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 .
The following is a Final Office Action in response to communications received on 4/27/2026. Claims 1 is currently pending and have been examined. No claims have been amended.
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.
Step 1: The claim 1 is a method claim. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claim 1 recites:
A method for real-time competitive credit card transaction auctions at the point-of-sale comprising:
providing a competitive credit auction bidding payment network;
issuing, to a user, a payment card having routing information encoded thereon for processing, via said competitive credit auction bidding payment network, point-of-sale transactions using said payment card;
during a point-of-sale transaction at a merchant:
reading said routing information from said payment card;
transmitting a transaction authorization request to said competitive credit auction bidding payment network, said authorization request comprising transaction characteristics for said transaction, said transaction characteristics including an account number of said payment card, a transaction amount of said transaction, a transaction type of said transaction, and a merchant identification number of said merchant;
receiving, at said competitive credit auction bidding payment network, said transmitted authorization request;
said competitive credit auction bidding payment network processing said received authorization request via a competitive bidding module, said competitive bidding module automatically determining a winning bidder lender to issue credit for said transaction from among a plurality of candidate lenders, each of said candidate lenders being preregistered to participate in said competitive credit auction bidding payment network,
said determining comprising:
for each candidate lender, determining a rate at which said each candidate lender is willing to extend credit in said transaction amount for said transaction by traversing an N-ary decision tree for said each candidate lender comprising a plurality of nodes encoding data about conditions under which said each candidate lender is willing to extend credit and a credit rate associated with each of said conditions, and comparing each traversed node with one or more of said transaction characteristics and/or profile data about said user accessible to said competitive bidding module to determine a rate matching said conditions;
sorting said determined rates to identify a lowest rate among said determined rates;
and selecting as said winning bidder said each candidate lender associated with said lowest rate; and
responding to said authorization request with an indication that said transaction is accepted.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for a competitive auction bidding payment when checking out at the POS of a merchant. The final payment being that of a lowest rate from among a plurality of interest rates from lenders (see [0035] if the instant specification). The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
A method for real-time competitive credit card transaction auctions at the point-of-sale comprising:
competitive credit auction bidding payment network;
point-of-sale transactions using said payment card;
via a competitive bidding module, said competitive bidding module automatically determining
The additional elements of emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1 does not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05)
As such, independent claim 1 is ineligible and no dependent claims are presented.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Arbel (US 10026117) in view of Shirakawa (US 20010020233) in further view of “Decision Trees- A Powerful Tool in Mathematical and Economic Modeling”.
Regarding claim 1, Arbel discloses:
A method for real-time competitive credit card transaction auctions at the point-of-sale comprising: providing a competitive credit auction bidding payment network; (bid computer 230 in Figure 2)
issuing, to a user, a payment card ([Col. 11 lines 55-60] at 402, the consumer 215 applies for and receives an electronic payment instrument 250 governed by a contract including terms and conditions 251d (generally, “default terms 251d”) to which consumer 215 agrees. having routing information encoded thereon [Col. 12 lines 57-67 Electronic transaction data 222 that is determined or scraped and see included credit card number in Col. 10 lines 55-60] for processing, via said competitive credit auction bidding payment network, point-of-sale transactions using said payment card; ([Col. 11 lines 10-20] In the illustrated embodiment, the consumer has one or more electronic payment instruments 250 such as credit cards, and registers with cloud computer 320, which serves as a cloud wallet resource or computer and stores consumer's credit card data in a database 322. The payment processor 315 and/or bid computer 230 provides services of processing transactions involving forms of electronic payment such as a credit card and serves as an intermediary between the consumer 215 and issuer 245, which is the recipient of proceeds of the transaction.)
during a point-of-sale transaction at a merchant: ([Col. 9 lines 44-50] Merchant 225 may be an in-store or brick and mortar merchant such that certain embodiments involve the consumer 215 visiting a merchant 225 store to make a purchase. In these embodiments, the merchant 225 may utilize an electronic payment device or terminal 220 in the form of a Point-Of-Sale (POS) or payment terminal or other terminal for accepting and processing electronic payments.)
reading said routing information from said payment card; [Col. 12 lines 57-67] At 606, the payment selection application 212 is manually activated by the consumer 215 or automatically upon establishment of a wireless or NFC connection or in response to receiving a communication from electronic payment device 210. Payment selection application 212 determines or scrapes electronic transaction data 222 received from the electronic payment device 210. Electronic transaction data 222 that is determined or scraped may include identification of the consumer 215 and a transaction amount, and as necessary, other data that may be useful in assisting the issuer 245 to determine whether to submit an offer 241 and proposed terms of such offers 241 such as identification of the merchant 225 and items or categories of items purchased.
