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 . The following is a Final Action. Claims 1-7, 9-15, and 17-18 are pending and rejected below.
Response to Amendments
Applicant’s amendments are acknowledged.
Response to Arguments
Objections to independent claims are withdrawn based on Applicant’s amendments.
Applicant arguments with respect to 101 have been fully considered but are moot and/or non-persuasive.
Applicant argues, “The pending claims recite a technical solution to a technical problem, by leveraging specific technical schemes to assess the time-series of the data, and to train a specific model based on the assessment of the data, in order to make a prediction......The pending claims leverage specific technology to provide "a technical solution for modelling and predicting reward liability data."
Notwithstanding the above, in rejecting the claims, the Office asserts that the claims are directed to managing personal behavior and a mental process....Specifically, the Office cites to accessing data and identifying seasonality patterns in that data. See, Id. These specific limitations are not directed to managing any behavior. The Office cites to "social activities, teaching, and following rules or instructions." See, Id. None of those "behaviors" are included in the cited limitations. Accessing specific data and then processing the data to specifically identify a seasonality pattern is not a human behavior.
The USPTO's October 2019 Update to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) explicitly states that "claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." See, Oct. 2019 PEG Update at p. 7 (emphasis added). Importantly, this evaluation must consider what the human mind is equipped to do, within practical bounds. Here, the human mind is not equipped to perform the identifying. The specific data includes the redeemed points for each reward program aggregated on a particular time basis, whereby different seasonality patterns are identified in that complex data. It is not practical for the human mind to perform this operation. What's more, accessing data in the manner recited, i.e., aggregated data, on a time-series basis, is not something that can even be performed in the human mind. See, USPTO Memo re Reminders on evaluating subject matter eligibility of claims, August. 4, 2025 ("The mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind.").....Based on the above, the pending claims are not directed to an abstract idea.
Examiner responds the “accessing historical reward related data...comprising past redeemed reward points...identifying first seasonality patterns and second seasonality patterns...predicting the future reward liability data...modifying at least one reward rule...” is an abstract idea reasonably categorized as Certain methods of organizing human activity -managing personal behavior (including social activities, teaching, and following rules or instructions) because the data pertains to redeemed reward points which is a type of “human...behavior”. Under BRI each of the limitations above (accessing...identifying...predicting...and modifying...) can be performed in the human mind. The additional technical element of “training a reward liability prediction model...” does not change machine learning is a meaningful way beyond a general link to machine learning technology or a generic and routine machine learning function and thus generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Applicant argues, “In assessing the practical application, Applicant submits that the additional elements include the training of the model, and as amended, the prediction using the model and the rule modification based on the predicted data. These limitations are specifically recited to provide improvement to the technological environment, i.e., data processing, prediction, etc. That is, by the ordered combination of operations, which mainly include the additional elements, the pending claims provide for enhanced performance in modifying rules based on specific predictions, which are made more accurate through the feature engineering imposed on the input data. As such, the "additional elements" are significant in the claimed subject matter. See, MPEP § 2106.04(d)(II) ("It is important to evaluate the significance of the additional elements relative to the invention"). The additional elements, as explained, form the novel, core of the claimed subject mater - and thus, are not mere additions after the fact. The pending claims integrate the alleged idea into a practical application.
Examiner responds the predicting and modifying are part of the abstract idea.
The “training a reward liability prediction model...” is a generic and routine machine learning function that does not change machine learning is a meaningful way beyond a general link to machine learning technology and generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h), and
Applicant argues, “Further, the decision in McRO, Inc. also confirms that the pending claims are directed to an improvement to the functioning of a technical field.
In McRO, Inc., the Court reversed the eligibility invalidation of claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules, because they were not directed to an abstract idea. See, McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). In so doing, the Court held that the claimed process for animating characters using a computer was patent-eligible where it used different techniques from what animators had done before. See, Id. at 1316. The Court emphasized that a key touchstone for determining patent eligibility under § 101 is whether the claims are limited to a specific, concrete way of achieving a result in a conventional industry practice. See, Id. Similar to McRO, the pending claims herein are directed to a different way to compile general data into specific intelligence, which is instructive of predicted future events. The claimed sequence of operations is explicit, and provides a specific concrete manner of achieving the specific result. As in McRO, therefore, the training, prediction and rules modification is achieved, at least in part, using a unique sequence. The pending claims are eligible.
