DETAILED ACTION
This nonfinal office action is responsive to claims filed on May 1, 2024. Claims 1-21 are pending. Claims 1, 8, and 15 are independent.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 16/848,340 and Provisional Application No. 62/834,103, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Neither the provisional application nor the prior filed application has support for training and using a machine learning algorithm as utilized in the claims of the current application.
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.
Claims 1-2, 5-9, 12-16, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Song (US20200279288), hereinafter Song, in view of Schwartz et al. (US20210201404), hereinafter Schwartz.
Regarding claim 1, Song teaches the method:
receiving data corresponding to a set of available objects, wherein the data is associated with a restricted authorization network, and wherein the restricted authorization network does not support inclusion of object terms associated with the set of available objects in authorization request messages; (Song, paragraph 0010: “The instructions also cause the computing device to apply available offer data to the machine learning model” and paragraph 0044: “In other embodiments, deep learning device 250 is associated with a third party and is in electronic communication with the payment network 120. In some embodiments, deep learning device 250 may be associated with, or be part of merchant bank 126, interchange network 128, and issuer bank 130, all shown in FIG. 1.” And paragraph 0058: “For example, the communications module 510 may be configured to receive input data (e.g., from database server 214) for the various inputs used to create the models described herein, or to transmit recommendation results of applications of those models (e.g., to client computing devices 230, server system 210).” – The available offer data is analogous to the available objects, where the merchant bank, interchange network, and issuer bank are all restricted authorization networks and the available offers are from a third part and therefore not included in the restricted authorization networks.)
… training … a machine learning algorithm to identify allowable objects from the set of available objects, wherein the machine learning algorithm is dynamically trained by processing a dataset including sample instrument utilizations and corresponding sample objects through the machine learning algorithm and iteratively updating one or more coefficients of the machine learning algorithm until one or more criteria are satisfied; (Song, paragraph 0005: “As such, the output is then compared to the label to determine differences between each of the output values and each of the label values. These differences are then back-propagated through the network, changing the weights of the edges and the values of the hidden nodes to such that the network will better conform to the known training data.” And paragraph 0060: “Model serving engine 530 applies the models built by the propensity engine 520 to generate various offer recommendations for cardholders (e.g., using aspects of cardholder data as inputs to the model). The model serving engine may use inputs such as offer limits and objectives for offer campaigns, business rules and constraints, and customer preference data.”)
processing the data through the machine learning algorithm to obtain a set of allowable objects; (Song, paragraph 0010: “The instructions also cause the computing device to apply available offer data to the machine learning model, thereby generating a set of offer recommendations matching one or more available offers with one or more cardholders.” – The set of offer recommendations is analogous to the set of allowable objects.)
receiving an authorization request message through the restricted authorization network, wherein the authorization request message is associated with an instrument utilization, and wherein the authorization request message does not include any object terms; (Song, paragraph 0033: “When cardholder 122 tenders payment for a purchase with a payment card, merchant 124 requests authorization from a merchant bank 126 for the amount of the purchase.” – The request for authorization does not include any object terms, the payment card is analogous to an instrument utilization.)
generating an authorization response message with no object information; (Song, paragraph 0034: “Based on these determinations, the request for authorization will be declined or accepted. If the request is accepted, an authorization code is issued to merchant 124.” – The authorization code is analogous to the authorization response message with no object information.)
performing post-processing of the instrument utilization by applying an object from the set of allowable objects, wherein the object is selected based on a set of instrument utilization values associated with the instrument utilization; and (Song, paragraph 0046: “In the example embodiment, the deep learning device 250 uses the transaction information, offer information, and impression information to train and apply deep learning techniques to evaluate tentative offers from merchants 124 (e.g., involving particular products or services) and the likelihood (propensity) particular cardholders 122 are to activate such offers.” – Using the transaction information, offer information, and impression information is analogous to the post-processing of the instrument utilization, where these are used in the machine learning to select the offers (objects).)
… updating the machine learning algorithm … based on feedback corresponding to the object and a set of other objects associated with other authorization request messages received through the restricted authorization network, wherein the machine learning algorithm is dynamically updated as the feedback is received. (Song, paragraph 0005: “This process may be repeated many thousands of time or more, based on the body of training data, configuring the network to better predict particular outputs given particular inputs.” And paragraph 0006: “For example, in some situations, feature engineering computations may be regularly needed to generate or regenerate the inputs used to train the neural network. These staging steps can represent a disproportionate computational burden when, for example, the input data is regularly updated with recent data (e.g., daily, weekly).” – The labels matching or not matching the outputs is analogous to the feedback corresponding to the object and a set of other objects associated with other authorization request messages as this is used to train the machine learning algorithm above.)
