DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is the final office action in response to the arguments and remarks filed on August 28, 2025.
Claims 1, 3, 5, 7, 11, 13, 15, and 17 have been amended; claims 2, 4, 12, and 14 have been canceled.
Claims 1, 3, 5-11, 13, and 15-20 are pending and have been examined.
Responses to Arguments/Remarks
35 USC § 101:
The applicant contends that independent claims 1 and 11 recite technical steps that demonstrate the claimed invention recites significantly more than a judicial exception. The examiner respectfully disagrees.
The using of the debit limit to decide whether to authorize a transaction is only a business approach to authenticating a transaction. Identifying a trigger event for determining the debit limit is merely an improvement in the recited abstract idea, but it does not indicate improvement of computer functionality or any technology field. The machine learning is cited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept beyond the recited abstract idea. Additionally, the examiner does not rely on the conventional and well-known rationale.
The applicant further contents: “As amended, independent claims 1 and 11 include processing tasks perform by technical tools (i.e., machine learning) that are far beyond the capability of the human mind, and therefore cannot even remotely be characterized as an organization of human activity.” The examiner would like to point out that “Mental Processes” and “Certain Methods of Organizing Human Activity” are two separate groups of abstract ideas. Amended claim 1 as a whole is directed to determining a debit limit for a transaction authentication. In particular, the claim recites monitoring event data for an event, receiving streaming data, extracting features, determining the debit limit, storing the debit limit with an age indicator, determining whether the age indicator is below a threshold, utilizing the stored debit limit to perform a transaction authentication in response to the age indicator being below the threshold, and recalculating the debit limit and utilizing it to perform a transaction authentication in response to the age indicator being over the threshold. In other words, the claim falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas in Step 2A Prong One (MPEP 2106.04(a)(d)) because the claim involves the steps for determining a debit limit for a transaction authentication, which is a process associated with fundamental economic principles or practices, such as mitigating risk. The machine learning is an additional element that is cited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the machine learning is being used in its ordinary capacity.
Furthermore, the claims of the instant applicant are not similar to the claims of Case 1: Appeal 2018-007443, cited by the applicant. Claim 1 of the cited Case 1 recites “monitoring the operation of machines and for issuing calls for preventive maintenance and predictions of equipment failures,” which is not a process related to fundamental economic principles. Additionally, claim 1 of the cited Case 1 further recites more detailed steps of classifying the measurements and data obtained and presented to a jury, and “‘jury’ of classification engines each apply different techniques and method to analyze, interpret, and scrutinize identical parallel attribute sets, which replace an actual physical machine.” Claim 1 of the instant application as a whole is directed to determining a debit limit for a transaction authentication, which is a process related to fundamental economic principles, such as mitigating risk, an abstract idea under “Certain Method of Organizing Human Activity.” As discussed by the examiner, the machine learning is an identified additional element that is cited at a high level of generality and that does nothing more than mere instructions to apply the exception using a generic computer component.
Regarding Ex Parte Hannun (Appeal 3018-003323), cited by the application, the specification of Hannun describes: “Presented herein are embodiments of an end-to-end speech systems, which may be referred to herein as ‘DeepSpeech,’ where deep learning supersedes the multiple algorithms and hand-engineered processing stages of prior approaches. In embodiments, this approach, combined with a language model, achieves higher performance than traditional methods on hard speech recognition tasks while also being much simpler.” The additional element of machine learning in claim 1 of the instant application is cited at a high level of generality to extract the features from the streaming data and supply the extracted features to a business logic to determine a debit limit, which does not integrate the judicial exception into a practical application or recite significantly more than the abstract idea.
The claims of the instant application are not in any way similar to McRo as the claims do not make any technological improvement of any algorithms in performing improvement of animation techniques. Clearly, this is simply a gratuitous citation to a case that was held as eligible when the facts clearly argue against any kind of McRo improvement. The claims clearly are directed to determining a debit limit for a transaction authentication, which is an abstract idea. Furthermore, claims of the instant application are not similar to DDR Holdings. In the case of DDR Holdings, the claim addresses the issue of retaining Web site visitors from being diverted from a host’s web site to an advertiser’s Web site, for which “the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer network". Here, however, the instant claim is directed to an abstract idea of determining a debit limit for a transaction authentication. Unlike the situation in DDR Holdings, the applicant did not identify any problems particular to computer networks and/or the Internet that the claim allegedly overcomes.