Examiner responds unlike McRo where “it was the incorporation of the claimed rules, not the use of the computer, that ‘improved [the] existing technological process’ by allowing the automation of further tasks...” the predicting and modifying are part of the abstract idea. The steps in the abstract idea (accessing... identifying... predicting...modifying...) are a business process, and the technological limitation of “training a reward liability prediction model...” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The additional computing elements (computer, server system, computing device, computer readable storage medium, instructions, processor) increase the efficiency of the steps above.
Applicant argues, “What's more, as amended herein, the pending claims require a modification to a rule, based on the predicted future reward liability data. This is a transformation of the program from what it was to what it is because of the claimed operations. That is, by the rule modification, the program is therefore transformed into a different state or thing, whereby the claims are eligible.
Examiner responds the modification of the rule does not change the form of the data.
Applicant argues, “Further, training a model in a specific manner on specific data, and then using the trained model, is not generic. See, Office action data Sept. 5, 2025, at p. 4. Further, as stated in Uniloc USA, INC. v. LG Electronics USA, INC., No. 19-1835 (Fed. Cir. 2020), a claim's compatibility with conventional computers does not render it abstract. The allegedly generic computer may be used, as here, and as in Uniloc, to provide technical improvement through performance of the specific limitations. Here, the technical improvement includes the specific sequence of operations to process specific data, through feature engineering, pattern recognition, training, prediction and then rules modification.
Also, the Office provided no evidence, as required in Berkheimer, that the limitations recited in the claims are "well-known, routine, and conventional." For this reason alone, the rejection should be withdrawn.
Examiner responds See Abstract Idea. Based on 2019 revised patent subject matter eligibility guidance Advanced module, Slide 38, Step2B–Limitations that are not indicative of an inventive concept (aka “significantly more”): “Generally linking the use of the judicial exception to a particular technological environment or field of use” – see MPEP 2106.05(h).
Applicant argues, “Moreover, as recently instructed by the Office, Examiners are cautioned not to oversimplify claim limitations and expand the application of the 'apply it' consideration." See, USPTO Memo re Reminders on evaluating subject matter eligibility of claims, August. 4, 2025. As noted, what is relevant is whether "the claim recites only the idea of a solution or outcome" and whether "the claim invokes computers or other machinery merely as a tool." Here, the pending claims recite explicit steps on how the historical data is to be processed, to set up training, and use of the trained model for prediction and rule modification. As such, the former consideration points toward eligibility. Further, because the computing device is integral to the solution - i.e., the solution cannot be accomplished without the computer - the computer is not merely a tool. As such, the later consideration also points toward eligibility.
Examiner responds the steps of “accessing historical reward related data...comprising past redeemed reward points...identifying first seasonality patterns and second seasonality patterns...predicting the future reward liability data...modifying at least one reward rule...” can be performed in the human mind, and it is categorized as certain methods of organizing human activity because it is managing personal behavior (including social activities, teaching, and following rules or instructions) as the data includes past redeemed reward points. The additional element, “training a reward liability prediction model...” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The additional computing elements (computer, server system, computing device, computer readable storage medium, instructions, processor) increase the efficiency of the steps above and are used as tool to implement the abstract idea.
Applicant arguments with respect to 103 have been fully considered but are moot and/or non-persuasive.
Applicant argues, “In rejecting Claim 1, the Office relies on Chang to disclose access of the specific data. See, Office action dated Sept. 5, 2025, at p. 6.
However, as amended, Claim 1 recites that the historical data is for a plurality of rewards programs. As cited, Chang discloses historical data as it relates to a model developed for an individual member of the loyalty program, which is apparently only one member and only one program. See, Abstract. At 0031, Chang discloses a prediction of the customer over a six month period. The historical data is not explained, or even disclosed. As such, there is no indication that historical data for each of a plurality of programs is aggregated into a time series.
At 0040, Chang discloses reward data, which is defined as the results from "a prior campaign." Again, while historical data is apparently disclosed, there is no disclosure of the data being aggregated in the manner claimed. As such, Chang is deficient.