transmitting a transaction authorization request to said competitive credit auction bidding payment network, said authorization request comprising transaction characteristics for said transaction, ([Col. 13 lines 33-43] At 610, the payment selection application 212 transmits the electronic transaction data 222 (and any term 251 preference or priority specified by the consumer 125) to the bid computer 230, and at 612, the analysis program 232 of or accessed by the bid computer 230 receives the electronic transaction data 222, accesses the database or table 500, and looks up information about the electronic payment instruments 250 available to the consumer 215 and issuers 245 thereof and to which requests 231 for bids to fund the transaction should be transmitted.) said transaction characteristics including an account number of said payment card ( [Col. 10 lines 55-60] containing data of available electronic payment instruments 250 (e.g., consumer name, credit card number, issuer, expiration date, security code or card verification code)), a transaction amount of said transaction, a transaction type of said transaction (items or categories of items purchased), and a merchant identification number of said merchant (a merchant account 312 on behalf of the merchant 225); [Col. 12 lines 57-67] Electronic transaction data 222 that is determined or scraped may include identification of the consumer 215 and a transaction amount, and as necessary, other data that may be useful in assisting the issuer 245 to determine whether to submit an offer 241 and proposed terms of such offers 241 such as identification of the merchant 225 and items or categories of items purchased.
receiving, at said competitive credit auction bidding payment network, said transmitted authorization request; ([Col. 13 lines 30-42] At 610, the payment selection application 212 transmits the electronic transaction data 222 (and any term 251 preference or priority specified by the consumer 125) to the bid computer 230, and at 612, the analysis program 232 of or accessed by the bid computer 230 receives the electronic transaction data 222, accesses the database or table 500, and looks up information about the electronic payment instruments 250 available to the consumer 215 and issuers 245 thereof and to which requests 231 for bids to fund the transaction should be transmitted.)
said competitive credit auction bidding payment network processing said received authorization request via a competitive bidding module, said competitive bidding module automatically determining a winning bidder lender to issue credit for said transaction from among a plurality of candidate lenders, [(Col. 13 lines 43-60) At 614, the analysis program 232 transmits requests 231 for bids or offers from the bid computer 230 to computers 240 of identified issuers 245, and in certain embodiments, also transmits the consumer's term preference and priority and/or authorization requests (e.g., based on the transaction amount) to issuer computers 240. At 616, issuer computers 240 determine whether the transaction should be approved (e.g. based on transaction amount and available credit), and at 618, determine offers 241 to be submitted to the consumer 215 if the issuers 245 participate in the real-time bidding process provided by the bid computer 230. If so, then each issuer 245 determines its bid or offer 241 including proposed terms 251p (“p” indicating “proposed”) to fund electronic transaction based at least in part upon electronic transaction data 222 and default terms 251d of issuer's electronic payment instrument 250.)
said determining comprising: for each candidate lender, determining a rate at which said each candidate lender is willing to extend credit in said transaction amount for said transaction (see comparison table in Figure 5/ [Col. 15 lines 23-25] One example is how much an interest rate was lowered compared to a default interest rate (e.g., a 25% reduction due to a default interest rate of 20% and a proposed interest rate of 15% and [Col. 15 lines 33-45] At 708, the analysis program 232 compares proposed terms 251p of issuer offers 241 participating in real-time bidding process (e.g., direct comparison for the same terms or comparison of normalized terms as discussed above with reference to 706) and, as necessary, default terms 251d of other issuers 245. At 710, in certain embodiments, the analysis program 232 submits another bid request 231 to an issuer computer 240 if that issuer 245 indicated that it would match or beat an offer 241 of another issuer. The analysis program 232 notifies that issuer 245 that its offer 241 is not the best offer, thus giving that issuer 245 an opportunity to submit a stronger offer 241 to the bid computer 230 before presenting results to the consumer. This process is transparent to the consumer 215 and merchant 225.)
