DETAILED ACTION
This is in reference to communication received 04 December 2025. Claims 1 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 11, representative of claims 1 and 20, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 11 recites invention directed to an entity like a financial institution temporarily increasing the available-credit-limit for their customer(s) whose transaction may exceed their available-credit-limit to help the customer make the transaction.
By referencing the collected spend information of a customer, average spend of a customer is mathematically calculated and an increase in the credit limit is performed by adjusting the current credit-limit (first-limit) to an increased-credit-limit (second-limit), and subsequent to receiving a response, chance the credit limit for the customer, and after certain period of time, credit limit of the user is reset to the actual credit limit of the user prior to the increase.
These limitations describe marketing/sales/advertising activities. Accessing the customer profile data and merchant data (such as from a file in a filing cabinet), determining that the customer may be making a purchase at the merchant, verifying whether the purchase will exceed the available credit-line extended to the customer, notifying the customer, and after the response is received, increase the credit-line for a short period of time. Increasing credit-limit for a customer for a short period of time to enable the customer make a purchase at a merchant would be a consumer-credit personnel providing a temporary authorization to increase the available credit to the customer to positively complete the transaction instead of denying the transaction.
The aforementioned claims recite additional functional elements that are associated with the judicial exception, including: applying of a predictive model to the average spend to determine a predicted utilization amount, identifying that a user’s utilization amount may result in exceeding the predicted utilization amount, temporary increase credit-limit based upon the location retrieved location of a mobile device of the customer; transmission of one or more notifications to adjust the user utilization amount from a first value to a second value for a second period of time, reverting (e.g. resetting) the temporary credit-line back to it original value are recited at a high-level of generality. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 1 and 20, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 20).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, 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 claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium 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. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
Claims 2 – 10 and 12 – 19 dependent on the aforementioned independent claims 1 and 11, and include all the limitations contained therein. These claims do not recite any additional technical elements, and simply disclose additional limitations that further limit the abstract idea with details defining when the increased-credit-limit will be reverted back to available-credit-limit, calculating average amount that customer spends at a particular location, what values will be considered to calculate the average spend value, flagging the customer account if their utilization exceeds a threshold amount. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Benkreira et al. US Publication 2020/0126087 in view of Huennekens et al. US Publication 2008/0195530 and Central Bank of Sri Lanka published article “Credit Card Guidelines No: 01/2010” hereinafter referred to as CBSL.
Regarding claim 11 and represented claims 1 and 20, Benkriera teaches location based available credit notification [Benkreira, 0004], system and method of secure prediction, comprising:
a memory (Benkreira, one or more memory devices storing instructions; and one or more processors in communication with one or more databases configured to execute the instructions) [Benkreira, 0004];
a computer readable non-transitory medium comprising computer executable instructions (Benkreira, a non-transitory computer-readable medium storing instructions executable by one or more processors to perform operations for location based available credit notification) [Benkreira, 0006]; and
a processor, wherein the processor is configured to [Benkreira, 0006]:
retrieving, by a processor, GPS coordinates from the mobile device associated with the user account (Benkreira, periodically send a location access request to user device 104 associated with customer 102 and receive a response from user device 104 which includes location and identity information. The received location may, for example, include real-time GPS coordinates of customer 102.) [Bankreira, 0020],
calculating, by the processor, an average spend over one or more route options for a first period of time based on the GPS coordinates
calculating, by the processor, an average spend over one or more route options for a first period of time based on the location information (Benkreira, the customer specific information and merchant data are accessed in the one or more database and wherein the customer specific information includes at least one of customer transaction data or customer mobility data; predict, by a prediction model using the customer specific information and the merchant data, if the customer is going to make a purchase and, if so, a monetary amount of the purchase; determine if the predicted monetary) [Benkreira, 0004];
applying, by the processor, machine learning algorithms to historical transactions data [Benkreira, 0004];
Benkreira does not explicitly teach customer location based (GPS coordinates based) location specific spending specifications to adjust the utilization amount for first value (e.g, available-current-limit to a second-value (e.g., increased-credit-limit) for a second period of time (e.g., for day of customer shopping spree only). However, Huennekens teaches system and method for processing credit card transactions that exceed a credit limit. Huennekens teaches making determination as to whether the potential transaction plus the account balance would exceed the credit limit by more than a predetermined amount, and conditions are met, transaction is approved.