Examiner responds under BRI, Chang’s “cross-redemption campaign” (0031, 0047-0048, Figure 5(513)) - using offers to promote lesser expensive rewards- is interpreted as a second reward program in addition to the first regular reward program where a customer redeems the rewards; under BRI the historical data pertains to both programs;
Several partners (ie. retail stores) are interpreted as “of a plurality of reward program providers” (0036)
“For the model type selected, performance time periods are selected at step 304. In some cases, there are defined "pre-performance" and "performance" periods” (the historical time data is interpreted as aggregated into time series) (0038-0039, Figure 4A-B)
Applicant argues, “Further, in rejecting Claim 1, the Office relies on Chang to disclose identifying the first seasonal pattern associated with the historical reward data.....As cited, Chang discloses specific types of models that predict when a customer might redeem points in a next six month period. That predicted timing may be useful in making offers to the customer. There is no identification of a seasonality pattern of historical data. That is, the seasonality pattern is a pattern in then historical data suggesting redeem of rewards points is seasonal, i.e., it is a representation of the historical reward related data. In 0031, Chang discloses what is going to happen in the next six months. Accordingly, this is not a seasonality pattern included in historical data.....Chang does disclose historical data used to build the models. The data is disclosed as including various details, including, redemption pricing data, and responses to prior campaigns, etc. Nothing in 0040, however, teaches or suggests that any specific pattern in the historical data is identified. As such, Chang is again deficient.
Examiner responds the ‘upcoming 6 months when a customer is likely to redeem points’ (0031, 0040) is interpreted as the first seasonality pattern- because the targeting is based on a “right time to target” (0004(bottom)) and a particular “timing of a campaign” (0029(bottom)), indicating the model is identifying a strategic date from the historic data (seasonality pattern) in addition to durations.
Applicant argues, “the Office fails to provide any motivation for one skilled in the art to combine Chang and Chidlovskii in the manner suggested. Rather, the Office only alleges that the combination would yield predictable results. See, Office action dated Sept. 5, 2025, at p. 7. In the rejection, therefore, there is no reason wy one skilled in the art would have thought to make the combination. See, MPEP § 2143 ("The key to supporting [the obviousness rejection is "clear articulation of the reason(s) why the claimed invention would have been obvious," emphasis added.). As such, under the Supreme Court's decision in KSR, the combination is improper.”
Examiner responds Chang is directed to using historical data to predict behavior of members of a loyalty program and applying the predictions to manage campaigns and offers” (Abstract, 0002). Chidlovskii is directed to predicting demand for items such as a commercial product (e.g. a retail merchandise product) using types of calendar cycles (ie. daily, weekly, seasonal) for decisions on scheduling employees and optimizing product prices (0002-0003), which are concerned with similar problems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang’s pattern to include Chidlovskii’s seasonal pattern, 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.
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 1-7, 9-15, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
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 amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-7, 9-15, and 17-18 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claims 1, 10, and 18 include limitations reciting predicting reward liability data of reward programs including steps:
Accessing historical reward related data...
Identifying first seasonality patterns and second seasonality patterns...
Predicting the future reward liability data...
Modifying at least one reward rule...
which is an abstract idea reasonably categorized as
Certain methods of organizing human activity –managing personal behavior... (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 2-7, 9, 11-15, and 17 further describe making determinations and descriptive data that further narrow the abstract idea.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 10, and 18 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include computer, server system, computing device, computer readable storage medium, instructions, processor. When considered in view of the claim as a whole, Examiner submits that the additional elements do not integrate the abstract idea into a practical application because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
The “training a reward liability prediction model” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Claim 2-7, 9, 11-15, and 17 do not include additional elements above and beyond claims 1, 10, and 18.
As a result, Claims 1-18 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1, 10, and 18 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include computer, server system, computing device, computer readable storage medium, instructions, processor. When considered in view of the claim as a whole, Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
The “training a reward liability prediction model” generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h) is recited with high generality and generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Claim 2-7, 9, 11-15, and 17 do not include additional elements above and beyond claims 1, 10, and 18 and thus do not provide significantly more to the abstract idea.
