DETAILED ACTION
In a communication received on 1 April 2026, the applicants amended claims 1, 8, and 15.
Claims 1-20 are pending.
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 9,171,301 B1) in view of Enzaldo et al. (US 2013/0132275 A1), and further in view of Ramesh et al. (US 2021/0304204 A1) in view of Aaron et al. (US 2013/0082103 A1).
With respect to claim 1, Gupta discloses: a system for implementing location-based validation in data item transfer approval process (i.e., a mobile transaction system that uses device location/proximity to decide whether to fulfill, disallow, or add security to a transfer request in Gupta, col. 1 line 65 - col. 2 lines 9), comprising:
a memory configured to store (i.e., processors and memory storing applications/data, including a mobile payment application used to make, authorize, and interact with transfer accounts in Gupta, col. 4 lines 20-42); and
a processor, operably coupled to the memory (i.e., processor coupled to memory in Gupta, col. 4 lines 20-42) and configured to:
receive a request message that indicates to transfer a data item (i.e., receiving a payment request from a first mobile device for transfer of funds to a second account holder in Gupta, col. 6 lines 51-62);
determine a second location data associated with the sender of the data item, wherein the second location data indicates a current location of a first user device from which the request message is received (i.e., sender-device current location information in the payment request, including GPS, geographic, or deducible location data in Gupta, col. 6 line 63 - col. 7 line 10);
determine a fourth location data associated with the receiver of the data item, wherein the fourth location data indicates a current location of a second user device associated with the receiver (i.e., querying the requested payee mobile device and receiving second location coordinates or information for that receiver device in Gupta, col. 7 lines 24-32).
Gupta discloses authorizing transactions based on the physical relationship between the mobile devices (col. 4 lines 43-54). Gupta do(es) not explicitly disclose the following. Enzaldo, in order to improve risk assessment based on aggregation of prior transaction data (¶0031, ¶0047, ¶0049), discloses:
a training dataset that comprises a set of historical data item transfers (i.e., historical money-transfer records and tri-state transaction dispositions‚ approved, rejected, or held in Enzaldo, ¶0047, ¶0043, ¶0092-0093); and
a set of rules, each of the set of rules indicates a circumstance that a data item transfer request is be approved assigned a warning indication, or denied based at least in part upon location data associated with a sender and a receiver of a respective data item (i.e., rules engines using historical, geographic, sender/recipient, and transaction characteristics to approve, reject, or hold money transfers in Enzaldo, ¶0027-¶0029, ¶0047, ¶0064-¶0065, ¶0077), wherein
to determine whether the request message is anomalous based at least in part upon the training dataset and the set of rules, the processor is further configured to: determine an initial status of the request message as one of approved, denied, or warning (i.e., real-time transaction dispositions‚ approved, rejected, or held in abeyance pending further information/investigation in Enzaldo, ¶0047, ¶0077); and
in response to determining that the initial status of the request message is warning (i.e., real-time transaction dispositions‚ approved, rejected, or held in abeyance pending further information/investigation in Enzaldo, ¶0047, ¶0077):
determine a historical data transfer pattern associated with the sender and the receiver (i.e., consumer-identification and aggregation passages use prior transactions to determine historical patterns such as frequency, totals, recipients, countries, and time periods in Enzaldo, ¶0062, ¶0064); and
determine whether the determined data transfer pattern corresponds to an anomalous pattern, comprising transferring more than a threshold number of data items in less than a threshold period of time (i.e., thresholds for total transaction amount and frequency over a specified time period, matching the anomalous threshold pattern in Enzaldo, ¶0064); and
in response to determining that the request message is not anomalous, grant the request message. (i.e., completes/approves money transfers when risk evaluation finds no unacceptable risk in Enzaldo, ¶0069, ¶0077).
Based on Gupta in view of Enzaldo, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Enzaldo to improve upon those of Gupta in order to improve risk assessment based on aggregation of prior transaction data.
Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
each of the set of historical data item transfers is labeled with a granted, warning, or denied indication (i.e., supervised ML/deep-learning training on transaction data, labeled/known classifications, flagged-transaction review, and iterative retraining in Ramesh, ¶0016, ¶0033);
determine whether the request message is anomalous based at least in part upon the training dataset and the set of rules (i.e., supervised ML/deep-learning classification using transaction features and labeled/flagged training data in Ramesh, ¶0016, ¶0033).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
Gupta discloses authorizing transactions based on the physical relationship between the mobile devices (col. 4 lines 43-54). Gupta, Enzaldo, Ramesh, and Aaron do(es) not explicitly disclose the following. Aaron, in order to improve transaction verification by correlating wireless terminal with purchasing location (¶0006, ¶0045, ¶0053), discloses:
determine a first location data associated with a sender of the data item, wherein the first location data indicates a location where the sender is registered to and opened a software application that is configured to facilitate transferring the data item, wherein the first location data comprises a first longitude and latitude coordinate (i.e., profile-stored registered purchasing locations, such as home/office, tied to a cardholder profile and used with terminal location for authorization analogous to the intended use of a user registering/opening a transfer software application in Aaron, ¶0043, ¶0047);
determine a third location data associated with a receiver of the data item, wherein the third location data indicates a location where the receiver has registered to and opened the software application (i.e., profile-stored registered purchasing locations, such as home/office, tied to a cardholder profile and used with terminal location for authorization analogous to a user registering/opening a transfer software application. in Aaron, ¶0043, ¶0047), wherein
determining that the initial status of the request message is warning comprises determine that the first location data is associated with a first authorized location, the second location data is associated with a first unauthorized location, the third location data is associated with a second unauthorized location, and the fourth location data is associated with a second authorized location (i.e., checking if user wireless device close to purchasing location; authorizing transaction based on comparing device and registered location suggests analogous scenarios of applying the location authorization check in Aaron, ¶0043, ¶0046-¶0047, ¶0055-¶0056, ¶0060).
Based on Gupta in view of Enzaldo and Ramesh, and further in view of Aaron, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Aaron to improve upon those of Gupta in order to improve transaction verification by correlating wireless terminal with purchasing location.
With respect to claim 2, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
the system of claim 1, wherein determining that the request message is not anomalous based at least in part upon the training dataset and the set of rules comprises identifying a rule, from among the set of rules (i.e., risk rules for flagging specific country accounts / transaction activity from marked countries in Ramesh, ¶0032).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
Gupta discloses authorizing transactions based on the physical relationship between the mobile devices (col. 4 lines 43-54). Gupta, Enzaldo, Ramesh, and Aaron do(es) not explicitly disclose the following. Aaron, in order to improve transaction verification by correlating wireless terminal with purchasing location (¶0006, ¶0045, ¶0053), discloses:
that indicates senders associated with the first location data and the second location data are allowed to transfer data items to receivers associated with the third location data and the fourth location data (i.e., if the request indicates that the determined profile of the credit card transaction includes an address and that the wireless terminal location is sufficiently in proximity to the registered purchasing location in Aaron, ¶0055).
Based on Gupta in view of Enzaldo and Ramesh, and further in view of Aaron, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Aaron to improve upon those of Gupta in order to improve transaction verification by correlating wireless terminal with purchasing location.
