CTNF 18/771,593 CTNF 93613 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013 is being examined under the first inventor to file provisions of the AIA. Status of the Application 07-42-04 AIA 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/09/2026 has been entered. 12-151 AIA 26-51 12-51 Status of Claims Claims 1, 3-6, 10, 12-14, and 18 are currently amended. Claims 8 and 16 are canceled. Claims 1-7, 9-15, 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. With respect to the use of machine learning techniques (Applicant’s arguments pages 8-9), it is common practice that such computational models/techniques and algorithms are per se of an abstract mathematical nature, irrespective of whether they can be “trained” based on training data. Hence, a mathematical method may contribute to the technical character of an invention, if it serves as technical purpose or if it regards as specific technical implementation motivated by the internal function of a computer. Elements in the present claims do not solve a technical problem, but an administrative/business method, i.e. predicting reward liability using machine learning. Since the mathematical algorithms or models used in the present application do not serve a technical purpose, but a business purpose, and their implementation does not go beyond generic technical implementation, the use of the artificial intelligence techniques, do not contribute to a technical character and they are to be part of the abstract idea. Claims can recite an abstract idea even if they are claimed as being performed on a computer (Applicant’s arguments pages 7-8). The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer’). Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory in Content Extraction is according to the court an abstract idea that is similar to other concepts that have been identified as abstract by the courts. Present claim 1 is collecting and analyzing data using a generic server system. Therefore, it is reasonable to conclude based on the similarity of the idea described in this claim to several abstract ideas found by the courts that claim 1 is directed to an abstract idea. The present claims mirror cases like Billing v. United States or FairWarning IP v. latric Systems where the court ruled that collecting, analyzing, and displaying data for risk or compliance purposes is an abstract idea, regardless of how complex the scoring algorithm is. Further, simply invoking “machine learning” to predict reward liability is often treated as a black box by the court unless the claims specify a technical improvement to the ML architecture itself. This concept is viewed as a tool used to automate a manual concept. Further, the additional elements in the claims (by a server system) do not improve any existing technology. As a result, the additional elements do not integrate the abstract idea into a practical application, Step 2A Prong Two. Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step,’ rendering the claim eligible at Step 2B ” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354. As discussed above, there is no evidence in the record that the steps of predicting reward liability using machine learning is accomplished in a non-conventional way. The Examiner therefore concludes that the claims used generic, conventional, technology to implement the abstract idea of predicting reward liability and that there is no inventive concept in the present claims. 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 not to be persuasive. The examiner respectfully disagrees. With respect to Applicant’s arguments in page 12, at least in para. 005, Chang teaches “A set of loyalty behavior models may be developed for an individual member of the loyalty program based on the historical data. For each campaign in a plurality of marketing campaigns, at least one combination of offers may be inserted into each loyalty behavior model to output a plurality of net profit scores for the individual member, wherein each combination of offers outputs a separate net profit score” which indicates that Chang is training a model based on historical data. Applicant’s arguments regarding the daily basis aggregation of data are moot in view of the reference Fredergill, para. 0051 (please see 103 rejections below). The Examiner submits that the present claims as amended are simply using a machine learning model to predict reward liability based on historical data and using FFT to identify seasonality (performed by a server). The present claims are broad and do not provide any inventive concept by aggregating data on a daily basis and the type of variables used. As a result, the Examiner maintains the rejections of the pending claims under 35 USC § 103 in the present office action. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 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 1-7, 9-15, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-7, 9-15, 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 1-7, 9-15, 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; identifying, by the server system, based on a fast-Fourier transform (FFT), a first seasonality pattern, which is a yearly seasonality pattern, and a second seasonality pattern included in the aggregated historical reward related data; training a reward liability prediction model based, at least in part, on first and second seasonality patterns and a correlated variable, 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 the predicted future reward liability data and one or more reward liability criteria” The limitations above recite an abstract idea under Step 2A Prong One. More particularly, 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 2-7, 9, 11-15, and 17 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, claims 2-7, 9, 11-15, 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”. 