DETAILED ACTION
This is a Final Office Action in response to the amendment filed 06/18/2026.
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 .
Status of Claims
Claims 1-5, 7-14, 16-19, 21-23 are currently pending in the application and have been examined.
Response to Amendment
The Amendment filed 06/18/2026 has been entered.
Response to Arguments
Claim Rejections 35 U.S.C. § 101:
Applicant submits on page 13 of the remarks that the claims do not recite a mental process. Examiner respectfully disagrees and notes that the steps recited in the claims are acts of evaluating information that can be practically performed in the human mind. As the Federal Circuit has explained, "Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016). See MPEP 2106.04(a)(2).
Applicant submits on pages 15-18 that the claims are integrated into a practical application. Examiner respectfully disagrees and notes that the additional elements recited in the claims are just applying the use of a generic computer environment to perform the abstract idea. These additional elements do not provide improvement to the computer technology and do not provide a meaningful link of the abstract idea to a practical application.
Regarding Applicant’s comparison to Ex parte Desjardins, the Examiner notes that the claims were patent eligible based on a finding that the claims reflect improvement to training a machine model by using less storage capacity, enabling reduced system complexity and effectively learning new tasks in succession whilst protecting knowledge about previous tasks. The present claims do not provide any improvement to a machine learning model.
Applicant submits on pages 19-20 that the claims amount to significantly more than any alleged abstract idea. Examiner notes that when determining whether a claim recites significantly more in Step 2B the analysis takes into consideration whether the claim effects a transformation or reduction of a particular article to a different state or thing. Transformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines." Bilski v. Kappos, 561 U.S. 593, 658, 95 USPQ2d 1001, 1007 (2010) (quoting Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972)). See MPEP 2106.05(c). Furthermore, the additional elements recited in the claims merely recite the use of a generic computer to perform generic computer functions of storing and transmitting data. These generic computer functions do not integrate the abstract idea into a practical application and do not recite significantly more than the judicial exception.
Claim Rejections 35 U.S.C. § 103:
Applicant submits on page 22 of the remarks that the combination of references Perrone in view of Abramowicz fails to teach or suggest the features of amended claim 1. Examiner respectfully disagrees, as noted in the present office action Perrone discloses increases in amount of business and increase earnings (i.e. revenue uplift) in at least Col. 3 Lines 5-20.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-5, 7-14, 16-19, 21-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1-5, 7-14, 16-19, 21-23 the independent claims (claims 1, 10 and 19) are directed, in part, to a method and a system to predicting a selection of product offerings. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1-5, 7-9, 21 are directed to a method comprising a series of steps which falls under the statutory category of a process, claims 10-14, 16-18, 22 are directed to a system which falls under the statutory category of a machine and claims 19, 23 are directed to a non-transitory computer-readable medium. However, these claim elements are considered to be abstract ideas because they are directed to a mental process which includes observations or evaluations.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to receiving one or more features characterizing a geographic region; receiving a selection of payment methods and/or product features offered by a platform and to be adopted by the first user; computing, by a computer system comprising one or more processing circuits, a plurality of specific predictions of revenue uplifts corresponding to the selection of payment methods and/or product features, wherein computing each specific prediction of revenue uplift comprises: selecting a specific model for a payment method and/or product feature of the selection of payment methods and/or product features corresponding to the one or more features characterizing the geographic region, the specific model being trained based on training data collected by the platform; and supplying the one or more features characterizing the first user to the specific model to compute a specific prediction of revenue uplift for the
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, independent claim 1, directed to a method recites additional elements: a platform, a computer system comprising one or more processing circuits, model being trained based on training data collected by the platform; independent claim 10 recites additional elements: system, processor, memory, platform, model; independent claim 19 recites additional elements: non-transitory computer readable medium, processor, platform, model. These additional elements are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figures 1 and 7-8 and related text and [0012]; [0084]; [0093] to understand that the invention may be implemented in a generic environment that “FIG. 8 is a block diagram illustrating components of a processing circuit or a processor, according to some example embodiments, configured to read instructions from a non-transitory computer-readable medium (e.g., a non-transitory machine-readable storage medium) and perform any one or more of the methods discussed herein.”; “FIG. 7 is a block diagram illustrating an example