As discussed above, claim 1 of the instant application as a whole is directed to determining a debit limit for a transaction authentication, which is a process related to fundamental economic principles, such as mitigating risk, an abstract idea under “Certain Method of Organizing Human Activity.” The machine learning is an additional element that is cited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer component. Therefore, the claim is not patent eligible.
35 USC § 103:
The applicant’s amendments have overcome the 35 USC § 103 rejection. However, there are new grounds of rejection necessitated by the applicant’s amendments as detailed in the 35 USC § 103 rejection section. Hence, the applicant’s arguments with respect to the claim rejection have been considered but are moot in view of the new grounds of rejection. New prior art is introduced.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1 recites “responsive to a subsequent transaction initiated via the value card, determining whether the age indicator is below a threshold; responsive to the age indicator being below the threshold, utilizing the stored debit limit in relation to a transaction authorization for a subsequent transaction,” and claim 11 recites “responsive to a subsequent transaction initiated via the value card, determine whether the age indicator is below a threshold; responsive to the age indicator being below the threshold, utilize the stored debit limit in relation to a transaction authorization for a subsequent transaction.”
The second underlined phrasing, “a subsequent transaction” in claims 1 and 11, should be respectively changed to “the subsequent transaction,” for more clarity.
Appropriate correction is required.
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, 3, 5-11, 13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In this instance, claims 1, 3, and 5-10 are directed to a method; claims 11, 13, and 15-20 are directed to an apparatus. Therefore, claims 1, 3, 5-11, 13, and 15-20 fall within the four statutory categories of invention.
Claim 1 as a whole is directed to determining a debit limit for a transaction authentication. In particular, the claim recites monitoring event data for an event, receiving streaming data, extracting features, determining the debit limit, storing the debit limit with an age indicator, determining whether the age indicator is below a threshold, utilizing the stored debit limit to perform a transaction authentication in response to the age indicator being below the threshold, and recalculating the debit limit and utilizing it to perform a transaction authentication in response to the age indicator being over the threshold. In other words, the claim falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas in Step 2A Prong One (MPEP 2106.04(a)(d)) because the claim involves the steps for determining a debit limit for a transaction authentication, which is a process associated with fundamental economic principles or practices, such as mitigating risk. More specifically, the following underlined claim elements recite abstract idea while the non-underlined claim elements recite additional elements according to MPEP 2106.04(a).
Claim 1 recites “[a] method for expediting speed and enhancing accuracy of debit limit determinations based on streaming data feeds, the method comprising: monitoring event data for an event trigger that, when received, directs a call to determine a debit limit for a customer, the event trigger being asynchronous with a card swipe of a value card; receiving streaming data from a plurality of arbitrary sources, the streaming data including features evaluated using business logic to determine the debit limit for the customer; employing machine learning to extract the features from the streaming data and supply the extracted features to the business logic; determining the debit limit based on the extracted features via the business logic responsive to the event trigger; storing the debit limit with an age indicator indicating an age of the debit limit; responsive to a subsequent transaction initiated via the value card, determining whether the age indicator is below a threshold; responsive to the age indicator being below the threshold, utilizing the stored debit limit in relation to a transaction authorization for a subsequent transaction; and responsive to the age indicator being over the threshold, initiating the trigger event to recalculate the debit limit and utilizing the recalculated debit limit in relation to the transaction authorization.”
