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 .
DETAILED ACTION
Response to Amendment
This communication is in response to the amendment filed on 11/19/2025 for the application No. 18/742,522, Claims 1-9 and 11-19 are currently pending and have been examined. Claims 1-9 and 11-19 have been rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
As to claims 1 and 11, regarding to the amended limitation “complex transactions behavior”, the amended limitation seems that is a relative term, which renders the claim indefinite. The specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how these limitations delimit the scope of the invention. These limitations render the claim indefinite. Per MPEP 2173,.05(b) the definiteness requirement "mandates clarity”. It is not clear in the instant claims the term “ complex transactions behavior”, how the processor that is part of the computer system claimed (independent claim 11) implements said “complex transactions behavior”. The instant specification discloses “[0069]Furthermore, the AI analysis module 122 can employ anomaly detection techniques to identify outliers and irregularities in the transaction history that may suggest additional discount eligibility. For instance, a sudden gap in transactions followed by a pattern consistent with military deployment and return can be flagged for further review. This dynamic analysis capability ensures that the AI analysis module 122 can account for complex and varied transaction behaviors.”, paragraph 69. Herein, there is not mention of a metric, so a processor can identify “complex transactions behavior”. The specification did not
"provide a reasonably clear and exclusive definition, leaving the facially subjective claim language without an objective boundary".
Dependent claims 2-9 and 12-19, ,they depend of claims 1 and 11, they do not cure the deficiencies set forth above. Therefore, these dependent claims are also rejected for failing to clarify the relative term which renders the claims indefinite.
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 1-9 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-9 and 11-19 are not compliant with 101, according with the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c). Examiner’s analysis is presented below for all the claims.
Claim 1: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a method.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim? Yes. The claim recites the limitations of “determining if the user has self-identified as belonging to the class of users eligible for the discount; determining if the user has been on a receiving end of a financial transaction … restricted to the class of users eligible for the discount; determining if the user is using a financial product restricted to the class of users eligible for the discount; determining if a financial account of the user is …restricted to the class of users eligible for the discount; reviewing a transaction history of the user to identify one or more transactions made at a geographic location with access limited to the class of users eligible for the discount; reviewing the transaction history of the user to identify at least one reoccurring transaction having a value consistent with an applied discount limited to the class of users eligible for the discount; and determining a score indicating the probability of the user belonging to the class of users eligible for the discount”.
The “determining, reviewing” limitations, as drafted, is a process and system that, under its broadest reasonable interpretation, covers performance of the limitations as certain methods of organizing human activity, advertising, marketing or sales activities or behaviors. The method for assessing a probability of a user belonging to a class of users eligible for a discount. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application? No.
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure: “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”. The limitations comprise generic recited elements, just software and data. Applicant makes sure the claim is an abstract idea because there is not in the recitation a computer, server or machine to perform the claimed method.
The combination of these additional elements can be considered no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea).
Step 2B : claim provides an inventive concept? No.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim,
“from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, amount to no more than mere instructions to apply the exception. i.e., mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Here, the limitations:
“from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Further, the instant specification does not provide any indication that the elements
“from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, are anything other than generic data, and the Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946.
In this case, the elements “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, limitations (pointed above) are well-understood, routine, conventional activity is supported under Berkheimer Option 2. See MPEP 2106.05 (d). The claim is ineligible.
Claim 11: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a system.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
The claim recites additional elements “one or more processors; and
non-transitory computer-readable storage media encoding instructions which, when execute by the one or more processors, cause the computer system..”
The limitations comprise generic recited computer elements, recited at a very high level. It merely reflects the use of conventional technology and amounts to only generally linking the use of an abstract idea to a particular technological environment. MPEP 2106.05(h).
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Dependent claims 2-9 and 12-19, the claims recite elements such as “wherein, when the score meets or exceeds a first threshold, further comprising prompting the user to confirm membership among the class of users eligible for the discount”, etc. These elements do not integrate the system of organizing human activity into a practical application. The claims are ineligible.
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 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 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.
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 nonobviousness.
Claims 1-4, 7, 11-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20160086222 (Kurapati) in view of US PG. Pub. No. 20040122736 (Strock) and in view of US PG. Pub. No. 20080147495 (Bal).