software architecture 706, which may be used in conjunction with various hardware architectures herein described. FIG. 7 is a non-limiting example of a software architecture 706, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture 706 may execute on hardware such as a machine 800 of FIG. 8 that includes, among other things, processors 804, memory/storage 806, and input/output (I/O) components 818. A representative hardware layer 752 is illustrated and can represent, for example, the machine 800 of FIG. 8. The representative hardware layer 752 includes a processor 754 having associated executable instructions 704. The executable instructions 704 represent the executable instructions of the software architecture 706, including implementation of the methods, components, and so forth described herein. The hardware layer 752 also includes non-transitory memory and/or storage modules as memory/storage 756, which also have the executable instructions 704. The hardware layer 752 may also include other hardware 758.”; “The machine 800 may include processors 804 (including processors 808 and memory/storage 806, and I/O components 818, which may be configured to communicate with each other such as via a bus 802. The memory/storage 806 may include a memory 814, such as a main memory, or other memory storage, and a storage unit 816, both accessible to the processors 804 such as via the bus 802. The storage unit 816 and memory 814 store the instructions 810 embodying any one or more of the methodologies or functions described herein. The instructions 810 may also reside, completely or partially, within the memory 814, within the storage unit 816, within at least one of the processors 804 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 800. Accordingly, the memory 814, the storage unit 816, and the memory of the processors 804 are examples of machine-readable media.” Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
The dependent claims further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 7-11, 16-19, 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat. No. 9,767,471 (hereinafter; Perrone) in view of US Pub. No. 2008/0288326 (hereinafter; Abramowicz).
Regarding claims 1/10/19, Perrone discloses:
A method; A system; A non-transitory computer-readable medium comprising: receiving one or more features characterizing a first user; (Perrone Col. 2, Lines The service provider may receive transaction information from a plurality of merchants, and may organize the transaction information into merchant profiles and buyer profiles; Col. 16, Lines 13-17 disclose The recommendation module 130 may access the first merchant profile 124(1) to determine information relevant to the first merchant such as a merchant category 602, merchant location information 604, or various other types of merchant information.) receiving one or more features characterizing a geographic region; (Perrone Col. 16, Lines 24-28 disclose Similarly, the merchants (and buyers) may be classified into location categories, such as for particular categories of geographic regions, e.g., same street, same neighborhood, same postal code, same district of a city, same city, and so forth.)
receiving a selection of payment methods and/or product features offered by a platform and to be adopted by the first user; (Perrone Col. 26, Lines 50-54 disclose The data describing the merchants 108 can include, for example, a merchant name, geographic location, contact information, and an electronic catalogue, e.g., a menu, that describes items that are available for purchase from the merchant.)
computing, by a computer system comprising one or more processing circuits, a plurality of specific predictions of revenue uplifts corresponding to the selection of payment methods and/or product features, wherein computing each specific prediction of revenue uplift comprises: selecting a specific model for a payment method and/or product feature of the selection of payment methods and/or product features corresponding to the one or more features characterizing the geographic region, the specific model being trained based on training data collected by the platform; (Perrone Col. 3 Lines 5-20 discloses increases in amount of business the merchant could perform and increase earnings; Col. 20, Lines 16-41 disclose Buyers can sometimes change home or work locations. To ensure that a buyer is still local to a particular geographic region, in some examples, association of geographic regions with particular buyer profiles can be refined based on how recently the particular buyer conducted transactions in respective geographic regions. For example, transactions that occurred over six months earlier might be discarded when identifying geographic regions in which a particular buyer frequently conducts transactions. In some situations, the locality of a buyer can vary depending on the type of merchant. For example, a stricter threshold of locality can be used when identifying a buyer as being local to a coffee shop versus when identifying the buyer as being local to a tailor or kite specialty retailer. Furthermore, in some examples, one or more predictive analytical models can be trained to predict on-the-fly whether a buyer that has checked-in to a merchant or is currently conducting a transaction with the merchant is a local buyer. Furthermore, while the example of FIG. 6 started by determining the subset of buyer profiles based on transactions with the first merchant and/or other merchants in the same category as a first merchant, in other examples, the recommendation module may determine the subset of buyer profiles based on other considerations such as by starting with a common location for conducting transactions, a common item, a common demographic of the buyers, or the like.)