Song does not explicitly teach dynamically training and updating the machine learning model in real-time
However, Schwartz teaches:
dynamically training and updating the machine learning model in real-time (Schwartz, paragraph 0039: “The machine learning algorithm or artificial intelligence may be evaluated to determine, based on the sample inputs supplied to the machine learning algorithms or artificial intelligence, whether the machine learning algorithms or artificial intelligence are providing accurate and/or appropriate real-time offers or other determinations (e.g., rejections, etc.) for each sample user. Based on this evaluation, the machine learning algorithms or artificial intelligence may be modified (e.g., one or more parameters or variables may be updated) to improve the accuracy of the machine learning algorithms or artificial intelligence in determining an appropriate determination (e.g., real-time offer, rejection, etc.) for a user based on the provided inputs for the user.”)
Schwartz is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and finance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Song, which already teaches a method of training a machine learning model to generate a set of available objects but does not explicitly teach that the training and updating is done dynamically in real-time, to include the teachings of Schwartz which does teach that the training and updating is done dynamically in real-time in order to provide “customized real-time offers that can be based on user attribute data and that can be repeatedly updated in real-time.” (Schwartz, paragraph 0004)
Regarding claim 2, Song and Schwartz teach the method of claim 1, as cited above.
Song further teaches:
the machine learning algorithm is dynamically trained by iteratively modifying a set of coefficients associated with the machine learning algorithm until an output is generated that satisfies one or more criteria. (Song, paragraph 0005: “As such, the output is then compared to the label to determine differences between each of the output values and each of the label values. These differences are then back-propagated through the network, changing the weights of the edges and the values of the hidden nodes to such that the network will better conform to the known training data. This process may be repeated many thousands of time or more, based on the body of training data, configuring the network to better predict particular outputs given particular inputs.” – Schwartz teaches that the machine learning algorithm is dynamically trained above, while the process of being back-propagated potentially being repeated thousands of times or more is iteratively modifying the coefficients (weights) until the output satisfies the criteria of being close enough to the label values.)
Regarding claim 5, Song and Schwartz teach the method of claim 1, as cited above.
Song does not explicitly teach:
extracting an external system identifier and a record identifier from the authorization request message; and
determining that the instrument utilization is eligible for the object as a result of the external system identifier not being associated with an external system group corresponding to the record identifier.
However, Schwartz further teaches:
extracting an external system identifier and a record identifier from the authorization request message; and (Schwartz, paragraph 0056: “For instance, services 230 can include credit account services 232, push notification services 234, 3rd party identity services 236, 3rd party bank account integration services 238, and security services 240 (e.g., know your customer (KYC), Liveness, and IDS). In some examples, 3rd party bank account integration services 238 can be used by real-time offers platform 202 to obtain real-time data relating to a user's bank account.” – The third-party identity is analogous to the external system identifier while the user’s bank account is analogous to the record identifier, these being obtained in real-time during the service, e.g., the authorization request, is analogous to extracting them.)
determining that the instrument utilization is eligible for the object as a result of the external system identifier not being associated with an external system group corresponding to the record identifier. (Schwartz, paragraph 0056: “Real-time offers platform 202 can use the real-time banking data from 3rd party bank account integration services 238 as part of the dataset that is used to make a real-time determination of a user's eligibility for one or more real-time offers. Real-time offers platform 202 may access one or more of services 320 based on user data 226 in order to derive real-time offers.” – Using the third-party banking details, which is analogous to the external system identifier, to determine a user’s eligibility for the offers is analogous to determine the instrument utilization is eligible for the object, where the external system group, e.g., the third-party bank, is not associated with the user’s account.)
Regarding claim 6, Song and Schwartz teach the method of claim 1, as cited above.
Song does not explicitly teach:
applying the object to the instrument utilization and other instrument utilizations associated with a same record identifier, wherein the object is applied once a bundled threshold amount is exceeded.