As to claims 1 and 11, Kurapati discloses a method for assessing a probability of a user belonging to a class of users eligible for a discount (“Disclosed herein is a method for providing an offer which includes selecting at least one offer for presentation by applying at least one rule dictated by a merchant to a set of offers, applying at least one rule dictated by a financial institution to the offers, and applying a filter to identify offers with the highest likelihood of being accepted by the user…”, abstract), comprising:
a) determining if the user has self-identified as belonging to the class of users eligible for the discount;
(…“a method of tracking financial transactions may include three separate groups of consumers. This embodiment is a method for tracking financial transactions. The method includes step of gathering transaction data from a plurality of user financial accounts by at least one computer, each user account a financial institution account at a financial institution, analyzing the transaction data for each user by the at least one computer for at least one criterion for a savings opportunity indication, and providing a first savings opportunity to a first plurality of users whose transaction data meet the at least one criterion. …”, paragraph 18.
“…methods and systems may comprise gathering transaction data from a user's financial account, analyzing the transaction data for a savings opportunity indication, matching a savings opportunity from a database of savings opportunities to the user based on the savings opportunity indication, and displaying the savings opportunity in association with a statement of a user's financial account. Further, a past response may be gathered to a savings opportunity indication and analyzing it, wherein the savings opportunity is based on both the analyzed transaction data and past response data. The statement may be an online statement, an online graphical user interface associated with the user's financial account, an online bill pay area, a dialog box associated with the user's financial account…”, paragraph 20);
b) determining if the user has been on a receiving end of a financial transaction from an organization restricted to the class of users eligible for the discount;
(“[0013] In embodiments, methods and systems may help track consumer behavior and incentives. One embodiment is a method for tracking financial transactions. The method includes step of gathering transaction data from a plurality of user accounts by at least one computer, each user account a financial institution account at a financial institution and analyzing the transaction data for each user by the at least one computer for at least one criterion. The method also includes a step of providing a first savings opportunity to a first plurality of users whose transaction data meet the at least one criterion, the first savings opportunity provided in association with an online statement of the user's financial account, tracking a redemption of the savings opportunity by the first plurality of users whose transaction data met the at least one criterion, and tracking purchasing behavior for a period of time for the first plurality of users whose transaction data met the at least one criterion before and after the savings opportunity was provided”, paragraph 13.
“[0016] In embodiments, systems and methods may track the behavior of more than one group of consumers receiving different incentives. Another embodiment is a method for tracking financial transactions. The method includes steps of gathering transaction data from a plurality of user accounts by at least one computer, each user account a financial institution account at a financial institution, analyzing the transaction data for each user by the at least one computer for at least one criterion for a savings opportunity indication, and providing a first savings opportunity to a first plurality of users whose transaction data meet the at least one criterion. The method also includes steps of providing a second savings opportunity to a second plurality of users whose transaction data meet the at least one criterion, tracking a redemption of the savings opportunity by the first plurality of users and the second plurality of users, and tracking purchasing behavior of the first plurality of users and the second plurality of users for a period of time before and after the savings opportunity was provided”, paragraph 16);
c) determining if the user is using a financial product restricted to the class of users eligible for the discount;
(“Another embodiment is a method for tracking financial transactions. The method includes steps of gathering transaction data from a plurality of user accounts by at least one computer, each user account a financial institution account at a financial institution, analyzing the transaction data for each user by the at least one computer for at least one criterion for a savings opportunity indication, and providing a first savings opportunity to a first plurality of users whose transaction data meet the at least one criterion…”, paragraph 16.
“0017] In some of these embodiments, there is a further step of comparing spending on a good or a service in a category of the first and second savings opportunity by the first plurality of users and the second plurality of users. In some embodiments, there is a further step of tracking purchasing behavior of a third plurality of users whose transaction data met the at least one criterion, said third plurality of users not being provided the savings opportunity. In some of these embodiments, there is a further step comprising presenting the first savings opportunity or the second savings opportunity to a user in association with an on-line statement of the user's financial institution account. In some embodiments, there is a further step of presenting the user with an opportunity to view detailed billing records of the user's transactions with a merchant associated with the first savings opportunity or the second savings opportunity….”, paragraphs 17-18.