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 2-7, 9, 11-15, and 17 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 2-7, 9, 11-15, 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”. 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 2-7, 9, 11-15, and 17 do not include any additional elements beyond those recited by independent claims 1, 10, and 18. As a result, claims 2-7, 9, 11-15, 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 1-7, 9-15, and 17-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or non-obviousness. 07-06 AIA 15-10-15 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 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. 07-21-aia AIA Claim s 1-7, 9-15, and 17-18 are rejected under 35 U.S.C. 103 as being un-patentable over Chang et al. (US 20090254413 A1) in view of Chidlovskii et al. (US 20150088790 A1) and in further view of Fredergill et al. (US20050144074A1) . Regarding claim 1 . Chang teaches A computer-implemented method, comprising: [Chang, Figure 2] accessing, by a server system, historical reward related data associated with a plurality of reward programs administered by a reward program provider of a plurality of reward program providers, [Chang, claim 7, Chang teaches “gathering historical data related to multiple customer loyalty program members”] 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, by the server system, the past redeemed reward points, for each reward program, on at least a daily basis; [Chang, claim 7, Chang teaches “gathering historical data related to multiple customer loyalty program members.” Further, Chang teaches in para. 0040 “The loyalty program enrollment data may include, without limitation, program enrollment date, program enrollment cancellation date, program enrollment fee, type of reward tier enrolled and associated date, type of reward tier switched and associated date. Reward related data may include, without limitation: number of rewards points earned, number of rewards points redeemed” wherein the historical reward (loyalty program) include reward data. Chang further teaches in para. 0040 campaign related data gathering which is equivalent to reward programs. See also figure 4a-4b for a period of years 2004, 2005…] Chang does not specifically teach, however; Fredergill teaches on a daily basis [Fredergill, para. 0051 teaches “The retailer host system 30 also performs end-of-day processing which extracts all customer activity from each store by retrieving the transaction log files from each in-store system controller 14 and service desk 16 at each retailer location” wherein aggregating on daily basis] It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang model to include Fredergill daily basis aggregation, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Chang in view of Fredergill does not specifically teach, however; Chidlovskii teaches identifying, by the server system, based on a fast-Fourier transform (FFT), a first seasonality pattern, which is a yearly seasonality pattern, and a second seasonality pattern included in the aggregated historical reward related data; [Chidlovskii, para. 0004, Chidlovskii teaches “In some embodiments the method further comprises generating a Fourier model of the history of the demand, for example by computing Fourier components for different periods including at least two of (1) one day, (2) one week, and (3) one year, or by computing a Fourier transform of the history of the demand, and the estimating comprises estimating demand for the resource at the prediction time by evaluating the Fourier model at the prediction time” wherein Fourier transform for different periods (seasonality)] It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to have modified the teaching of Chang in view of Fredergill to incorporate the teaching of Chidlovskii by using Fourier transform to identify pattern in different periods. The motivation to combine Chang with Chidlovskii has the advantage of better prediction results for time series (Chang, para. 0030). Further, Chang teaches training, by the server system, a reward liability prediction model based, at least in part, on first and second seasonality patterns and a correlated variable, wherein the trained reward liability prediction model is configured to predict future reward liability data associated with the plurality of reward programs, [Chang, Abstract, Chang teaches “A set of loyalty behavior models is developed for an individual member of the loyalty program is developed based on the historical data.” Further, Chang teaches in para. 0031 “One of the specific types of models listed among the examples above is a redemption model. Such a model may suggest, for example, that a particular customer is likely to redeem points from a loyalty rewards program during a next six month period. Certain redemptions are more expensive (e.g., airline tickets) than others (e.g., retail merchandise). Thus, based on model redemption predictions (ie. liability to redeem more expensive redemptions), it may be advantageous to target such customers near the beginning of that six month period with a cross-redemption campaign encouraging the members to use their reward points to purchase less expensive rewards, such as retail merchandise” wherein predict future reward liability by training reward liability prediction model based on variables] the correlated variable including aggregate earned reward points for the plurality of reward programs; [Chang, para 0040, Chang teaches “Campaign related data may include, without limitation: campaign enrollment and/or response indicator, type of promotion offer, cell information, and specific campaign performance data for each individual consumer member in this specific marketing campaign. Results from a prior campaign