Thus, Claims 1-7, 9-15, and 17-18 do not provide significantly more to 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
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 9-14, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (20090254413) in view of Chidlovskii (20150088790).
Regarding Claim 1, Chang discloses:
A computer-implemented method, (Figure 2, 0063-0068 – computer system, processor, memory, instructions) comprising:
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, the historical reward related data comprising past redeemed reward points for each reward program aggregated on a particular time basis; (Abstract - A set of loyalty behavior models is developed for an individual member of the loyalty program is developed based on the historical data.
[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, 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.
Under BRI, the “cross-redemption campaign” (0031, 0047-0048, Figure 5(513)) - using offers to promote lesser expensive rewards- is interpreted as a second reward program in addition to the first regular reward program where a customer redeems the rewards; under BRI the historical data would pertain to both programs;
0036- several partners (retail stores) are interpreted as “of a plurality of reward program providers”
0038-0039, Figure 4A-B -For the model type selected, performance time periods are selected at step 304. In some cases, there are defined "pre-performance" and "performance" periods” (the historical time data is interpreted as aggregated into time series)
0040 - Reward related data may include, without limitation: number of rewards points earned, number of rewards points redeemed, redemption transactions along with the associated date of the transaction, type of redemptions and cost of redemptions.....Results from a prior campaign may include, without limitation....redemption pricing data, reward points offer data....
identifying, by the server system, first seasonality patterns and second patterns included in the historical reward related data; (0031, 0040 – the ‘upcoming 6 months when a customer is likely to redeem points’ is interpreted as the first seasonality pattern, because the targeting for the redemption is a “right time to target” (0004(bottom)) / a particular “timing of a campaign” (0029(bottom)) indicating the model is identifying a strategic date from the historic data (seasonality pattern) in addition to durations; the redemption pricing data, type of redemption, number of points, etc. is interpreted as second patterns)
training, by the server system, a reward liability prediction model based, at least in part, on seasonality patterns, wherein the trained reward liability prediction model is configured to predict future reward liability data associated with the plurality of reward programs. (Abstract - A set of loyalty behavior models is developed for an individual member of the loyalty program is developed based on the historical data.
[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.
[0038] For the model type selected, performance time periods are selected at step 304. In some cases, there are defined "pre-performance" and "performance" periods. In other cases, there are defined "pre-performance" and "post-performance" periods. "Pre-performance" refers to a time period before a customer participated in a campaign. "Performance" refers to a time period during which the customer participated in the campaign. "Post-performance" refers to a time period after the customer's participation in the campaign was complete. FIG. 4a is a graphical representation of an example of a pre-performance period 402 and a performance period 404 used to define and validate a model.)
predicting, by the server system, using the trained reward liability prediction model, the future reward liability data associated with the one or more reward programs (0031 –customer likely to redeem more expensive points in next 6 months; “...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.”)
modifying, by the server system, at least one reward rule associated with one or more reward program based, at least in part, on the predicted future reward liability data and one or more reward liability criteria. (Figure 5(513) –the offer (ie. reward rule) is customized (changed) based on the specific model being used (based on historical data); 0047-0048- a "redemption" campaign may be intended to encourage a consumer to redeem points in a loyalty program for items that are less costly to the provider.... As used herein, an "offer" is a feature of a campaign which can be changed depending on a customer's predicted response to that feature. An offer may also be referred to as a variable.)
Chang does not explicitly state the second pattern is a seasonality pattern.
Chidlovskii, directed to predictive modeling of time series data, discloses this limitation [0030] 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......[0031] 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) Chang is directed to the use of historical data to predict behavior of members of a loyalty program, and applying the predictions to manage campaigns and offers (Abstract, 0002). Chidlovskii is directed to predicting demand for items such as a commercial product (e.g. a retail merchandise product) using types of calendar cycles (ie. daily, weekly, seasonal) for decisions on scheduling employees and optimizing product prices (0002-0003) which are concerned with similar problems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang’s pattern to include Chidlovskii’s seasonal pattern, 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 2, Chang discloses: The computer-implemented method as claimed in claim 1. Chang does not explicitly state, however, Chidlovskii discloses: wherein the reward liability prediction model is implemented based at least on a seasonal auto-regressive integrated moving average (SARIMA) time-series model. [0030] 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......[0031] 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’s model to include
Chidlovskii’s seasonal auto-regressive integrated moving average (SARIMA) time-series 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 discloses: The computer-implemented method as claimed in claim 2, wherein identifying the first seasonality patterns and the second patterns comprises: detecting, by the server system, seasonality trends within the historical reward related data of each reward program provider; upon determination of the seasonality trends, identifying, by the server system, the first seasonality patterns within the historical reward related data [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. (seasonality pattern based on trends within historical data) 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.