“…The user financial account is may be a credit card account, a bank account, a checking account, a savings account, a personal finance program account, a loan account, and the like. Analyzing the transaction data may involve extracting a merchant name, a merchant category, a merchant location, a transaction amount, a transaction frequency, a zip code, a store name, a store category, a store number a transaction description, a purchase frequency, a total spending amount, and the like. Further, the transaction data may be anonymized”, paragraph 23);
d) determining if a financial account of the user is linked with a financial institution or account restricted to the class of users eligible for the discount;
([0015] In some embodiments, the at least one criterion is selected from the group consisting of a previous purchase of an item, an amount of one or more purchases of an item, a number of transactions, a frequency of transactions and a transaction category. In some embodiments, there is a further step of comparing tracking information about purchasing behavior of the first plurality of users and a second plurality of users whose transaction data met the at least one criterion but who were not provided the savings opportunity …”, paragraphs 15 and 17);
e1) ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history;
(“[0014] In some of these embodiments, the transaction data comprises at least one of a merchant name, a merchant location, a transaction amount, a date of a transaction, a time of a transaction, a merchant category, a product category and a number of transactions. In some embodiments, there is a further step of tracking purchasing behavior of a second plurality of users whose transaction data met the at least one criterion…”, paragraphs 14, 20 and 224.
“[0236] Further, the system may provide offers/suggestions integrated [Examiner equates ingesting] in an electronic account statement of the user. The system may include a bookmarklet that enables displaying offers/rewards in-line with the transaction history of the user. The bookmarklet may be an applet that may be integrated with the browser to show in-line offers when a bank website, that may have the user's account, may be accessed….”, paragraph 236.
See also “[0023] In embodiments, methods and systems may comprise presenting an opportunity to assess alternative offerings related to a financial transaction from a user's financial account, wherein the financial transaction is related to a presently selected offering, in response to the selection of the opportunity, gathering transaction data relating to the presented selected offering and analyzing the transaction data[Examiner equates ingesting] , wherein the step of analyzing involves normalizing the transaction data [Examiner equates ingesting] such that a comparison to other offers can be assessed, collecting offer data relating to an alternative offering and normalizing the offer data such that a comparison with the normalized transaction data can be assessed, comparing the normalized transaction data with the normalized offer data to assess if the alternative offering presents an improvement to the user in comparison to the presently selected offering, and presenting the alternative offering to the user if the alternative offering presents an improvement….”, paragraph 23.
“…analysis may include evaluation of potential profitability to the financial institution based on cross-sell opportunity and customer profitability. These inferences may be leveraged from transactions and data known to the financial institution to deliver timely and perfectly matched cross-sell products [Examiner equates ingesting] . FIG. 69 depicts an example of a cross-sell for an auto-loan refinance based on an auto insurance transaction.”, paragraph 291.
“[0319] The machine learning process 7202 develops a model of customer spending habits including category preferences, geographic locations, seasonal variety, periodic purchases, recent changes from historic spending patterns and the like. The machine learning process 7202 may also develop weighting criteria relative to influence on customers spend behavior including a heavier weighting on recent transactions, extended changes in geography, and the like….”, paragraphs 319 and 385).
e2) reviewing, by the artificial intelligence algorithm, the transaction history of the user
using a geospatial analysis to identify one or more transactions made at a geographic location with access limited to the class of users eligible for the discount;
(“[0014] In some of these embodiments, the transaction data comprises at least one of a merchant name, a merchant location, a transaction amount, a date of a transaction, a time of a transaction, a merchant category, a product category and a number of transactions. In some embodiments, there is a further step of tracking purchasing behavior of a second plurality of users whose transaction data met the at least one criterion…”, paragraphs 14 and 20.
“[0026] In embodiments, methods and systems may comprise presenting a statement of a user's financial transaction data, where the financial transaction data were obtained from a financial institution that maintains a financial account on behalf of the user, and presenting a map of a geographic area and indicating where, within the geographic area, a savings opportunity is presented, wherein the savings opportunity relates to the financial transaction data….The geographic area may relate to a user's identified location. The user's location may be identified manually by the user. The user's location may be identified automatically from a mobile device implementing the method. …”, paragraph 26.
“…an additional incentive to accept the savings opportunity may be made when the user is in a geographic location set by a merchant offering the savings opportunity. The incentive may be at least one of an additional % discount, an additional monetary discount, an additional savings opportunity, the opportunity to share the savings opportunity, and a related opportunity”, paragraph 46.