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Benkreira by adopting teachings of Huennekens to help their customers successfully make their purchase or pay their bills at the time of emergency.
Benkreira in view of Huennekens teaches system and method comprising:
applying, by the processor, machine learning algorithms to historical transactions data specific to geographic locations corresponding to the GPS coordinates to generate location-specific spending predictions (Huennekens, a determination is made as to whether a potential transaction is for products associated with consumer emergencies or necessities, such as transactions involving a hotel, a car rental, an overseas transaction, or an emergency. …. a second determination will be made in step 402. In step 402, the determination is made as to whether the potential transaction plus the account balance would exceed the credit limit by more than a predetermined amount …. The potential transaction is approved (e.g. temporary increasing of credit-card limit to make the transaction complete) [Huennekens, 0032],
applying, by the processor, predictive model using neural networks to process location-correlated spending data from the location-specific spending predictions to determine a predicted utilization amount (Benkreira, based on a response from the user device to a location access request; determining an available credit associated with the customer …. predicting, by a prediction model using the customer specific information and merchant data, if the customer is going to make a purchase and, if so, a monetary amount of the purchase; determining if the predicted monetary amount of the purchase exceeds the available credit associated with the customer;) [Benkreira, 0006, 0004];
identifying, by the processor, one or more users that have a user utilization amount exceeding the predicted utilization amount (Benkreira, predicting, by a prediction model using the customer specific information and merchant data, if the customer is going to make a purchase and, if so, a monetary amount of the purchase; determining if the predicted monetary amount of the purchase exceeds the available credit associated with the customer) [Benkreira, 0006];
transmitting, by the processor, one or more notifications to adjust the user utilization amount from a first value to a second value for a second period of time (Huennekens, a determination is made as to whether a potential transaction is for products associated with consumer emergencies or necessities, such as transactions involving a hotel, a car rental, an overseas transaction, or an emergency. …. a second determination will be made in step 402. In step 402, the determination is made as to whether the potential transaction plus the account balance would exceed the credit limit by more than a predetermined amount …. The potential transaction is approved (e.g. temporary increasing of credit-card limit to make the transaction complete) [Huennekens, 0032];
receiving, by the processor, one or more responses that are responsive to the one or more notifications (Huennekens, The potential transaction is approved (e.g. temporary increasing of credit-card limit to make the transaction complete) [Huennekens, 0032];
automatically implementing, by the processor, temporary credit limit modification by adjusting user utilization amount to the second value through a secure API calls to financial databases (Huennekens, The potential transaction is approved (e.g. temporary increasing of credit-card limit to make the transaction complete) [Huennekens, 0032], and
automatically reverting, by the processor, the user utilization amount back to the first value using timer-based database triggers (Huennekens, a determination is made as to whether a potential transaction is for products associated with consumer emergencies or necessities, such as transactions involving a hotel, a car rental, an overseas transaction, or an emergency. …. a second determination will be made in step 402. In step 402, the determination is made as to whether the potential transaction plus the account balance would exceed the credit limit by more than a predetermined amount …. The potential transaction is approved (e.g. temporary increasing of credit-card limit to make the transaction complete, which clearly shows that time-to-live of the temporary credit increase is valid as long as the associated transaction is complete) [Huennekens, 0032].
Benkreira in view of Huennekens does not explicitly teach reverting user utilization amount back to the first value. However, CBSL teaches that the prevailing credit limit may be increased temporarily for a maximum limit of time [CBSL, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Benkreira in view of Huennekens by adopting teachings of CBSL and temporarily increase customer’s credit limit to enable the customer purchase product or service without embarrassment at merchant’s location.
Benkreira in view of Huennekens and CBSL teaches system and method further comprising:
reverting, by the processor, the user utilization amount back to the first value (CBSL, the prevailing credit limit may be increased temporarily for a maximum limit of time limit of 6 months) [CBSL, page 3].
Regarding claim 12 and represented claim 2, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising reverting, by the processor, the user utilization amount from the second value to the first value after expiration of the second period of time (CBSL, the prevailing credit limit may be increased temporarily for a maximum limit of time limit of 6 months) [CBSL, page 3].