may include, without limitation: data related to responses of multiple customers to at least one specific offer, campaign enrollment fee data, duration data, campaign enrollment date data, a response indicator, response channel data, redemption pricing data, reward points offer data, threshold data, and cap data” wherein reward points offer data is equivalent to earned reward points variables] predicting, by the server system, using the trained reward liability prediction model, the future reward liability data associated with the plurality of reward programs; [Chang, para 0031, Chang teaches “customer is likely to redeem points from a loyalty rewards program during a next six month period” “based on model redemption predictions, it may be advantageous to target such customers near the beginning of that six month period with a cross-redemption campaign encouraging the members to use their reward points to purchase less expensive rewards, such as retail merchandise” wherein future reward liability data] and 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 the predicted future reward liability data and one or more reward liability criteria [Chang, Figure 5, 513, Chang teaches the offer (ie. reward rule) is customized (changed) based on the specific model being used (based on historical data)]. Regarding claim 2 . Chang in view of Fredergill and Chidlovskii teaches all of the limitations of claim 1 (as above). Chang does not specifically teach, however; Chidlovskii teaches wherein the reward liability prediction model is implemented based at least on a seasonal auto-regressive integrated moving average (SARIMA) time-series model [Chidlovskii, para. 0030, Chidlovskii teaches “To improve predictive accuracy, ARIMA models can be modified to take into account the periodic nature of time series data, an approach known as a multiplicative seasonal ARIMA, or SARIMA, approach. It includes weekly or quarterly dependence relations within the auto-regressive model, by proving that the time series obtained as the difference between the observations in two subsequent weeks is weakly stationary. Conceptually, this approach is premised on the expectation that similar conditions typically hold at the same hour of the day and within the same weekdays. The resulting SARIMA(p, d, q).times.(P, D, Q)s model adds to the standard ARIMA a seasonal auto-regressive, a seasonal moving average, and a seasonal differential component, as follows.” Para. 0031 teaches “The system for the demand prediction of FIGS. 1 and 2 combine a baseline history analysis (e.g. harmonic analysis generating a Fourier model) with a predictor function (e.g. SVR or another regression function, or ARIMA or SARIMA) in order to provide more accurate prediction over various time horizons and time series data sets”] It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang in view of Fredergill model to include Chidlovskii’ s seasonal auto-regressive integrated moving average (SARIMA) timeseries model, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claim 3 . Chang in view of Fredergill and Chidlovskii teaches all of the limitations of claim 2 (as above). Further, Chang teaches wherein identifying the first seasonality pattern and the second seasonality pattern comprises: detecting, by the server system, seasonality trends within the historical reward related data of each reward program provider [Chang, para 0031, Chang teaches “One of the specific types of models listed among the examples above is a redemption model. Such a model may suggest, for example, that a particular customer is likely to redeem points from a loyalty rewards program during a next six month period. Certain redemptions are more expensive (e.g., airline tickets) than others (e.g., retail merchandise). Thus, based on model redemption predictions, it may be advantageous to target such customers near the beginning of that six month period with a cross-redemption campaign encouraging the members to use their reward points to purchase less expensive rewards, such as retail merchandise” wherein seasonality trend] Chang in view of Fredergill does not specifically teach, however; Chidlovskii teaches based, at least in part, on the FFT and at least a frequency value of 365 days; upon determination of the seasonality trends, identifying, by the server system, the first seasonality patterns within the historical reward related data based, at least in part, on a seasonality decomposition model; [Chidlovskii, para. 0013, Chidlovskii teaches “The historical data of parking occupancy is suitably represented as a time series, and may be modeled using various approaches such as machine learning techniques (e.g. support vector regression, SVR), auto-regressive models like auto-regressive integrated moving average (ARIMA), spectral methods like harmonic decomposition” also para. 0014 teaches “The model may, for example, be generated using harmonic analysis generating a Fourier model comprises computing Fourier components for a plurality of different periods, such as a Fourier component with a period of one day, a Fourier component with a period of one week, and/or a Fourier component with a period of one year. It is also contemplated to employ a Fourier transform, e.g. implemented as a fast Fourier transform (FFT) or other discrete Fourier transform (DFT), as the baseline model” wherein plurality of different periods is equivalent to seasonality] and determining, by the server system, the second seasonality patterns based, at least in part, on seasonal lags in moving average and auto-regressive components of a SARIMA time-series model [Chidlovskii, para. 0030, Chidlovskii teaches “To improve predictive accuracy, ARIMA models can be modified to take into account the periodic nature of time series data, an approach known as a multiplicative seasonal ARIMA, or SARIMA, approach. It includes weekly or quarterly dependence relations within the auto-regressive model, by proving that the time series obtained as the difference between the observations in two subsequent weeks is weakly stationary. Conceptually, this approach is premised on the expectation that similar conditions typically hold at the same hour of the day and within the same weekdays. The resulting SARIMA(p, d, q).times.