“…targeting users with an offer may include selecting at least one reward, offer, or incentive to present to a user by: …, applying at least one rule dictated by a financial institution to the set of offers, and applying a filter to identify offers with a highest likelihood of being accepted by the user, and adjusting at least one parameter of the at least one offer prior to presentation to the user based on at least one characteristic of the user. The at least one parameter may be at least one of a minimum spending threshold, a discount amount, and a duration of a campaign, and a category of offer. The filter to identify offers with the highest likelihood of being accepted may be based on a predictive model of user purchase behavior developed using at least one of: data on one or more past user responses to one or more savings opportunities, public data relevant to the user, inferred data about the user, preferences selected from the group consisting of merchant category preferences, transaction category preferences, product category preferences, and merchant preferences, a geographic location, … a spending level, and a change from an historic spending pattern. Adjusting the at least one parameter may include calculating a spending trajectory based on a historical spending pattern …”, paragraph 55 and 401)
f1) reviewing, by the artificial intelligence algorithm, the transaction history of the user [using temporal pattern] recognition to identify at least one [ reoccurring transaction having a value consistent] with an applied discount limited to the class of users eligible for the discount;
(Kurapati teaches “…tracking purchasing behavior of the first plurality of users and the second plurality of users for a period of time before and after the savings opportunity was provided”, paragraph 16.
“…The filter may be a blacklist of at least one of a merchant, a merchant type, a transaction type, a time period, and a savings opportunity type. The filter may relate to a merchant offering a savings opportunity. The filter may relate to a past spend with the merchant, a past spend in a category, a past purchase frequency, a transaction, and a change in purchase pattern [Examiner interprets as reviewing temporal pattern] ”, paragraph 37.
“[0272] The decision engine 108 may apply factors in matching a savings opportunity to the user. For example, a financial institution may blacklist certain merchants, merchant types, transactions, transaction types, and the like from being used to match a purchase reward to the user. In another example, the financial institution or the merchant may use a spend pattern to match an offer to the user. In some embodiments, the offer may be made in conjunction with a display of spend pattern metrics. The spend pattern may be used to send alerts to the user regarding spend with a merchant, in a category, of a total amount, in a time period, and the like….”, paragraph 272.
“[0319] The machine learning process 7202 develops a model of customer spending habits including category preferences, geographic locations, seasonal variety, periodic purchases, recent changes from historic spending patterns and the like [Examiner interprets as reviewing temporal pattern] …”, paragraph 319.
“…FIG. 70A illustrates another embodiment, wherein the plurality of financial transaction data sets 7002 also includes historic customer data sets 7008, information on historic customer response to offers…”, paragraph 315 and Fig. 70A);
f2) refining, by the artificial intelligence algorithm, identification of eligible transactions, including:
([0272] The decision engine 108 may apply factors in matching a savings opportunity to the user. For example, a financial institution may blacklist certain merchants, merchant types, transactions, transaction types, and the like from being used to match [refining] a purchase reward to the user. In another example, the financial institution or the merchant may use a spend pattern to match an offer to the user. In some embodiments, the offer may be made in conjunction with a display of spend pattern metrics. The spend pattern may be used to send alerts to the user regarding spend with a merchant, in a category, of a total amount, in a time period, and the like…”, paragraph 272);
[f3) employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and]
f4) detecting [complex] transaction behaviors including deployment-related transaction patterns [consistent with military service]; and
(the Examiner notes that giving the broadest reasonable interpretation, Kurapati’s system teaches “…the at least one criterion is selected from the group consisting of a previous purchase of an item, an amount of one or more purchases of an item, a number of transactions, a frequency of transactions and a transaction category. In some embodiments, there is a further step of comparing tracking information about purchasing behavior of the first plurality of users and a second plurality of users whose transaction data met the at least one criterion …”, paragraph 15.
“[0016] In embodiments, systems and methods may track the behavior of more than one group of consumers [consistent with military service is a group of consumers] receiving different incentives. Another embodiment is a method for tracking financial transactions. The method includes steps of gathering transaction data from a plurality of user accounts by at least one computer, each user account a financial institution account at a financial institution, analyzing the transaction data for each user by the at least one computer for at least one criterion for a savings opportunity indication, and providing a first savings opportunity to a first plurality of users whose transaction data meet the at least one criterion….”, paragraph 16.
“[0047] In an embodiment, a method for identifying group members may include identifying demographic characteristics of members of a group via market research, identifying purchasing behaviors of the members of the group [military service is a group of consumers] via market research, correlating the identified demographic characteristics with the identified purchasing behaviors …”, paragraph 47);
g) determining a score indicating the probability of the user belonging to the class of users eligible for the discount.