With respect to claim 3, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
the system of claim 1, wherein determining that the request message is not anomalous based at least in part upon the training dataset and the set of rules comprises:
determining that the first location data is associated with approved historical data item transfers (i.e., determining patterns including the shipping/billing address of the for the sender of the payment in Ramesh, ¶0015);
determining that the second location data is associated with approved historical data item transfers (i.e., flagging of known specific country accounts in Ramesh, ¶0032);
determining that the third location data is associated with approved historical data item transfers (i.e., determining counterparty country code is prohibited in Ramesh, ¶0015); and
determining that the fourth location data is associated with approved historical data item transfers. (i.e., determining and comparing to countries marked for known fraudulent activity in Ramesh, ¶0032).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
With respect to claim 4, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
the system of claim 1, wherein the processor is further configured to determine that the request message is anomalous based at least in part upon the training dataset and the set of rules, comprising:
determining that the first location data is associated with approved (i.e., valid transaction indicated as no money laundering or fraud in Ramesh, ¶0030)
historical data item transfers (i.e., determining patterns including the shipping/billing address of the for the sender of the payment in Ramesh, ¶0015);
determining that the second location data is associated with approved historical data item transfers (i.e., flagging of known specific country accounts in Ramesh, ¶0032);
determining that the third location data is associated with denied historical data item transfers (i.e., determining counterparty country code is prohibited in Ramesh, ¶0015); and
determining that the fourth location data is associated with denied historical data item transfers (i.e., determining and comparing to countries marked for known fraudulent activity in Ramesh, ¶0032).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
With respect to claim 5, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
the system of claim 1, wherein the processor is further configured to determine that the request message is anomalous based at least in part upon the training dataset and the set of rules, comprising:
determining that the first location data is associated with denied (i.e., training data includes classifications of transactions that are prohibited associated with certain transaction metadata in Ramesh, ¶0030)
historical data item transfers (i.e., determining patterns including the shipping/billing address of the for the sender of the payment in Ramesh, ¶0015);
determining that the second location data is associated with denied historical data item transfers (i.e., flagging of known specific country accounts in Ramesh, ¶0032);
determining that the third location data is associated with approved historical data item transfers (i.e., determining counterparty country code is prohibited in Ramesh, ¶0015); and
determining that the fourth location data is associated with approved historical data item transfers (i.e., determining and comparing to countries marked for known fraudulent activity in Ramesh, ¶0032).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
With respect to claim 6, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses:
the system of claim 1, wherein the processor is further configured to determine that the request message is anomalous (i.e., training data classified historical transactions as prohibited according to a plurality transaction metadata in Ramesh, ¶0030) based at least in part upon the training dataset and the set of rules (i.e., the training includes weights and values corresponding to risk rules in Ramesh, ¶0032), comprising:
determining that the first location data is associated with denied (i.e., training data includes classifications of transactions that are prohibited associated with certain transaction metadata in Ramesh, ¶0030);
historical data item transfers (i.e., determining patterns including the shipping/billing address of the for the sender of the payment in Ramesh, ¶0015);
determining that the second location data is associated with denied historical data item transfers (i.e., flagging of known specific country accounts in Ramesh, ¶0032);
determining that the third location data is associated with denied historical data item transfers (i.e., determining counterparty country code is prohibited in Ramesh, ¶0015); and
determining that the fourth location data is associated with denied historical data item transfers. (i.e., determining and comparing to countries marked for known fraudulent activity in Ramesh, ¶0032)
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
With respect to claim 7, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses: the system of claim 1, wherein:
determining the third location data is in response to communicating a third API call to the second user device, wherein the third API call indicates to provide a location where the receiver has registered to the software application (i.e., registration information of consumers and merchants includes name, address etc., service provider delivers transaction datasets for processing and flagging the data including merchant/consumer address in Ramesh, ¶0013, ¶0028).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
Gupta discloses authorizing transactions based on the physical relationship between the mobile devices (col. 4 lines 43-54). Gupta, Enzaldo, Ramesh, and Aaron do(es) not explicitly disclose the following. Aaron, in order to improve transaction verification by correlating wireless terminal with purchasing location (¶0006, ¶0045, ¶0053), discloses:
determining the first location data is in response to communicating a first application programming interface (API) call to the first user device, wherein the first API call indicates to provide the location where the sender has registered to the software application (i.e., retrieving a registered purchasing location corresponding to the cardholder and/or associated wireless terminal in Aaron, ¶0042, ¶0043, ¶0045);
determining the second location data is in response to communicating a second API call to the first user device, wherein the second API call indicates to provide a current location of the first user device (i.e., determining geolocation of the wireless terminal associated with the cardholder in Aaron, ¶0066); and
determining the fourth location data is in response to communicating a fourth API call to the second user device, wherein the fourth API call indicates to provide a current location of the second user device (i.e., performing geolocation of the IP address of the computer terminal where transaction initiated in Aaron, ¶0066).