(P, D, Q)s model adds to the standard ARIMA a seasonal auto-regressive, a seasonal moving average, and a seasonal differential component, as follows” also para. 0031 teaches “The system for the demand prediction of FIGS. 1 and 2 combine a baseline history analysis (e.g. harmonic analysis generating a Fourier model) with a predictor function (e.g. SVR or another regression function, or ARIMA or SARIMA) in order to provide more accurate prediction over various time horizons and time series data sets”] It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang in view of Fredergill model to include Chidlovskii’ s seasonal auto-regressive integrated moving average (SARIMA) timeseries model, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claim 4 . Chang in view of Fredergill and Chidlovskii teaches all of the limitations of claim 1 (as above). Chang in view of Fredergill does not specifically teach, however; Chidlovskii teaches wherein the first seasonality pattern comprise a yearly seasonal component of the past redeemed reward points and the second seasonality pattern comprise a weekly seasonal component of the past redeemed reward point [Chidlovskii, para. 0014 teaches “The model may, for example, be generated using harmonic analysis generating a Fourier model comprises computing Fourier components for a plurality of different periods, such as a Fourier component with a period of one day, a Fourier component with a period of one week, and/or a Fourier component with a period of one year”] It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang in view of Fredergill model to include Chidlovskii’ s seasonal auto-regressive integrated moving average (SARIMA) timeseries model (weekly and yearly periods), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claim 5-6 . Claims 5-6 are derivable from Chang. The claims are simple repetitions of the subject matter of claim 1 and hence they are anticipated by Chang wherein Chang predict future reward liability by training reward liability prediction model based on variables and the variable including program aggregate earned reward. Regarding claim 7 . Chang in view of Fredergill and Chidlovskii teaches all of the limitations of claim 1 (as above). Chang in view of Chidlovskii does not specifically teach, however; Fredergill teaches wherein the past redeemed reward points for each reward program over a period of months or years are aggregated on daily time basis [Fredergill, para. 0051 teaches “The retailer host system 30 also performs end-of-day processing which extracts all customer activity from each store by retrieving the transaction log files from each in-store system controller 14 and service desk 16 at each retailer location” wherein aggregating on daily basis] It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang in view of Chidlovskii to include Fredergill daily basis aggregation, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Regarding claim 9 . Chang in view of Fredergill and Chidlovskii teaches all of the limitations of claim 1 (as above). Further, Chang teaches wherein the reward program provider is an issuer [Chang, para 0030, Chang teaches “rewards program associated with a transaction card provider” wherein card issuer]. Regarding claims 10-13 , the claims stand rejected based on the same citations and rationale as applied to claims 1-4, respectively. Regarding claim 14 , the claim stands rejected based on the same citations and rationale as applied to claims 5-6. Regarding claim 15 , the claim stands rejected based on the same citations and rationale as applied to claim 7. Regarding claim 17 , the claim stands rejected based on the same citations and rationale as applied to claim 9. Regarding claim 18 , the claim stands rejected based on the same citations and rationale as applied to claim 1. Conclusion The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure. ROSENBERG et al. (US 20180322517 A1). ROSENBERG teaches generating product forecasts, and more specifically pertains to generating rules for product forecast optimization and reverse engineering of rules from existing products and forecasts. Alderfer et al. (US 20100114661 A1). Alderfer teaches transactions of members of the rewards programs are monitored to identify a subset of members that performed a desired action. Then, the rewards currency of respective rewards programs is distributed to respective rewards-program owners for the subset of members that performed the desired action. 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) 273-3734. Information regarding the status of an application may be obtained from the patent application information retrieval (PAIR) system. Status information of published applications may be obtained from either private PAIR or public PAIR. Status information of unpublished applications is available through private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov . Should you have any questions on access to the private PAIR system, contact the electronic business center (EBC) at (866) 271-9197 (toll-free). If you would like assistance from a USPTO customer service representative or access to the automated information system, call (800) 786-9199 (in US or Canada) or (571) 272-1000. /ABDALLAH A EL-HAGE HASSAN/ Primary Examiner, Art Unit 3623 Application/Control Number: 18/771,593 Page 2 Art Unit: 3623
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