This judicial exception is not integrated into a practical application because, when analyzed under Step 2A Prong Two (MPEP 2106.04(d)), the non-underlined additional elements — business logic and machine learning in claim 1 — perform the steps for determining a debit limit for a transaction authentication. These additional elements, in steps of extracting the features and determining the debit limit based on the extract features, are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. Merely adding generic computer components to perform the abstract idea does not integrate the abstract idea into a practical application. Claim 1 as a whole, judging from the additional elements individually and in combination, does not integrate the judicial exception into a practical application. The non-underlined additional elements above merely serve as tools to perform the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field; the claim does not apply the abstract idea with, or by use of, a particular machine; and the claim does not apply or use the abstract idea in some other meaningful ways beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, claim 1 as a whole fails to recite a practical application of the abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under Step 2B (MPEP 2106.05), using business logic and machine learning to perform the steps of extracting the features and determining the debit limit based on the extracted features amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept beyond the recited abstract idea. As discussed above, taking the additional elements separately, these additional elements perform the steps or functions that correspond to the actions required to perform the abstract idea. Therefore, the additional claim elements, when considered individually and in combination, fail to recite significantly more than the abstract idea.
Accordingly, claim 1 is rejected as being directed toward patent-ineligible subject matter.
Claim 11 recites the abstract idea similar to that discussed above in connection with claim 1. The newly identified additional elements of apparatus and a processing circuitry do not recite a practical application or significantly more than the abstract idea.
Claims 3, 5-10 and 13, and 15-20 have also been considered for subject-matter eligibility. However, these claims fail to recite patent-eligible subject matter for the following reasons:
Claims 3 and 13 recite an abstract idea of returning a decision regarding the attempt to authorize the subsequent transaction within one minute of receiving notice of the attempt, which falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas. No new additional elements are identified.
Claims 5 and 15 recite an abstract idea of modifying the debit limit by an error factor determined based on the age indicator, which falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas. No new additional elements are identified.
Claims 6 and 16 recite an abstract idea of determining the debit limit comprising determining a propensity for receiving an insufficient funds notification for a transaction request initiated by the customer over a range of transaction values and defining the debit limit at a transaction value corresponding to a threshold propensity, which falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas. No new additional elements are identified.
Claims 7 and 17 recite an additional element of wherein the machine learning module identifies the features based on pattern identification associations with multivariate optimization. The additional element fails to recite patent-eligible subject matter as it merely describes the characteristics of an additional element included in the abstract idea. The additional element is insufficient to integrate the abstract idea into a practical application because the additional element does not pertain to an improvement to the functioning of a computer or to any other technology or technical field. The additional element does not offer significantly more than the abstract idea, because the additional element merely describes the characteristics of an additional element included in the abstract idea.
Claims 8 and 18 recite an abstract idea of determining the propensity for receiving the insufficient funds notification, which falls under the “Certain Method of Organizing Human Activity” grouping of abstract ideas. The additional element of a time-survival model is insufficient to integrate the abstract idea into a practical application because the additional element does not pertain to an improvement to the functioning of a computer or to any other technology or technical field. The additional element does not offer significantly more than the abstract idea, because the additional element merely further recites additional instructions to implement the abstract idea on the computer components and/or generally links the use of a judicial exception to a particular technological environment or field of use.
Claims 9 and 19 recite an additional element of wherein the event trigger comprises a user onboarding event, linking a new account, completing an automated clearing house (ACH) transaction, or experiencing an insufficient funds event. The additional element fails to recite patent-eligible subject matter as it merely recites information included in the abstract idea. The additional element is insufficient to integrate the abstract idea into a practical application because the additional element does not pertain to an improvement to the functioning of a computer or to any other technology or technical field. The additional element does not offer significantly more than the abstract idea, because the additional element merely describes information included in the abstract idea.
Claims 10 and 20 recite an additional element of wherein the streaming data comprises user exposure signals, user history signals, credit report signals, user payment signals, and inter-institution signals. The additional element fails to recite patent-eligible subject matter as it merely recites information included in the abstract idea. The additional element is insufficient to integrate the abstract idea into a practical application because the additional element does not pertain to an improvement to the functioning of a computer or to any other technology or technical field. The additional element does not offer significantly more than the abstract idea, because the additional element merely describes information included in the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 5-11, 13, and 15-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “responsive to a subsequent transaction initiated via the value card, determining whether the age indicator is below a threshold; responsive to the age indicator being below the threshold, utilizing the stored debit limit in relation to a transaction authorization for a subsequent transaction; and responsive to the age indicator being over the threshold, initiating the trigger event to recalculate the debit limit and utilizing the recalculated debit limit in relation to the transaction authorization” and claim 11 recites “responsive to a subsequent transaction initiated via the value card, determine whether the age indicator is below a threshold; responsive to the age indicator being below the threshold, utilize the stored debit limit in relation to a transaction authorization for a subsequent transaction; and responsive to the age indicator being over the threshold, initiate the trigger event to recalculate the debit limit and utilize the recalculated debit limit in relation to the transaction authorization.”