and supplying the one or more features characterizing the first user to the specific model to compute a specific prediction of revenue uplift for the(Perrone Col. 3 Lines 5-20 discloses increases in amount of business the merchant could perform and increase earnings; Col. 20, Lines 30-33 disclose in some examples, one or more predictive analytical models can be trained to predict on-the-fly whether a buyer that has checked-in to a merchant or is currently conducting a transaction with the merchant is a local buyer.)
Although Perrone discloses predicting product offerings, Perrone does not specifically disclose an aggregated prediction. However, Abramowicz discloses the following limitations:
and computing, by the computer system, an aggregated prediction of revenue uplift from adopting the selection of payment methods and/or product features based on the plurality of specific predictions of revenue uplifts, the aggregated prediction of revenue uplift being smaller than a sum of the plurality of specific predictions of revenue uplifts for the selection of payment methods and/or product features.
(Abramowicz [0124] discloses the aggregated prediction could then be a function of the various probabilities (for example, a probability-weighted average of the midpoint of each range); Table 1 discloses lover predictions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the method and system for forecasting customer satisfaction in commercial transactions of Abramowicz in order to predict a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers (Abramowicz abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 2, Although Perrone discloses predicting product offerings, Perrone does not specifically disclose variance associated or a distribution. However, Abramowicz discloses the following limitations:
The method of claim 1, wherein the specific model comprises one or more sub-models trained based on historical data associated with the payment method and/or product feature, wherein a sub-model of the one or more sub-models being configured to compute an estimate having a distribution of values and a variance associated with the distribution of values, and the specific model is configured to combine estimates from the one or more sub-models to compute the specific prediction of revenue uplift of the specific model. (Abramowicz [0092] discloses For example, a predictor may simply indicate that "consumer satisfaction will be high", as long as the prediction aggregation mechanism includes some algorithm for converting such qualitative statements into quantitative entities, such as point estimates or probability distributions; [0154] discloses variance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the method and system for forecasting customer satisfaction in commercial transactions of Abramowicz in order to predict a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers (Abramowicz abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 7/16, Perrone discloses:
The method of claim 1; The system of claim 10, wherein the computing the aggregated prediction of revenue uplift comprises computing an overlap of the plurality of specific predictions of revenue uplifts associated with different payment providers among the selection of payment methods and/or product features. (Perrone Col. 8, Lines 1-13 disclose a payment processing module for multiple payment types and providers.)
Regarding claims 8/17, Although Perrone discloses predicting product offerings, Perrone does not specifically disclose a midpoint. However, Abramowicz discloses the following limitations:
The method of claim 1; The system of claim 10, wherein the computing the aggregated prediction of revenue uplift comprises adding midpoints of a corresponding interval for each of the plurality of specific predictions of revenue uplifts. (Abramowicz [0124] discloses midpoint of ranges.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the method and system for forecasting customer satisfaction in commercial transactions of Abramowicz in order to predict a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers (Abramowicz abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 9/18, Perrone discloses:
The method of claim 8; The system of claim 17, wherein the corresponding interval for each of the plurality of specific predictions of revenue uplifts is a confidence interval computed based on the training data. (Perrone Col. 10, Lines 44-52 disclose The statistical model may be initially trained using a set of training data, checked for accuracy, and then used for matching transactions with particular buyer profiles by determining confidence scores, and associating a particular transaction with a particular buyer profile when a confidence score exceeds a specified threshold of confidence. The statistical model may be periodically updated and re-trained based on new training data to keep the model up to date.)