Regarding claim 13 and represented claim 3, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising calculating, by the processor, an average spend for a geographic destination (Benkreira, the customer specific information and merchant data are accessed in the one or more database and wherein the customer specific information includes at least one of customer transaction data or customer mobility data; predict, by a prediction model using the customer specific information and the merchant data, if the customer is going to make a purchase and, if so, a monetary amount of the purchase (e.g., based on customers normal/average purchase at the merchant); determine if the predicted monetary amount of the purchase exceeds the available credit associated with the customer) [Benkreira, 0004].
Regarding claim 14 and represented claim 4, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising training, by the processor, the predictive model to take into account continuous acquisition of one or more parameters to determine the average spend (Benkreira, The customer specific information is used to iteratively train the prediction model and update the algorithm to predict the possible occurrence of a prospective purchase event associated with customer 102.) [Benkreira, 0024].
Regarding claim 9, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method, wherein the processor is further configured to increase the user utilization amount from the first value to the second value for the second period of time (CBSL, the prevailing credit limit may be increased temporarily for a maximum limit of time limit of 6 months) [CBSL, page 3].
Regarding claim 15 and represented claim 5, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method, wherein one or more parameters includes at least one selected from a group of a zip code, prior transaction information, spending pattern information, payment debt information, point of sale information, and dollar conversion information (Benkreira, Service provider system 108 will store customer specific information for customer 102 including but not limited to, for prior purchase transactions by customer 102, merchant location, time and date of purchase, monetary amount of purchase, merchant category, etc. This information is retrieved by prediction model 312 to learn routine behavior of customer 102 on the specific day of the week) [Benkreira, 0061].
Regarding claim 16 and represented claim 6, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method, further comprising flagging, by the processor, the user account if the user utilization amount exceeds a threshold associated with the average spend (Benkreira, Predicting the occurrence of a prospective purchase event includes predicting if customer 102 is going to make a purchase, a monetary amount of the purchase and predicting if the purchase may result in the purchase transaction exceeding the available credit of customer 102. If prediction model 312 predicts that the purchase will result in the purchase transaction exceeding the available credit, a prospective purchase event occurs and service provider system 108 sends a prospective purchase event notification to customer 102) [Benkreira, 0057].
Regarding claim 17 and represented claim 7, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising to adjusting, by the processor, the user utilization amount (CBSL, the prevailing credit limit may be increased temporarily for a maximum limit of time limit of 6 months) [CBSL, page 3] for at least one selected from a group of one or more merchants and one or more types of transactions (Benkreira, If customer 102 wishes to receive prospective purchase event notifications for certain merchants or merchant categories, customer 102 may be able to select specific ones of the displayed merchants or merchant categories) [Benkreira, 0050].
Regarding claim 18 and represented claim 8, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising increasing, by the processor, the user utilization amount from the first value to the second value for the second period of time (CBSL, the prevailing credit limit may be increased temporarily for a maximum limit of time limit of 6 months) [CBSL, page 3].
Regarding claim 19 and represented claim 10, as combined and under the same rationale as above, Benkreira in view of Huennekens and CBSL teaches system and method further comprising generating, by the processor, one or more future preferences upon completion of a transit associated with the one or more route options, the predictive model further taking into account the one or more future preferences to adjust the predicted utilization amount (Benkreira, Service provider system 108 will store customer specific information for customer 102 including but not limited to, for prior purchase transactions by customer 102, merchant location, time and date of purchase, monetary amount of purchase, merchant category, etc. This information is retrieved by prediction model 312 to learn routine behavior of customer 102 on the specific day of the week) [Benkreira, 0061].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because amended claimed invention as a whole, when analyzed in view of the specification, the claimed invention provide an improvement in aggregating data and performing predictive modeling to improve planning and the handling of unforeseen events is acknowledged and considered.
However, upon further review, it is deemed that the claimed invention is not eligible for patent under 35 USC 101, and examiner’s position is responded to in Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the amended invention as currently claimed is acknowledged and considered.
However, applicant is arguing amended claimed invention which have been responded to in response to pending amended claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Early et al. US Patent 7,389,266 teaches system and method for managing credit account products with adjustable credit limits.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
March 7, 2026