The specification is silent with respect to these limitations. The specification discloses: “In an example embodiment, the debit limit 195 is saved along with an age indicator, which accounts for the time since the last limit determination (and therefore how old the debit limit 195 is). In some cases, the debit limit may further be modified by an error factor that may be determined based on the age indicator. In this regard, for example, the debit limit 195 may be reduced by some amount as the time since the last limit determination increases” (see paragraph [0061] of the specification). The specification discloses that the debit limit may be modified by an error factor that may be determined based on the age indicator. The specification does not disclose determining whether the age indicator is below a threshold or performing different actions based on whether the age indicator is below the threshold or not.
Dependent claims 3, 5-10, 13, and 15-20 are rejected because they depend on the rejected independent claims 1 and 11, respectively.
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.
Claims 1, 5, 9-11, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kievit (US 20250005583 A1) in view of Lee et al. (US 20160071200 A1), and further in view of Unbehagen (US 20080301043 A1).
Claims 1 and 11:
Kievit discloses the following:
an apparatus for expediting speed and enhancing accuracy of (credit) limit determination based on streaming data feeds, the apparatus comprising processing circuitry. (See paragraph [0010], “[i]n an example, a system comprises one or more processors and memory including instructions that, as a result of being executed by the one or more processors, cause the system to perform the processes described herein”; Figs. 1-3; paragraph [0036]; and paragraphs [0073]-[0074].)
monitoring event data for an event trigger that, when received, directs a call to determine a (credit) limit for a customer, the event trigger being asynchronous with a card swipe of a value card. (See Fig. 3; paragraphs [0075]-[0076], “[a]s noted above, the performance monitoring sub-system 202 may obtain (in real-time, in response to a triggering event, or at pre-defined time intervals) a credit evaluation associated with the user 112”; Fig. 5; paragraphs [0097]-[0098], “[a]s an illustrative example, based on a real-time evaluation of the performance and historical data associated with the secured payment instrument according to the one or more user metrics associated with the secured payment instrument, the graduation system 106 may increase the credit limit from $250 to $500, as illustrated in FIG. 5 through the credit limit increase 502”; Fig. 6; paragraphs [0102]-[0103], “[a]t step 602, the payment instrument application system may receive an application for a new secured payment instrument. The application may include information that may be used to perform a credit inquiry of the user's credit report to determine whether the user is qualified for a line of credit associated with a secured payment instrument”; and paragraphs [0106]-[0107], “the payment instrument application system may identify a set of user metrics that may be implemented to dynamically determine adjustments that may be made to the credit limit and/or security deposit associated with the secured payment instrument based on the credit performance associated with the user. As noted above, these user metrics may correspond to real-time usage of the secured payment instrument over time.”)
receiving streaming data from a plurality of arbitrary sources, the streaming data including features evaluated using business logic to determine the (credit) limit for the customer. (See paragraph [0008], “[i]n some embodiments, the computer-implemented method further comprises determining a set of user metrics for determining adjustments to the security deposit and the credit limit”; paragraph [0036], “[i]n an embodiment, the payment instrument application system 104 automatically submits a credit inquiry to a credit reporting service 110 to obtain a credit evaluation for the user 112. For instance, the result of the credit inquiry may include one or more credit scores associated with the user 112, the current amount of credit allocated to the user 112 from different financial entities (including the payment instrument service 102, if any), the current amount of credit available to the user 112, and the like”; Fig. 3; paragraphs [0075]-[0076], “[a]s noted above, the performance monitoring sub-system 202 may obtain (in real-time, in response to a triggering event, or at pre-defined time intervals) a credit evaluation associated with the user 112.… Additionally, the historical data may include any user metrics previously determined for the user 112 based on an initial credit evaluation and data corresponding to other user accounts (e.g., other payment instruments issued to the user 112, financial accounts maintained by the user 112, etc.). As noted above, these user metrics may correspond to real-time usage of the secured payment instrument over time”; Fig. 5; paragraphs [0097]-[0098]; Fig. 6; and paragraphs [0106]-[0107], “the payment instrument application system may identify a set of user metrics that may be implemented to dynamically determine adjustments that may be made to the credit limit and/or security deposit associated with the secured payment instrument based on the credit performance associated with the user.”)