(“0050] In an aspect, a method for identifying a member of a group with particular demographic characteristics may include identifying purchasing behaviors of members of a group with particular demographic characteristics via market research, gathering transaction data from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants, using the market research to set prior probabilities that a user is a member of the group in a Bayesian belief network, analyzing the transaction data of the user via a Bayesian belief network to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics, and presenting to the user at least one of an offer and a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics. …”, paragraph 50).
Although Kurapati teaches reoccurring transaction “…FIG. 70A illustrates another embodiment, wherein the plurality of financial transaction data sets 7002 also includes historic customer data sets 7008, information on historic customer response to offers…”, paragraph 315 and Fig. 70A.Kurapati does not expressly disclose
reoccurring transaction having a value consistent
f3) employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and
But, Strock’s system teaches, “… A portion or all of the Bank's customers may be enrolled or participating in any given program, depending on the eligibility rules of the program. A rewards database stores customer-specific reward information for each customer enrolled in the Promotional Rewards Program. Rewards such as rewards currency are automatically calculated based on the triggering behaviors performed by the customer, and earned rewards are awarded either upon demand of the customer, or automatically on an immediate or periodic basis…”, paragraph 31.
“…The Offer Qualification Module 52 may also be configured to allow for phased offers and phased rewards, where a customer earns a consistent or increasing level of a reward for performing a desired behavior (or transaction) over a longer period of time [reoccurring transaction having a value consistent]. For instance, according to one phased offer, a customer may earn 1000 points for spending $500 in June, 2000 points for spending $500 in July, and 3000 points for spending $500 in August, where the July points can be earned only if the June threshold was satisfied, where each successive phase of earnings can only be accrued if the prior phase is completed successfully (i.e., additional points in August can be earned only if the customer spent $500 in both June and July). Through another phased offer, a customer may earn 500 bonus miles each month for the next five months as long as the customer spends $500 per month (or makes a certain number of purchases)”, paragraph 218.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Strock’s teaching with the teaching of Kurapati. One would have been motivated to provide functionality to store consistent behaviors performed by the customer in order to trigger rewards.
Next, Bal discloses
f3) employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and
(“For instance, tracking patterns of usage is a known technique used to identify purchases or transactions that represent anomalies in the ordinary behavior of a particular consumer…”, paragraph 36).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bal’s teaching with the teaching of Kurapati. One would have been motivated to provide functionality to use techniques to detect anomalies in order to provide promotions (see Bal abstract).
As to claim 11, it comprises the same limitations than claim 1 above therefore is rejected in the same way, further the claim comprises one or more processors (see Kurapati Fig. 1 and associated disclosure); and non-transitory computer-readable storage media encoding instructions which, when execute by the one or more processors, cause the computer system (see at least Figs. 1-4 and associated disclosure).
As to claims 2 and 12, Kurapati discloses
wherein, when the score meets or exceeds a first threshold, further comprising prompting the user to confirm membership among the class of users eligible for the discount.
(“…applying a filter to identify offers with a highest likelihood of being accepted by the user, and adjusting at least one parameter of the at least one offer prior to presentation to the user based on at least one characteristic of the user. The at least one parameter may be at least one of a minimum spending threshold, a discount amount, and a duration of a campaign, and a category of offer. The filter to identify offers with the highest likelihood of being accepted may be based on a predictive model of user purchase behavior developed using at least one of: data on one or more past user responses to one or more savings opportunities [Examiner interprets as the user to confirm membership], public data relevant to the user, inferred data about the user,…”, paragraph 55).
As to claims 3 and 13, Kurapati discloses
wherein when the score meets or exceeds a second threshold, further comprising sending a request to a vendor to offer the user the discount on a future transaction
(“[0260] Tracking the purchasing behavior of the two or more groups, including those who received an offer …, allows the system to calculate and compare data, such as …total spend by group, … user spending below an offer dollar threshold for the different cohorts or groups…..This information may then be used to evaluate the … effectiveness of a particular campaign, not just with respect to participation in a single offer or transaction, but across time and potentially involving numerous transactions. This facilitates the merchant's understanding the impact of the offer on merchant revenues and allows the merchant to manipulate the various campaigns [Examiner interprets as sending a request to a vendor to offer the user the discount on a future transaction]to optimize its marketing budget”, paragraph 260).