Chang does not explicitly state: Chidlovskii discloses the detecting seasonality trends is based, at least in part, on fast-Fourier transform (FFT) method and the identifying, by the server system, the first seasonality pattern is based, at least in part, on a seasonality decomposition model; (0013-0014 -... 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....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) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Chang’s model to include Chidlovskii’s FTF method and seasonality decomposition 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.
Chang does not explicitly state: Chidlovskii discloses: 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. [0030] 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......[0031] 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’s model to include
Chidlovskii’s seasonality patterns and seasonal lags in moving average and auto-regressive components of the SARIMA time-series 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 discloses: The computer-implemented method as claimed in claim 1, wherein the first seasonality patterns comprise a seasonal component of the past redeemed reward points. (0031, 0040 –customer likely to redeem points in next 6 months)
Chang does not explicitly state the first seasonal component is yearly nor the second seasonality pattern comprise a weekly seasonal component. Childlowski discloses yearly and weekly seasonal components in a model (0014 - 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’s components to include Childowski’s yearly sesonal component and weekly seasonal components, 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, Chang discloses: The computer-implemented method as claimed in claim 1, wherein the reward liability prediction model is further trained based, at least in part, on exogenous variables, the exogenous variables comprising at least one seasonality pattern and a correlated variable. (0031- when a customer is likely to redeem (seasonality pattern); 0040 – Results from a prior campaign may include, without limitation... redemption pricing data (correlated variable))
Regarding Claim 6, Chang discloses: The computer-implemented method as claimed in claim 5, wherein the correlated variable comprises aggregated earned reward points in each reward program for the reward program provider. (0040 –under BRI the redemption pricing data as the “results in a campaign” would be based on aggregated earned reward points)
Regarding Claim 9, Chang discloses: The computer-implemented method as claimed in claim 1, wherein the reward program provider is an issuer. (0030 - ...loyalty or rewards program associated with a transaction card provider)
Claims 10-13 stand rejected based on the same citations and rationale as applied to Claims 1-4, respectively.
Claim 14 stand rejected based on the same citations and rationale as applied to Claims 5-6.
Claims 17 stand rejected based on the same citations and rationale as applied to Claim 9.
Claim 18 stand rejected based on the same citations and rationale as applied to Claims 1.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (20090254413) in view of Chidlovskii (20150088790) in view of Fredergill (20050144074).
Regarding Claim 7, Chang discloses: The computer-implemented method as claimed in claim 1, wherein the past redeemed reward points for each reward program over a period of months or years are aggregated. (0040(bottom)-the redemption pricing data in the results of a prior campaign would be based on aggregating redeemed reward points; Figure 4A-B – months or years) Chang does not explicitly state the aggregation is on a daily time basis. Fredergill directed to a reward and redemption program, discloses this limitation (0051 - In this particular embodiment, the point value for redeeming a redemption item is -750 points. The reduction of the price of the redeemable item is -$1.20, assuming the item purchased has a price that is greater than or equal to $1.20. This information is maintained on the merchant web server 50 and the retailer host system 30. For the in-store system transactions in the retailer outlet, this information has to be downloaded into the in-store system controller 14 from the retailer host system 30. 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. These files of daily activity are uploaded to the retailer's host 30 and/or to the central database host system 44. It is to be understood that the loyalty program e-commerce server 46 may also perform end of the day processing in a manner similar to that of the retailer host system 30.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to apply Fredergill’s daily aggregation to Chang in view of Childowski’s reward points, 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.
Claim 15 stand rejected based on the same citations and rationale as applied to Claims 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [AltContent: rect]
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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 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 date of this final action.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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