employing machine learning to extract the features from the streaming data and supply the extracted features to the business logic. (See paragraph [0008], “[t]he set of user metrics are determined based on an initial credit evaluation corresponding to the user. The computer-implemented method further comprises updating the historical data according to changes in a performance associated with usage of the secured payment instrument over time. The changes are determined according to the set of user metrics”; paragraph [0042], “the payment instrument application system 104 implements the machine learning algorithm or artificial intelligence to dynamically, and in real-time, process any account characteristics corresponding to new secured payment instruments to assign a set of user metrics that can be used to dynamically determine whether to adjust the credit limits and/or security deposits associated with these new secured payment instruments”; and paragraphs [0106]-[0107], “[a]s noted above, these user metrics may correspond to real-time usage of the secured payment instrument over time. For example, a user metric may correspond to the user's ability to provide on-time payments for existing balances associated with the secured payment instrument over time…. Through implementation of the machine learning algorithm or artificial intelligence, the payment instrument application system may identify a particular cluster of similarly situated users from which a set of user metrics may be identified.”)
determining the (credit) limit based on the extracted features via the business logic responsive to the event trigger. (See paragraph [0008], “[t]he set of user metrics are determined based on an initial credit evaluation corresponding to the user. The computer-implemented method further comprises updating the historical data according to changes in a performance associated with usage of the secured payment instrument over time. The changes are determined according to the set of user metrics”; Fig. 3; paragraph [0075], “[a]s noted above, the performance monitoring sub-system 202 may obtain (in real-time, in response to a triggering event, or at pre-defined time intervals) a credit evaluation associated with the user 112”; paragraphs [0106]-[0107], “[a]s noted above, these user metrics may correspond to real-time usage of the secured payment instrument over time. For example, a user metric may correspond to the user's ability to provide on-time payments for existing balances associated with the secured payment instrument over time…. Through implementation of the machine learning algorithm or artificial intelligence, the payment instrument application system may identify a particular cluster of similarly situated users from which a set of user metrics may be identified”; and paragraphs [0122]-[0123], “[i]f the graduation system determines that the credit limit associated with the secured payment instrument may be increased based on the identified changes in performance related to usage of the secured payment instrument, the graduation system may, at step 808, automatically increase the credit limit associated with the secured payment instrument based on these identified changes…. The various criteria may also be defined according to the user metrics defined for the user and that are used to determine whether to adjust the credit limit and/or security deposit associated with the secured payment instrument.”)
store the (credit) limit. (See paragraph [0039], “[t]he dataset of account characteristics, in some instances, may include data corresponding to user metrics assigned to secured payment instruments issued to the sample users, any adjustments made to credit limits and/or security deposits based on transaction data associated with these secured payment instruments and the assigned user metrics, and the like”; paragraph [0068], “the graduation algorithm 206 may automatically update the user accounts datastore 108 to reflect this change to the credit limit associated with the secured payment instrument”; and paragraphs [0097]-[0098].)
Kievit does not explicitly disclose a debit limit. Additionally, Kievit does not explicitly disclose the following:
storing the debit limit with an age indicator indicating an age of the debit limit;
responsive to a subsequent transaction initiated via the value card, determining whether the age indicator is below a threshold;
responsive to the age indicator being below the threshold, utilizing the stored debit limit in relation to a transaction authorization for a subsequent transaction; and
responsive to the age indicator being over the threshold, initiating the trigger event to recalculate the debit limit and utilizing the recalculated debit limit in relation to the transaction authorization.