As to claims 4 and 14, Kurapati discloses wherein when the score meets or exceeds a … threshold, further comprising sending a request to a vendor to apply a retroactive discount to a completed transaction.
(Kurapati discloses “…system may match the transaction…between the cardholder and a merchant with an offer available for users making transactions …[criteria]… and the like [Examiner interprets as sending a request to a vendor to apply a retroactive discount]. When the match is made, the user may be eligible for one or more offers, either retroactively based on the transaction or for future transactions”, paragraph 405. See also paragraph 260).
Kurapati does not expressly disclose second threshold, but from the teaching of processing a first threshold (..“user spending …[below or up]… an offer dollar threshold for the different cohorts or groups…”, paragraph 260), and Kurapati’s system process of criteria or rules to provide an incentive (see rules in abstract, criteria in paragraph 27) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a second threshold into the teaching of Kurapati and those in the art would have recognized that applying the known teaching in Kurapati’ system would have yielded predictable results.
Furthermore, regarding to second threshold; The Examiner notes that per MPEP 2144.04, mere duplication of parts or functionality has no patentable significance unless a new and unexpected result is produced. Again Kurapati teaches rules, abstract, criteria, paragraphs 27 and 260 to grant offers or savings opportunities.
As to claims 7 and 17, Kurapati discloses
wherein the financial product restricted to the class of users eligible for the discount includes at least one of a VA loan or military credit card.
(“…The user financial account is may be a credit card account, a bank account, a checking account, a savings account, a personal finance program account, a loan account, and the like. ..”, paragraph 23);
Claims 5-6, 8-9, 15-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20160086222 (Kurapati) in view of US PG. Pub. No. 20040122736 (Strock) in view of US PG. Pub. No. 20080147495 (Bal) ) and in view of US Patent No. 20020174013 (Freeman).
As to claims 5 and 15, Kurapati does not disclose but Freeman discloses,
wherein the class of users eligible for the discount includes at least one of veterans, senior citizens, students, educators, first responders, healthcare workers, government employees, employees of a nonprofit organization, members of a membership club, or employees of a particular company.
(Freeman that is in the business of “A method and system for providing advertisement information on chip cards, and for the distribution of the resulting revenues. It also includes tracking and storing of integrated relational information regarding advertisement….”, abstract.
Fereeman teaches “…focus a user profile or set of profiles will be developed that are believed to be the optimum group to which the chip card advertisement information should be targeted…”, paragraph 74 and claim 63.
“…0006] Chip cards can be used in two types of operating environments: closed systems or open systems. A closed system is managed in a contained environment where there is a single card issuer, who also acts as the sole service provider. A proprietary card is issued to customers of the service provider for exclusive use at its facilities. … Closed systems are typically used in applications such as transit systems, colleges and universities, public telephones, theme parks, military bases, prisons, and large corporations. …”, paragraph 6 .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate class of users eligible for discount as students or employees of a particular company into the teaching of Kurapati and those in the art would have recognized that applying the known teaching to Kurapati’ system would have yielded predictable results (see Freeman paragraph 6).
As to claims 6 and 16, Kurapati does not disclose but Freeman discloses,
wherein the organization restricted to the class of users eligible for the discount includes at least one of a branch of a military or a veterans assistance program.
Fereeman teaches
“…0006] Chip cards can be used in two types of operating environments: closed systems or open systems. A closed system is managed in a contained environment where there is a single card issuer, who also acts as the sole service provider. A proprietary card is issued to customers of the service provider for exclusive use at its facilities. … Closed systems are typically used in applications such as transit systems, colleges and universities, …, military bases, . …”, paragraph 6 .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a class of user such as branch of a military into the teaching of Kurapati and those in the art would have recognized that applying the known teaching to Kurapati’ system would have yielded predictable results (see Freeman paragraph 6).
As to claims 8 and 18, Kurapati does not disclose but Freeman discloses, wherein the financial institution or account restricted to the class of users eligible for the discount includes features tailored to the class of users eligible for the discount, such as reduced fees, preferential interest rates, or specialized customer support.
(“…The chip card may or may not incorporate an electronic display for showing the advertisement directly on the card. Credit transactional fees are adjusted based on advertising download rates and other parameters….”, abstract.