Regarding claim 11, Although Perrone discloses predicting product offerings, Perrone does not specifically disclose variance associated or a distribution. However, Abramowicz discloses the following limitations:
The system of claim 10, wherein the specific model comprises one or more sub-models trained based on historical data associated with the payment method and/or product feature, wherein a sub-model of the one or more sub-models is configured to compute an estimate having a distribution of values and a variance associated with the distribution of values, and the specific model is configured to combine estimates from the one or more sub-models to compute the specific prediction of revenue uplift of the specific model. (Abramowicz [0092] discloses For example, a predictor may simply indicate that "consumer satisfaction will be high", as long as the prediction aggregation mechanism includes some algorithm for converting such qualitative statements into quantitative entities, such as point estimates or probability distributions; [0154] discloses variance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the method and system for forecasting customer satisfaction in commercial transactions of Abramowicz in order to predict a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers (Abramowicz abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 21/22/23, Although Perrone discloses predicting product offerings, Perrone does not specifically disclose filtering data. However, Abramowicz discloses the following limitations:
The method of claim 1, wherein computing each specific prediction of revenue uplift further comprises: filtering the training data collected by the platform based on the selection of payment methods and/or product features; and training the specific model using the filtered training data. (Abramowicz discloses filtering in at least [0101-0102].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the method and system for forecasting customer satisfaction in commercial transactions of Abramowicz in order to predict a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers (Abramowicz abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Claim(s) 3, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perrone in view of Abramowicz, further in view of US Pub. No. 2015/0310466 (hereinafter; LaCivita).
Regarding claims 3/12, although Perrone discloses predicting product offerings, Perrone does not specifically disclose a holdback sub-model. However, LaCivita discloses the following limitations:
The method of claim 2; The system of claim 11, wherein the one or more sub-models comprise a holdback sub-model trained based on the historical data, and wherein the historical data is based on the payment method and/or product feature being hidden from a plurality of second users. (LaCivita See at least [0121].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the sales analyzer of LaCivita in order to retrieve specific sales information (LaCivita abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Claim(s) 4, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perrone in view of Abramowicz, further in view of US Pub. No. 2005/0086246 (hereinafter; Wood).
Regarding claims 4/13, although Perrone discloses predicting product offerings, Perrone does not specifically disclose a difference in difference sub-model. However, Wood discloses the following limitations:
The method of claim 2; The system of claim 11, wherein the one or more sub-models comprise a difference- in-difference sub-model trained based on matching pairs of first users that have matching first user features in the historical data, and wherein the difference-in-difference sub-model is configured to compute an estimate for the payment method and/or product feature based on identifying a pair of first user features matching the one or more features characterizing the first user. (Wood [0008] discloses comparing difference-difference values.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the database for performance baseline of Wood in order to provide comparisons (Wood abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Claim(s) 5, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perrone in view of Abramowicz, further in view of US Pub. No. 2009/0006490 (hereinafter; Hunt).
Regarding claims 5/14, although Perrone discloses predicting product offerings, Perrone does not specifically disclose an inverse variance weighting model. However, Hunt discloses the following limitations:
The method of claim 2; The system of claim 11, wherein the specific model is configured to combine predictions from the one or more sub-models based on inverse-variance weighting based on the variance of the distribution of values of the estimate. (Hunt discloses inverse variance weighting in at least [0048].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for determining recommendations from buyer information of Perrone with the data tables of Hunt in order to reduce bias when identifying segments for comparison (Hunt abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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/FRANCIS Z. SANTIAGO MERCED/Examiner, Art Unit 3625
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