, by the server system, a reward liability prediction model, which includes the SARIMA time-series model, based, at least in part, on first and second seasonality patterns and a correlated variable by: inputting the first seasonality pattern and aggregated earned reward points for the plurality of reward programs as exogenous variables into the SARIMA time- series model, and inputting the second seasonality pattern as a seasonality variable into the SARIMA time-series model, wherein the trained reward liability prediction model is configured to predict future reward liability data associated with the plurality of reward programs, the correlated variable including aggregate earned reward points for the plurality of reward programs; predicting using the trained reward liability prediction model, the future reward liability data associated with the plurality of reward programs; and modifying at least one reward rule associated with one or more of the plurality of reward programs based, at least in part, on a comparison of the predicted future reward liability data against one or more reward liability criteria comprising at least an upper reward liability budget threshold and a lower reward liability budget threshold”
The limitations above recite an abstract idea under Step 2A Prong One. Moreparticularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for predicting reward liability using machine learning. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 10 and 18 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 10 and 18 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 6-7, 9, and 17recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for predicting reward liability using machine learning. As a result, claims 6-7, 9, and 17 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “computer-implemented”, “by a server system”, and “automatically”. When considered in view of the claim as a whole, the steps of “accessing and aggregating” do not integrate the abstract idea into a practical application because “accessing and aggregating” are insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 10 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 10 further recites “A server system” and claim 18 further recites “A non-transitory computer-readable storage medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 10 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 6-7, 9, and 17 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 6-7, 9, and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “computer-implemented”, “by a server system”, and “automatically”. The steps of “accessing and aggregating” do not amount to significantly more than the abstract idea because “accessing and aggregating” are well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 10 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 10 further recites “A server system” and claim 18 further recites “A non-transitory computer-readable storage medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 10 and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 6-7, 9, and 17 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 6-7, 9, and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1, 6-7, 9-10, and 17-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure, Kulkarni et al. (US 20110196718 A1). Kulkarni teaches identifying a demand distribution for a print product resource consumable in a print production environment, identifying a first seasonal period in the demand distribution, creating a seasonally adjusted demand distribution by removing a first seasonal component associated with the first seasonal period the demand distribution, identifying a second seasonal period in the seasonally adjusted demand distribution, creating an updated seasonally adjusted demand distribution by removing a second seasonal component associated with the second seasonal period from the seasonally adjusted demand distribution, using a forecasting model to automatically forecast a predicted future demand value for the consumable based on the updated seasonally adjusted demand distribution, updating the predicted future demand value using one or more of the first seasonal component and the second seasonal component, determining whether additional inventory is needed based on at least the updated predicted future demand value, and in response to a need for additional inventory, generating an order for the print product resource consumable.
Applicant's amendments and arguments dated 02/21/2022 necessitated the updating of the 35 USC § 101 and the 35 USC § 103 rejections of the pending claims presented in the present 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).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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