However, Lee, an analogous art of determining a spending limit of a payment card, discloses the following:
determining a debit limit (i.e., a spending limit associated with a debit card) based on collected data. (See paragraph [0023], “[p]ayment cards may include credit cards, debit cards, charge cards, stored-value cards, prepaid cards, fleet cards, virtual payment numbers, virtual card numbers, controlled payment numbers, etc.”; paragraph [0027], “[t]he payment card 104 may be associated with a transaction account for which the consumer 102 may want to establish a budget or impose one or more spending limits”; paragraph [0029], “[f]or instance, the consumer 102 may conduct payment transactions at merchant point of sale devices 110 using the payment card 104 for which a budget or spending limit is to be established”; and paragraphs [0043]-[0044].)
storing the debit limit (that is associated with a time indicator). (See Fig. 3; paragraph [0050], “[i]n some embodiments, an account profile 210 may also include a spending limit 310 and a spending target 312. The spending limit 310 may be a limit imposed on a spending category for a period of time, based on a budget generated by the processing unit 204 in response to a received spending limit request for the account profile 210”; Fig. 5; and paragraphs [0058]-[0062], “[i]n step 510, the processing unit 204 may determine if the period of time for the currently applied spending limit has expired.” A time indicator must be associated with a spending limit in order to determine whether the period of time for the spending limit has expired.)
responsive to a subsequent transaction initiated via the value card, determining whether the time indicator is below a threshold. (See Fig. 5; paragraphs [0058]-[0062], “[i]n some embodiments, the processing unit 204 may be configured to apply the spending limit to the payment card 104 as a spending control…. In step 508, the processing unit 204 may wait and monitor for new payment transactions to be initiated by the consumer 102 using the payment card 104. In step 510, the processing unit 204 may determine if the period of time for the currently applied spending limit has expired.”)
responsive to the time indicator being below the threshold, utilizing the stored debit limit in relation to a transaction authorization for a subsequent transaction. (See Fig. 5; paragraphs [0060]-[0062], “[i]n step 510, the processing unit 204 may determine if the period of time for the currently applied spending limit has expired. If the period of time has not expired, then, in step 512, the processing unit 204 may continue to monitor until an authorization request for a payment transaction is received. Once the authorization request is received by the receiving unit 202, the processing unit 204 may determine if the spending limit is exceeded based on the transaction amount for the payment transaction as indicated in the authorization request.”)
responsive to the time indicator being over the threshold, initiating the trigger event to recalculate the debit limit and utilizing the recalculated debit limit in relation to the transaction authorization. (See Fig. 5; paragraphs [0060]-[0062], “[i]n step 510, the processing unit 204 may determine if the period of time for the currently applied spending limit has expired…. Once the period of time has expired, then, in step 522, the processing unit 204 may decrease the spending limit based on the earlier calculated budget…. If the spending target has not been reached, the process may return to step 508 for monitoring regarding the lower spending limit and a new period of time.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Lee in the Kievit system. Moreover, in order to improve the flexibility of the Kievit system, one of ordinary skill in the art would have been motivated to determine the debit limit for a debit card, to set a period of time for a debit limit, and to authenticate a transaction based on the debit limit if the period of time has not expired or to recalculate the spending limit if the period of time has expired, so that the flexibility of system can be improved by implementing a period of time for a spending limit.
Lee discloses determining whether a period of time for a spending limit has expired. A time indicator must be associated with a spending limit in order to determine whether the period of time for the spending limit has expired. The examiner introduces another reference, Unbehagen, an analogous art of determining a spending limit of a payment card, discloses storing the debit limit that is associated with an age indicator indicating an age of the debit limit. (See paragraphs [0017]-[0018], “[a] debit card is a card that when used to make a purchase or cash withdrawal, the corresponding purchase amount is deducted from a financial institution account that is linked to the debit card…. For example, a customer can specify a $1000 per month spending limit and a $10,000 per year spending limit. After a durational spending limit is set, the bank will decline authorization requests for purchases that would cause the spending limit to be exceeded during the corresponding time period”; paragraph [0020], “[i]n another embodiment, the durational spending limit is non-periodic in that the spending limit expires at the end of the specified duration and is no longer used to limit a customer's spending. In yet another embodiment, a customer can specify whether a durational spending limit is to be periodic or non-periodic”; Fig. 2A; and paragraph [0029], “[t]he customer can also specify a spending period (e.g., 30 days), and a starting date (e.g., Apr. 1, 2006) for the durational spending period.” These citations indicate that the starting date could indicate an age of the debit limit and the expiration of the spending limit must be determined based on the starting date and the spending period (i.e., 30 days).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Unbehagen in the Kievit system as modified. Moreover, in order to improve the accuracy of the Kievit system as modified, one of ordinary skill in the art would have been motivated to store an age indicator with a debit limit for a debit card, so that the system can accurately determine whether the spending limit has expired.