sorting said determined rates to identify a lowest rate among said determined rates; [Col. 15 lines 45-60] At 712, the bid computer 230 receives any additional offers 241 in response to 710 discussed above, and normalizes or standardizes any additional proposed terms 251p and/or default terms 251d as necessary. At 714, the analysis program 232 compares received offers 241, and at 716, identifies or selects an electronic payment instrument 250s (“s” indicating “selected” electronic payment instrument) that is determined to provide the maximum benefit to the consumer 215. In another embodiment, 714 involves the analysis program 232 generating a list or ranking of electronic payment instruments 250 based at least in part upon comparison and indicating which electronic payment instrument(s) would provide the best or better benefit to the consumer 250. and see [Col. 15 lines 23-25] One example is how much an interest rate was lowered compared to a default interest rate (e.g., a 25% reduction due to a default interest rate of 20% and a proposed interest rate of 15%
and selecting as said winning bidder said each candidate lender associated with said lowest rate; and [Col. 15 lines 23-25] One example is how much an interest rate was lowered compared to a default interest rate (e.g., a 25% reduction due to a default interest rate of 20% and a proposed interest rate of 15%
responding to said authorization request with an indication that said transaction is accepted. [Col. 16 lines 30-40] At 726, the transaction is processed and finalized, e.g., by one or more of an issuer computer 245, payment processor computer 310 and cloud wallet server 320, and the consumer 215 receives benefit of using selected electronic payment instrument 250 according to the proposed terms 251p of the issuer offer 241 accepted by use of that issuer's electronic payment instrument 250, or if selected electronic payment instrument was not the subject of bid response, according to default terms 251d to which consumer 215 previously agreed.
and comparing […] with one or more of said transaction characteristics and/or profile data about said user accessible to said competitive bidding module to determine a rate matching said conditions; [Col. 14 lines 55-67] At 706, the analysis program 232 normalizes or standardizes proposed terms 251p of offers 241 as necessary. For example, it may be that all of the offers 241 received at bid computer 230 involve the same term 251 such that the bid program 232 can perform a direct comparison of the proposed terms 251p of offers 241 (and the same default term 251d as necessary). Issuer offers 241 may involve a common term 251 such that normalization is not required, whereas other offers 241 may involve different types of terms 251 such that direct comparison is not possible. In these cases, the different proposed terms 251p and/or default terms 251d are normalized, transformed or converted into a common unit, term or denominator so that they can be compared with each other to assess the benefit conferred to the consumer 215. And see Figures 2 and 5
While Arbel discloses a bid computer receiving information from the customer and merchant related to a transaction and identifying the best user benefit from the bid computer (i.e. lowest interest rate) to return to a customer by comparing available rates from issuers, the reference does not expressly disclose:
each of said candidate lenders being preregistered to participate in said competitive credit auction bidding payment network,
by traversing an N-ary decision tree for said each candidate lender comprising a plurality of nodes encoding data about conditions under which said each candidate lender is willing to extend credit and a credit rate associated with each of said conditions,
and comparing each traversed node with one or more of said transaction characteristics and/or profile data […];
However Shirakawa teaches:
each of said candidate lenders being preregistered to participate in said competitive credit auction bidding payment network, [0070] potential lending offers registered in the borrowing request file 42 and lending offer file 44. and [0073] As the first example of the financial auction, a case wherein one potential borrowing request A1 (company A1) and four potential lending offers B1 to B4 (investors B1 to B4) are subjected to matchmaking, as shown in Table 1, will be described in detail with reference to FIG. 9 (one-to-many matching scheme).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the collection of issuers in Arbel to include each of said candidate lenders being preregistered to participate in said competitive credit auction bidding payment network, as taught in Shirakawa, in order to provide potential lending options from a screened collection of registered lenders for addressing the risk of financing options (paragraphs 0005 and 0027).
