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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Status of Claims
Claims 1-20 have been subject to restriction.
Claims 1-9 have been elected, without traverse, and are rejected.
Claims 10-20 are non-elected, and are withdrawn.
Claim Objections
Regarding Claim 5: Claim 5 recites training the first machine learning model based on historical loan application approval data to predict the buy lending, the historical loan application data comprising…
The underlined portion seems to be incomplete; the Examiner interprets the claim as meaning to recite the buy lending rate. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-9 are directed to a method, which is a process. Therefore, claims 1-9 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 1 sets forth the following limitations reciting the abstract idea of determining deal structures for sale of a vehicle based on probability of acceptance and profitability:
receiving a customer identifier associated with a customer interested in a vehicle;
determining a financial data associated with the customer identifier and a vehicle data associated with the vehicle;
determining one or more product or services associated with the vehicle;
determining, based on the financial data associated with the customer identifier and the vehicle data associated with the vehicle, loan parameters for financing purchase of the vehicle;
determining, based on the loan parameters, a first value indicative of acceptance of the loan parameters by at least one lending entity;
determining, a second value indicative of a probability of the customer purchasing the vehicle and the one or more product or services based on the loan parameters;
determining, a third value indicative of a profitability in the purchase of the vehicle and the one or more products or services based on the associated loan parameters;
providing a deal structure comprising the loan parameters to the customer for the purchase of the vehicle and the one or more product and services, the loan parameters optimizing the total of the first value, the second value, and the third value.
The recited limitations above set forth the process for determining deal structures for sale of a vehicle based on probability of acceptance and profitability. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to determining financial data and vehicle data in order to determine loan parameters and profitability in order to optimize and provide a deal structure to a customer (see specification: [0001-0002] disclosing the problem of inaccurate estimates for loans by vehicle dealers and short transaction windows for vehicle transactions), which is an sales and marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
a device comprising a processor;
a first machine learning model;
a second machine learning model;
a third machine learning model;
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a device comprising a processor, this is recited with a very high level of generalization, and is recited in passing as merely performing the steps of the abstract idea. As disclosed in paragraph [0071] of the specification, the device comprising a processor may be any of a tablet device, a mobile device, a smart phone, a personal computer, etc. The paragraph further discloses that the computing device can include any computer operating environment. It is evident that the device comprising a processor is any generic computing device that merely provides a general link to a computing environment, such that the abstract idea is performed by the computing device. The machine learning models are also disclosed with a very high level of generality. The closest disclosure occurs in specification paragraph [0034] and [0042], which merely discloses the learning model includes a learning algorithm, merely disclosing a gradient boosting algorithm, nth degree polynomial, or tree-based classification algorithm as an example. There is no further discussion of any particularity or specific functionality of the machine learning models. The machine learning models merely act as a black box that receives inputs of the abstract idea to provide an output of the abstract idea. They merely perform calculations for determining various data, and are merely applied to the abstract idea.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-19 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of determining deal structures for sale of a vehicle based on probability of acceptance and profitability, and do not recite any further additional elements. Thus, each of claims 2-9 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-9 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-9 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged; however, the additional elements of claims 2-9 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-9 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-9 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-9 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 8-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gaur (US 20210233164 A1).
Regarding Claim 1: Gaur discloses a method comprising:
receiving, by a device comprising a processor, a customer identifier associated with a customer interested in a vehicle; (Gaur: [0122] – “a system (e.g., the one or more servers 105 of FIG. 1, the one or more service provider computer(s) 210 of FIG. 2) may receive a customer identifier”; Gaur: [0121] – “a process 700 for recommending vehicle products”).
determining, by the device, a financial data associated with the customer identifier and a vehicle data associated with the vehicle; (Gaur: [0123] – “the system may determine financial data associated with the customer identifier and vehicle data associated with a vehicle. In some embodiments, the financial data and vehicle data may be from one or more databases of the system. In some embodiments, the system may send a request to a third-party system to retrieve the financial data and/or vehicle data”).
determining, by the device, one or more product or services associated with the vehicle; (Gaur: [0125] – “the system may determine a product or service associated with the vehicle. The product or service may include product or services for vehicle protection, vehicle accessories, extended warranties, insurance, paint protections, or any suitable product or service associated with a vehicle”).