“… Revenue received by the Affinity Operator from advertisers may be shared with the merchant and other entities in the transactional network, by adjusting credit transactional fees based on download rates and other parameters (e.g., by discounting the fees in relation to the number of advertisements downloaded onto chip cards)…”, paragraph 21).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of reducing fees based on a criteria such as advertisement downloaded into the teaching of Kurapati and those in the art would have recognized that applying the known teaching to Kurapati’ system would have yielded predictable results (see Freeman paragraph 21 and abstract).
As to claims 9 and 19, Kurapati discloses
wherein the geographic location with access limited to the class of users eligible for the discount (“The step of matching may be limited to savings opportunities near a user's identified location. The user's location may be identified manually by the user. The user's location may be identified automatically from a mobile device implementing the method”, paragraph 21).
Kurapati does not disclose but Freeman teaches
includes at least one of a military base or government facility,
(“0006] Chip cards can be used in two types of operating environments: closed systems or open systems. …. Closed systems are typically used in applications such as…, military bases, prisons [government facility], …”, paragraph 6.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate geographic locations such as military bases and prisons [government facility], into the teaching of Kurapati and those in the art would have recognized that applying the known teaching to Kurapati’ system would have yielded predictable results (see Freeman paragraph 6).
Response to Arguments
Applicant’s arguments of 11/19/2025 have been very carefully considered but are not persuasive.
Rejection of claims 1-9 and 11-19 under 35 USC 101 is maintained because the prima facie of unpatentability established above.
Arguments regarding rejections under 35 U.S.C 103 are moot in light of the above new grounds of rejection above.
Applicant argues (remarks 7-10)
Claim Rejections - 35 U.S. C § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. This rejection is respectfully traversed…
The claims are patent eligible under Step 2A, Prong Two
because the claims are directed to a practical application of technology. MPEP 2106.04(d).
Further, the claims are patent eligible under Step 2B as being directed to significantly more
amounting to an inventive concept. MPEP 2106.05.
Independent claim 1, which is representative, is directed to a method for assessing a
probability of a user belonging to a class of users eligible for a discount. Claim 1 is amended to
recite:…
In response the Examiner asserts that a prima facie of unpatentability has been established. Further, the Examiner looked both the instant claims and the specification to elaborate Examiner's facially sufficient analysis above. The Examiner considered each limitation or element in the claims individually and as a whole according with the guidelines published in published in the MPEP 2103 through 2106.07(c).
The 101 analysis presented above is facially sufficient, and presents why the elements in the claims are considered to be insignificant extra-solution activity (See MPEP 2106.05(g) and also why the elements in the claims are considered to be no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
Amended claim 1 now recites detailed technical features that reflect the specific
improvements described in the specification. The claim requires ingesting a transaction history
by an artificial intelligence algorithm to train the algorithm, where the transaction history
includes specific data elements of timestamps, amounts, and locations associated with
transactions. Specification at 65. The claim further requires the artificial intelligence algorithm to
perform geospatial analysis to identify transactions at geographic locations with limited access,
and to employ temporal pattern recognition to identify recurring transactions. Specification at
65-66. Additionally, the claim now recites refining identification of eligible transactions through
anomaly detection techniques and detection of complex transaction behaviors including
deployment-related transaction patterns consistent with military service. Specification at 69.
In response the Examiner asserts the facially sufficient analysis above using the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c), established the abstract idea claimed in the instant claims. The Step 2A - Prong 1 concluded that there is a Judicial Exception recited in the claim. The method for assessing a probability of a user belonging to a class of users eligible for a discount. Considered directed to certain methods of organizing human activity, advertising, marketing or sales activities or behaviors.
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure: “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”. The limitations comprise generic recited elements, just software and data. Applicant makes sure the claim is an abstract idea because there is not in the recitation a computer, server or machine to perform the claimed method. Again the claimed method is just software and data.
These amendments transform the subject matter of amended claim 1 into a specific
technological solution employing multiple advanced analytical techniques. The artificial
intelligence algorithm is not invoked as a generic tool but rather as a specifically configured
component that performs integrated geospatial analysis, temporal pattern recognition, and
anomaly detection to solve a technical problem in the field of financial transaction analysis.
As explained in the July 2024 Subject Matter Eligibility Examples, claims that use
artificial intelligence to provide improvements over traditional methods of data analysis can be
patent eligible when the claim reflects the disclosed improvement….