However, Schwartz further teaches:
applying the object to the instrument utilization and other instrument utilizations associated with a same record identifier, wherein the object is applied once a bundled threshold amount is exceeded. (Schwartz, paragraph 0062: “In some implementations, artificial intelligence module 302 may assign one or more scores to different user attributes and/or to offer attributes. The user attribute scores and the offer attribute scores can be processed to determine whether a particular user qualifies for a particular offer.” – the user attribute scores is analogous to the instrument utilizations associated with a same record identifier, e.g., a same user. Using the scores to determine whether a user qualifies is analogous to the object being applied once a bundle threshold amount is exceeded. If the scores are high enough then they exceed the bundled threshold to present the offer, which is analogous to applying the object.)
Regarding claim 7, Song and Schwartz teach the method of claim 1, as cited above.
Song does not explicitly teach:
the feedback indicates whether object terms associated with the object and the set of other objects were accepted by corresponding users.
However, Schwartz further teaches:
the feedback indicates whether object terms associated with the object and the set of other objects were accepted by corresponding users. (Schwartz, paragraph 0066: “The user's decision to accept or reject an offer can be provided as feedback to artificial intelligence module 302 in order to further train the model. For instance, if the user rejects the offer, variables can be adjusted to identify offers that are more likely to be accepted in the future.”)
Regarding claim 8, Claim 8 has all the same limitations of claim 1 which are taught by Song and Schwartz – see claim 1 above.
Song additionally teaches:
one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to: (Song, paragraph 0009: “The method is implemented using a processor in communication with a memory.”)
Regarding claim 9, Song and Schwartz teach the system of claim 8, as cited above.
Claim 9 additionally has the same limitations of claim 2 which are taught by Song and Schwartz – See claim 2 above.
Regarding claim 12, Song and Schwartz teach the system of claim 8, as cited above.
Claim 12 additionally has the same limitations of claim 5 which are taught by Song and Schwartz – see claim 5 above.
Regarding claim 13, Song and Schwartz teach the system of claim 8, as cited above.
Claim 13 additionally has the same limitations of claim 6 which are taught by Song and Schwartz – see claim 6 above.
Regarding claim 14, Song and Schwartz teach the system of claim 8, as cited above.
Claim 14 additionally has the same limitations of claim 7 which are taught by Song and Schwartz – see claim 7 above.
Regarding claim 15, Claim 15 has the same limitations of claim 1 which are taught by Song and Schwartz – see claim 1 above.
Song additionally teaches:
A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to: (Song, paragraph 0010: “In yet another aspect, a non-transitory computer readable medium that includes computer executable instructions is provided. When executed by a computing device comprising at least one processor in communication with at least one memory device, the computer executable instructions cause the computing device to train a machine learning model using neural collaborative filtering.”)
Regarding claim 16, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 16 additionally has the same limitations of claim 2 which are taught by Song and Schwartz – see claim 2 above.
Regarding claim 19, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 19 additionally has the same limitations of claim 5 which are taught by Song and Schwartz – see claim 5 above.
Regarding claim 20, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 20 additionally has the same limitations of claim 6 which are taught by Song and Schwartz – see claim 6 above.
Regarding claim 21, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 21 additionally has the same limitations of claim 7 which are taught by Song and Schwartz – see claim 7 above.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Song in view of Schwartz in view of D. Reynolds and A. Deitch (US20200279288), hereinafter Reynolds.
Regarding claim 3, Song and Schwartz teach the method of claim 1, as cited above.
Song and Schwartz do not explicitly teach:
evaluating the set of instrument utilization values and an external system identifier corresponding to the instrument utilization to determine that the instrument utilization is eligible for the object
However, Reynolds teaches:
evaluating the set of instrument utilization values and an external system identifier corresponding to the instrument utilization to determine that the instrument utilization is eligible for the object. (Reynolds, paragraph 0101: “In some implementations, the process 400 may validate that the one or more offerings the consumer is requesting to purchase are eligible for redemption using the first promotion, prior to processing the charge. For example, the first promotion may be for redemption only at the hair salon described above. Accordingly, the process 400 would process the charge associated with the request to purchase a haircut using the first promotion. Similarly, the process 400 would not process the charge associated with the request to purchase a bike using the first promotion.” – The processing of the charge is analogous to the set of utilization values while the merchant locations are analogous to the external system identifier, e.g., the third party as taught by Schwartz above. Determining if the purchase is eligible for a promotion is analogous to evaluating the set of instrument utilization values and an external system identifier to determine that the instrument utilization is eligible for the object.)