Claims 5 and 15:
Kievit in view of Lee and Unbehagen discloses limitations shown above.
Lee discloses wherein the debit limit is modified by an error factor determined based on a time indicator. (See Fig. 5; paragraphs [0060]-[0062], “[i]n step 510, the processing unit 204 may determine if the period of time for the currently applied spending limit has expired…. Once the period of time has expired, then, in step 522, the processing unit 204 may decrease the spending limit based on the earlier calculated budget…. If the spending target has not been reached, the process may return to step 508 for monitoring regarding the lower spending limit and a new period of time.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Lee in the Kievit system. Moreover, in order to improve the flexibility of the Kievit system, one of ordinary skill in the art would have been motivated to recalculate the spending limit if the period of time has expired, so that the flexibility of system can be improved by implementing a period of time for a spending limit.
Unbehagen discloses an age indicator. (See paragraphs [0017]-[0018]; Fig. 2A; and paragraph [0029], “[t]he customer can also specify a spending period (e.g., 30 days), and a starting date (e.g., Apr. 1, 2006) for the durational spending period.” These citations indicate that the starting date could indicate an age of the debit limit and the expiration of the spending limit must be determined based on the starting date and the spending period (i.e., 30 days).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Unbehagen in the Kievit system as modified. Moreover, in order to improve the accuracy of the Kievit system as modified, one of ordinary skill in the art would have been motivated to store an age indicator with a debit limit for a debit card, so that the system can accurately determine whether the spending limit has expired.
Claims 9 and 19
Kievit in view of Lee and Unbehagen discloses limitations shown above.
Kievit further discloses wherein the event trigger comprises a user onboarding event, linking a new account, completing an automated clearing house (ACH) transaction, or experiencing an insufficient funds event. (See Fig. 6 and paragraph [0102].)
Claims 10 and 20:
Kievit in view of Lee and Unbehagen discloses limitations shown above.
Kievit further discloses wherein the streaming data comprises user exposure signals, user history signals, credit report signals, user payment signals, and inter-institution signals. (See paragraph [0031]; paragraph [0036]; paragraph [0049], “[t]or instance, the graduation system 106 may track, in real-time, transaction data, historical data, credit evaluations from the credit reporting service 110, and assigned user metrics associated with the secured payment instrument 116 and other issued secured payment instruments in order to update a dataset used to train the machine learning algorithm or artificial intelligence implemented by the graduation system 106”; and paragraph [0107], “[t]he machine learning algorithm or artificial intelligence may be trained to identify correlations amongst users to create a set of clusters that may be used to identify similar account holders and corresponding payment instruments based on one or more vectors (e.g., credit scores, changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance, demographic information, payment instruments issued, payment instruments in default, etc.)…. Through implementation of the machine learning algorithm or artificial intelligence, the payment instrument application system may identify a particular cluster of similarly situated users from which a set of user metrics may be identified.” Examiner’s Note: The types of data included in the streaming data do not impact the scope of the claim, as the types of data included in the streaming data do not affect the steps of actions of the claim components in any way.)
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kievit (US 20250005583 A1) in view of Lee et al. (US 20160071200 A1), and further in view of Unbehagen (US 20080301043 A1) and John et al. (US 20240272906 A1).
Claims 3 and 13:
Kievit in view of Lee and Unbehagen discloses limitations shown above.
Lee discloses authorizing the subsequent transaction. (See paragraph Fig. 5; paragraphs [0058]-[0062].)