Based on Gupta in view of Enzaldo and Ramesh, and further in view of Aaron, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Aaron to improve upon those of Gupta in order to improve transaction verification by correlating wireless terminal with purchasing location.
With respect to claim 8, the limitation(s) of claim 8 are similar to those of claim(s) 1. Therefore, claim 8 is rejected with the same reasoning as claim(s) 1.
With respect to claim 9, the limitation(s) of claim 9 are similar to those of claim(s) 2. Therefore, claim 9 is rejected with the same reasoning as claim(s) 2.
With respect to claim 10, the limitation(s) of claim 10 are similar to those of claim(s) 3. Therefore, claim 10 is rejected with the same reasoning as claim(s) 3.
With respect to claim 11, the limitation(s) of claim 11 are similar to those of claim(s) 4. Therefore, claim 11 is rejected with the same reasoning as claim(s) 4.
With respect to claim 12, the limitation(s) of claim 12 are similar to those of claim(s) 5. Therefore, claim 12 is rejected with the same reasoning as claim(s) 5.
With respect to claim 13, the limitation(s) of claim 13 are similar to those of claim(s) 6. Therefore, claim 13 is rejected with the same reasoning as claim(s) 6.
With respect to claim 14, Gupta discloses analyze historical data to determine normalcy of users transacting (col. 8 lines 21-30). Gupta and Enzaldo do(es) not explicitly disclose the following. Ramesh, in order to improve accuracy of results by implementing iterative training (¶0011, ¶0019, ¶0033), discloses: the method of claim 8, wherein the set of rules comprises a criteria for identifying one or more anomalous patterns in the set of historical data item transfers, wherein the one or more anomalous patterns comprise repetitive data item transfer requests or transferring a more than threshold amount of data items in a less than a threshold period of time (i.e., risk rules flagging transactions based on number of transactions, an mount in cross-border transactions; a transfer of all or substantial portion of deposit within a time period, number of transactions between the same parties; speaking to frequency of transactions and/or transferring a threshold significant amount within a time period in Ramesh, ¶0032).
Based on Gupta in view of Enzaldo, and further in view of Ramesh, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Ramesh to improve upon those of Gupta in order to improve accuracy of results by implementing iterative training.
With respect to claim 15, the limitation(s) of claim 15 are similar to those of claim(s) 1. Therefore, claim 15 is rejected with the same reasoning as claim(s) 1.
With respect to claim 16, the limitation(s) of claim 16 are similar to those of claim(s) 2. Therefore, claim 16 is rejected with the same reasoning as claim(s) 2.
With respect to claim 17, the limitation(s) of claim 17 are similar to those of claim(s) 3. Therefore, claim 17 is rejected with the same reasoning as claim(s) 3.
With respect to claim 18, the limitation(s) of claim 18 are similar to those of claim(s) 4. Therefore, claim 18 is rejected with the same reasoning as claim(s) 4.
With respect to claim 19, the limitation(s) of claim 19 are similar to those of claim(s) 5. Therefore, claim 19 is rejected with the same reasoning as claim(s) 5.
With respect to claim 20, the limitation(s) of claim 20 are similar to those of claim(s) 6. Therefore, claim 20 is rejected with the same reasoning as claim(s) 6.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERMAN L LIN whose telephone number is (571)270-7446. The examiner can normally be reached Monday through Friday 9:00 AM - 5:00 PM (Eastern).
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Sherman Lin
6/14/2026
/S. L./Examiner, Art Unit 2447
/JOON H HWANG/Supervisory Patent Examiner, Art Unit 2447
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