While Arbel discloses a bid computer receiving information from the customer and merchant related to a transaction and identifying the best user benefit from the bid computer (i.e. lowest interest rate) to return to a customer by comparing available rates from issuers. The information regarding the interest rates, transaction, customer, merchant, and issuers with available competitive rates past through the bid computer 30 (competitive bidding module) [See Figures 2 and 5] and Shirakawa teaches the registration of screened lenders, the combination of references does not expressly disclose:
by traversing an N-ary decision tree for said each candidate lender comprising a plurality of nodes encoding data about conditions under which said each candidate lender is willing to extend credit and a credit rate associated with each of said conditions,
and comparing each traversed node with one or more of said transaction characteristics and/or profile data […];
However “Decision Trees- A Powerful Tool in Mathematical and Economic Modeling” teaches by traversing an N-ary decision tree (see Figure 3 showing the decision tree with each point having two or more branches) for said each candidate lender comprising a plurality of nodes encoding data about conditions under which said each candidate lender is willing to extend credit and a credit rate associated with each of said conditions, and comparing each traversed node with one or more of said transaction characteristics and/or profile data […]; [page 37-38] Decision tree technique is standardly implemented for customer retention, especially CART algorithm. Based on the database in Table I and client score achieved via decision tree depicted on Figure 3 in order to eliminate the clients churning we might offer them different mortgage interest rate. Restrictions apply. Let x be the basic interest rate offered by Our Bank. Let the average basic mortgage interest rate of competitor be y. […] In our simplified model, the matters stated above reflect that 80% of applicants for a mortgage will get the same or even more favorable conditions than competitors granted. What is more, half of the applicants will have better conditions in our financial institution in compare with rival ones. […]A significant category of banking services is credit card. Solutions to the customer credit card churn were searched through decision trees in e. g. [10]. Greater efficiency can be achieved with support or combination with other data mining methods. Predictive analysis can be executed by classification and regression tree. Various kinds of decision trees enable picking the most relevant characteristics in the context of credit card churn problem and permit subsequent manipulation with diminished data set.
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Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a bid computer receiving information from the customer and merchant related to a transaction and identifying the best user benefit from the bid computer (i.e. lowest interest rate) to return to a customer by comparing available rates from issuers; information regarding the interest rates, transaction, customer, merchant, and issuers with available competitive rates past through the bid computer 30 of Arbel and the registration of screened lenders in Shirakawa to include by traversing an N-ary decision tree for said each candidate lender comprising a plurality of nodes encoding data about conditions under which said each candidate lender is willing to extend credit and a credit rate associated with each of said conditions, and comparing each traversed node with one or more of said transaction characteristics and/or profile data […], as taught in “Decision Trees- A Powerful Tool in Mathematical and Economic Modeling”, in order to use various kinds of decision trees enabling picking the most relevant characteristics in the context of credit card churn problem and permit subsequent manipulation with diminished data set. Moreover, principles resulting from decision trees can be utilized to develop the system for early warning. (page 38)
Response to Arguments
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to the first remarks regarding MPEP 2106.04(d) and 2106.05 establish that the Prong 2 analysis must consider all additional elements recited in the claim, both individually and in combination, to determine whether the claim integrates any judicial exception into a practical application, the examiner respectfully disagrees that the limitations "issuing, to a user, a payment card having routing information encoded thereon for processing, via said competitive credit auction bidding payment network, point-of-sale transactions using said payment card," and "reading said routing information from said payment card." are technical in nature. While the examiner has determined that he bidding payment network is an additional element, the transmission of the payment related data and the reading of the payment related data is part of the abstract idea. The technical details cited from the specification are not detailed in the claims are therefore not read into the claim interpretation. The bidding payment network is interpreted as an additional element recited at a high level of generality. The same is true of the transaction authorization request as the level of detail recited is the transaction data that is part of the abstract idea. The limitation being a specific role in the process does not preclude the limitation from being part of the abstract idea. As to the N-ary tree, the examiner asserts the claims recite features not more than data processing of the candidate lenders, conditions, credit and credit rate. The level of detail recited does not preclude the N-ary tree from being part of the abstract idea along with the data that is being analyzed. While the examiner does not contend the N-ary tree is part of the abstract idea, even if considered as an additional element, the element would be recited at a high level of generality and would not integrate the judicial exception into a practical application. The recitation of computational processing at a high level does not preclude the limitation from being part of the abstract idea nor does it necessarily integrate the judicial exception into a practical application. For at least these reasons, the examiner has not ignored the limitations, alone or in combination, and has treated all limitations as required by the analysis.
With respect to the second remarks regarding the operations of the N-ary tree, the examiner asserts that merely automating a business practice by using the computational speed of a computer does not integrate the judicial exception into a practical application. That is, per MPEP 2106.05 (f), the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). The N-ary decision tree structure alone does not recite enough technical detail to integrate the judicial exception into a practical application. The examiner notes that the recitation of the tree possibly being implemented using AI models that are trained, in the specification, is not part of the current claimed invention and therefore not read into the claim interpretation as the example in the specification is non-limiting. For at least these reasons, the examiner does not find the automation of the decision in the claimed invention using N-ary trees to being persuasive.