None of Kievit, Lee, and Unbehagen explicitly discloses returning a decision regarding the attempt to authorize the subsequent transaction within one minute of receiving notice of the attempt.
However, John, an analogous art of processing transaction based on the spending limit, discloses returning a decision regarding the attempt to authorize the subsequent transaction within one minute of receiving notice of the attempt. (See paragraph [0051], “[t]he authorizer data store 165 is a data store for the authorizer server 160. The authorizer data store 165 stores data that are relevant for the authorizer server 160 to make decisions on approving fund transfer transactions…. The approval of a fund transfer transaction such as a card payment often is a quick decision process that requires the authorizer server 160 to respond within a time limit that is within a few seconds or within milliseconds. The authorizer data store 165 replicates the relevant data from data store 115 to reduce the latency in data retrieval and transfer of the data.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of John in the Kievit system as modified. Moreover, in order to improve the transaction processing speed of the Kievit system as modified, one of ordinary skill in the art would have been motivated to authorize a transaction based on the stored data, so that the system can approve a fund transfer transaction within a few seconds.
Claims 6, 8, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kievit (US 20250005583 A1) in view of Lee et al. (US 20160071200 A1), and further in view of Unbehagen (US 20080301043 A1) and Vergari (US 20170061534 A1).
Claims 6 and 16:
Kievit in view of Lee and Unbehagen discloses limitations shown above.
Kievit discloses determining the spend limit based on transaction history associated with the customer. (See paragraph [0008], Fig. 3, paragraph [0075], paragraphs [0106]-[0107], and paragraphs [0122]-[0123].)
Lee discloses determining the debit limit. (See paragraph [0023], paragraph [0027], paragraph [0029], and paragraphs [0043]-[0044].)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Lee in the Kievit system. Moreover, in order to improve the flexibility of the Kievit system, one of ordinary skill in the art would have been motivated to determine the debit limit for a debit card, so that the flexibility of the system can be improved by applying the spending limit on different types of cards.
None of Kievit, Lee, and Unbehagen explicitly discloses wherein determining the debit limit comprises determining a propensity for receiving an insufficient funds notification for a transaction request initiated by the customer over a range of transaction values and defining the debit limit at a transaction value corresponding to a threshold propensity.
However, Vergari, an analogous art of analyzing transaction data, discloses wherein determining the spending limit comprises determining a propensity for receiving an insufficient funds notification for a transaction request initiated by the customer over a range of transaction values and defining the spending limit at a transaction value corresponding to a threshold propensity. (See paragraph [0017], “[t]ypically, non-sufficient funds declines are associated with debit card transactions rather than credit card transactions because a debit account is more susceptible to having insufficient funds (on debit held on the account or exceeding arranged overdraft) than a credit account, which is more likely to allow an account holder to exceed an agreed credit limit on the basis that repayments will also be subject to interest repayments”; Figs. 1-2; and paragraphs [0037]-[0038], “[i]n a first scenario, the likelihood of a non-sufficient funds decline occurring during a subsequent individual outcome window for that individual is over the predetermined threshold and so the issuer does not issue a credit card (S26A). Although not shown in FIG. 2, the issuer may alternatively choose to impose a different measure such as reducing credit limit on an existing account…. Although not shown in FIG. 2, the issuer may alternatively choose to impose a different measure such as increasing credit limit on an existing account.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the subject matter of Vergari in the Kievit system as modified. Moreover, in order to improve the assessing of credit risk of individual account holders of the Kievit system as modified, one of ordinary skill in the art would have been motivated to determine a propensity associated with insufficient funds decline and to determine a spending limit at a transaction value corresponding to a threshold propensity, so that the spending limit can be adjusted based on the assessing of the credit risk.
Claims 8 and 18:
Kievit in view of Lee, Unbehagen, and Vergari discloses limitations shown above.
Vergari further discloses wherein the propensity for receiving the insufficient funds notification is determined based on a time-survival model. (See Figs. 1-2; paragraphs [0029]-[0032], “however, there may be a period of time between the observation window and the outcome window, for example, one month…. In this step, the one or more rules comprise at least one algorithmic model based on the correlation between normalised indices of card activity of the plurality of