In response the Examiner asserts the facially sufficient analysis above considered the alleged limitations as a whole and as individually. The additional limitations or features beside the abstract idea are “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”. The elements are claimed at very high level of generality. The combination of these additional elements were considered in the facially sufficient analysis no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea). Again, mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Eligibility Examples, Example 47, Claim 3. In Example 47, Claim 3, a method using an artificial
neural network to detect malicious network packets was found eligible because the claim
integrated the abstract idea into a practical application by improving network security. Id at 10-
13. The claim reflected technical improvements by reciting details of how the artificial neural
network aided in detecting anomalies and taking automated remediation actions.
Similarly, amended claim 1 recites specific details of how the artificial intelligence
algorithm performs geospatial analysis, temporal pattern recognition, and anomaly detection to…
In response the Examiner asserts that the instant claims are not similar to Example 47, claim 3.
Regarding to the elements “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, they do not impose any meaningful limits on practicing the abstract idea. The claim does not reflect any technical improvement on the functioning of the computer or technical field and details of how implement the abstract idea.
Here, amended claim 1 recites a particular solution using specific artificial intelligence
techniques. The claim does not merely recite the idea of identifying eligible users but rather
specifies the particular technological approach of training an artificial intelligence algorithm on
transaction data including timestamps, amounts, and locations, then using that trained algorithm
to perform geospatial analysis, temporal pattern recognition, anomaly detection, and detection of
complex transaction behaviors including deployment patterns.
Amended claim 1 also recites significantly more than the exception at Step 2B
In response the Examiner asserts that the under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
The additional elements “from an organization” ; “linked with a financial institution or account“; “ingesting, by an artificial intelligence algorithm, a transaction history of the user to train the artificial intelligence algorithm, the transaction history including timestamps, amounts, and locations associated with transactions in the transaction history; “by the artificial intelligence algorithm”; “using a geospatial analysis”; “using temporal pattern recognition”; “refining, by the artificial intelligence algorithm, identification of eligible transactions, including: employing anomaly detection techniques to identify outliers and irregularities in the transaction history; and detecting complex transaction behaviors including deployment-related transaction patterns consistent with military service”, in the instant claims were considered a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the instant claims. The claims are not eligible for patent protection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Artificial Intelligence for Futuristic Banking”. IEEE. 2021. This publication discloses “Artificial Intelligence (AI) has become an essential resource for large banks that deal with regulatory changes, new Anti-Money Laundering (AML) obligations and vulnerable fraud-prone clients. Cybersecurity has thus become a hot topic due to security failures using traditional methods and concerns about how companies use the personal data collected from clients or their regular users. The most obvious apparent reason why cybersecurity is critical in banking sector transactions is to protect client assets with a high level of data privacy. The main approaches in the front office conventional banking such as AI chatbots, smart virtual assistants and biometric user authentication are discovered to answer security challenges and to enhance prosperity in the field. Concurrently, advanced AI applications in fraud detection, fraud risk monitoring, anti-money laundering techniques and cross-border payments handling are observed under the back-office operations. The paper reviews the conceptualizations of privacy concerns and the antecedents and consequences of using AI-power in the banking sector. Moreover, overlooked limitations of AI such as scarcity of quality data, a rise of hidden-bias in suggestions and obliviousness of lacking knowledge are discussed with several thriving solutions.”
“Temporal pattern recognition”. IEEE. 1988. This paper discloses
“A network is described that takes as its input individual incoming events. Sequences of these events (letters, phonemes, or, more abstractly, object sightings in a vision system), received by the system over time are categorized as specific sequences by the temporal system. The temporal system produces Gaussian classifications that represent the statistics of the temporal data, self-developed classes. The system recognizes sequences in a noisy environment, giving as output a Gaussian distance from the stored sequence, thus providing an analog measure of closeness of fit to currently known patterns. The system can recognize sequences with missing or extraneous elements, as well as out-of-order sequences. In addition, a desirable prediction property-that the system realizes it may be in a particular sequence long before the entire sequence has been introduced- is a consequence of the multidimensional Gaussian distance calculation”.
Applicants’ 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA VICTORIA VANDERHORST whose telephone number is (571)270-3604. The examiner can normally be reached on M-F 8-4 hours from 9:00 AM-4:00 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashraf Waseem can be reached on 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA V VANDERHORST/ Primary Examiner, Art Unit 3621 3/11/2026