With respect to the comparison of the instant application to DDR Holdings, the examiner does not find the comparison of the cases to be persuasive as the fact patterns of the two cases differ. While the examiner agrees that DDR Holdings was found to provide a technical response to a business process, the improvement was not merely to how payment networks operate. DDR Holdings provided a technical improvement to the interfacing of a webpages and the improvement did not lie in payment process. The improvement in DDR Holdings, which recited a specific way to automate the creation of a composite web page by an "outsource provider" that incorporates elements from multiple sources in order to solve a problem faced by websites on the Internet, is not comparable to the instant application. (DDR Holdings vs. Hotels.com).
With respect to the remarks directed to Step 2B, the examiner first asserts that all limitations were considered. The limitations were considered alone and in combination as shown in the rejection above. None of the limitation, however, were found to recite well-understood, routine, and conventional limitations and therefore the analysis does not require Berkheimer evidence within the analysis. The ordered combination, as stated in the rejection, was considered and determined to have the same conclusion as was found at Prong 2. This was indicated on the record and therefore the analysis was complete and proper. The remarks directed to the consideration of Berkheimer evidence are considered moot for the reasons stated above.
With respect to the remarks directed to 35 USC 103, in particular the remarks directed to the teachings of Arbel, the examiner maintains that the reference teaches both (1) process the routing information on the payment card for a point- of-sale transaction and (2) transmit a transaction authorization request to said competitive credit auction bidding payment network. The examiner asserts as shown in the claim mapping at least Col. 11 lines 10-20, the bid computer provides the service of the processing transactions which this transaction data as shown in Col. 12 lines 57-67 can be the encoded routing information. As such the Arbel reference does teach the claim limitation.
With respect to the remarks directed to 35 USC 103, in particular the remarks directed to Arbel and the teachings of the recited “transaction type”, while the examiner does not concede that the category of purchased items is not a transaction type (i.e grocery purchase, gas purchase, entertainment purchase), the specification does not limit the definition of transaction type. It merely provides examples of what the transaction might be in [0024], [0032], and [0034]. Since one of the examples in the specification is a retail purchase and the reference does teach the identification of the items purchased (i.e. a retail purchase), the retail purchase interpretation is determined in the reference and therefore still taught by the Arbel reference.
With respect to the remarks directed to 35 USC 103, in particular with respect to the Decision Trees- A Powerful Tool in Mathematical Economic Modeling, as shown in the rejection, the reference in Figure 3 shows a decision tree with a plurality of branches and on pages 37-38 the decision tree technique is applied where the mortgage interest rates (extend credit and credit rate) of a competitor and of the instant bank (each candidate lender), with the clients information (profile data). The analysis is executed and a resulting decision is made. For at least these reasons the reference teaches the decision tree being used to compare potential lenders at the level of detail required by the claims and the use of the transaction characteristics/profile date to make the comparison (see in particular Fig. 3, as discussed above). Further respect to the motivation to combine the references, the remarks have not considered the entirety of the cited motivation to combine. The reference also states in the cited motivation to combine that the use of decision trees enables the picking of the most relevant characteristics in the context. The problem being solved by the cited reference does not have to be the same of that as that of the instant application. Here the decision trees are being used in the same processing manner with the same processed data being recited with one possible use being the of determining if a competitor can provide a better rate (page 38). The reference is noting the improvement to the determination process through the use of decision trees, one of which is the alerting that a competitor might be providing an advantage to the customer and the customer might then be turned over. This motivation to combine explicitly from the reference was cited to show the motivation to use decision trees in the competitive credit auction bidding payment network in order to improve the decision making/determination process. As stated in MPEP 2143, with respect to teaching, suggestion, and motivation, “The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. "[A]n implicit motivation to combine exists not only when a suggestion may be gleaned from the prior art as a whole, but when the ‘improvement’ is technology-independent and the combination of references results in a product or process that is more desirable, for example because it is stronger, cheaper, cleaner, faster, lighter, smaller, more durable, or more efficient. Because the desire to enhance commercial opportunities by improving a product or process is universal—and even common-sensical—we have held that there exists in these situations a motivation to combine prior art references even absent any hint of suggestion in the references themselves. In such situations, the proper question is whether the ordinary artisan possesses knowledge and skills rendering him capable of combining the prior art references." Id. at 1368, 80 USPQ2d at 1651.” While the examiner has applied an explicit teaching from the reference, the teaching need not be explicit and other motivations to combine could persist.
For at least these reasons the claims remain rejected under 35 USC 103.
Relevant Art Not Cited
Baghestani (US 20220198556) discloses comparing lenders’ rates to provide suggested lenders to a user buying a vehicle.
Conclusion
THIS ACTION IS MADE FINAL. 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 VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/9/2026
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