Reynolds is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and finance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Song and Schwartz, which already teaches selecting an object based on the instrument utilization but does not explicitly teach evaluating the set of instrument utilization values and an external system identifier to determine that the instrument utilization is eligible for the object, to include the teachings of Reynolds which does teach evaluating the set of instrument utilization values and an external system identifier to determine that the instrument utilization is eligible for the object as “allowing consumers to redeem promotions and process payments through the promotional service reduces the processing power requirement of the merchant devices. Since these processing operations would be otherwise performed by the merchant devices, the stress on the merchant devices is reduced.” (Reynolds, paragraph 0098)
Regarding claim 10, Song and Schwartz teach the system of claim 8, as cited above.
Claim 10 additionally has the same limitations of claim 3 which are taught by Song, Schwartz, and Reynolds – see claim 3 above.
Regarding claim 17, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 17 additionally has the same limitations of claim 3 which are taught by Song, Schwartz, and Reynolds – see claim 3 above.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Song in view of Schwartz in view of Barta et al. (US20170069003), hereinafter Barta.
Regarding claim 4, Song and Schwartz teach the method of claim 1, as cited above.
Song further teaches:
extracting an external system identifier and a record identifier from the authorization request message; (Song, paragraph 0033: “As described with respect to system 120, a financial institution called the “issuer” (or “issuer bank”) 130 issues a payment card or electronic payments account identifier, such as a credit card, to a consumer or cardholder 122, who uses the payment card to tender payment for a purchase from a merchant 124. To accept payment with the payment card, merchant 124 must normally establish an account with a financial institution that is part of the financial payment system.” – The issuer is analogous to the external system identifier while the payment card or electronic payments account identifier is analogous to the record identifier.)
Song and Schwartz do not explicitly teach:
analyzing the set of instrument utilization values using a set of fraud rules; and
authorizing the instrument utilization based on the set of fraud rules, the external system identifier, and the record identifier.
However, Barta teaches:
analyzing the set of instrument utilization values using a set of fraud rules; and (Barta, paragraph 0035: “Uniquely herein, the merchant 102 is then also permitted to provide fraud prevention rules that are specific to the merchant 102, and that are employed by and/or through the sales platform 124 (in this exemplary embodiment), in processing purchase transactions at the merchant 102 for product(s).” – The fraud prevention rules in processing purchase transactions is analogous to analyzing the set of instrument utilization values using a set of fraud rules.)
authorizing the instrument utilization based on the set of fraud rules, the external system identifier, and the record identifier. (Barta, paragraph 0055: “Conversely, if at 706, the criteria for the amount rules are not violated, the engine 122 permits the transaction to proceed, at 710.” And paragraph 0057: “In this manner, permitting the transaction to proceed, by the engine 122, may include permitting the merchant 102 (e.g., via the sales platform 124, etc.) to transmit the authorization request to the acquirer 104 associated with the merchant 102, or may include permitting the authorization request to be transmitted to the payment network 106 or the issuer 108 (depending on where the engine 122 intercepts or receives the authorization request).” – The requester is analogous to the external system identifier, while the transaction would include the record identifier. The rules not being violated and permitting the transaction to proceed is analogous to authorizing the instrument utilization based on the set of fraud rules, external system identifier, and record identifier.)
Barta is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and finance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Song and Schwartz, which already teaches generating authorization request messages and authorizing the instrument utilization but does not explicitly teach using fraud rules to authorize the instrument utilization, to include the teachings of Barta which does teach using fraud rules to authorize the instrument utilization in order to improve security and protection over generic measures. (Barta, paragraph 0061)
Regarding claim 11, Song and Schwartz teach the system of claim 8, as cited above.
Claim 11 additionally has the same limitations of claim 4 which are taught by Song, Schwartz, and Barta – see claim 4 above.
Regarding claim 18, Song and Schwartz teach the non-transitory, computer-readable storage medium of claim 15, as cited above.
Claim 18 additionally has the same limitations of claim 4 which are taught by Song, Schwartz, and Barta – see claim 4 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. Kim and T. Mao (US10796363)
T. Hunt and X. Liu (US20200357052)
Sardari et al. (US20230316280)
Morin et al. (US11244340)
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/J.C.M./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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