determining, by the device using a first machine learning model based on the financial data associated with the customer identifier and the vehicle data associated with the vehicle, loan parameters for financing purchase of the vehicle; (Gaur: [0124] – “the system may determine loan information associated with the customer identifier and the vehicle. The loan information may include an interest rate, price of the vehicle, monthly payments, a payment term, one or more credit limits, a loan to value ratio, and/or any suitable information associated with loans for a customer with the customer identifier. For example, the system may determine loan information based on the financial data associated with the customer identifier and vehicle data”; Gaur: [0021] – “the machine learning models may set loan terms based on a likelihood that a customer will purchase a vehicle according to the loan terms”).
determining, by the device using a second machine learning model based on the loan parameters, a first value indicative of acceptance of the loan parameters by at least one lending entity; (Gaur: [0030] – “A probability threshold may describe a value or a value range for determining whether or not lenders will approve and/or purchase loan information (e.g., using machine learning, and based on previous purchase data). If a probability of loan information exceeds a probability threshold, the probability indicates that one or more lenders are most likely to approve and/or purchase the spot contract”).
determining, by the device using a third machine learning model, a second value indicative of a probability of the customer purchasing the vehicle and the one or more product or services based on the loan parameters; (Gaur: [0126] – “the system may determine, using a machine learning model, a first value indicative of a probability that the customer will purchase the vehicle and the product or service based on the loan information. In some embodiments, the system may utilize a machine—learned model to determine the first value based on the loan information that was considered for other similar vehicles and/or for similar customers”).
determining, by the device using a fourth machine learning model, a third value indicative of a profitability in the purchase of the vehicle and the one or more products or services based on the associated loan parameters; (Gaur: [0127] – “the system may determine a second value indicative of a profitability of a purchase of the vehicle and the product or service based on the loan information. The second value indicative of the vehicle's profitability may be an indication of how profitable a purchase of the vehicle may be when purchased according to the loan information… the system may use the machine learning model to further determine the second value based on the loan information”).
providing, by the device, a deal structure comprising the loan parameters to the customer for the purchase of the vehicle and the one or more product and services, the loan parameters optimizing the total of the first value, the second value, and the third value. (Gaur: [0131] – “the system may determine that the vehicle and the product or services are most likely to produce an acceptable profit (e.g., equal to or greater than a profit goal), and then the system may perform actions in block 720. In some embodiments, the system may repeat actions in the block 714 and 718 until loan information having a value exceeding the probability threshold and a value exceeding the profitability threshold is determined”; Gaur: [0132] – “the system may send the loan information for presentation to the customer. The system may use one or more graphical user interfaces to display the vehicle, the product or service, and loan information”). In summary, the system optimizes the deal structure by iterating the process until probability and profitability thresholds are satisfied.
Regarding Claim 2: Gaur discloses the limitations of claim 1 above.
Gaur further discloses a method comprising:
identifying, by the device, one or more estimates associated with a value of the vehicle and the one or more product or services; (Gaur: [0031] – “the computer system may identify one or more “book” estimates associated with a value of the vehicle, and use the book value to determine a recommended vehicle product or service, and/or to determine loan terms. For example, the computer system may determine an estimate value of the vehicle based on the vehicle data (e.g., a make, a model, a year, a vehicle type, tire type, size, colors, sunroofs, extended cabs, four-wheel drive, number of engine cylinders, historical data (e.g., maintenance and/or repair record, or the like). In some examples, the computer system may receive an estimate value from a database of the computer system and/or an external source (e.g., a Blue Book value that refers to a value of a vehicle by a guide known as the Kelley Blue Book)”).
determining, by the device, based on the one or more estimates, the loan parameters. (Gaur: [0031] – “the computer system may identify one or more “book” estimates associated with a value of the vehicle, and use the book value to determine a recommended vehicle product or service, and/or to determine loan terms”).
Regarding Claim 8: Gaur discloses the limitations of claim 1 above.
Gaur further discloses training the third machine learning model based on historical loan application approval data to determine the second value indicative of the probability of the customer purchasing the vehicle and the one or more product or services based on the loan parameters, the historical loan application approval data comprising a list of approved/disapproved loan applications, the customer profile for each of the approved/disapproved loan applications, and the financial data for each of the approved/disapproved loan applications. (Gaur: [0036] – “Based on historical data, the computer system may determine whether or not that loan information associated with similar vehicles have been accepted by the same customer and/or similar customers (e.g., purchases of similar vehicles by the same customer and/or similar customers, absence of purchases of similar vehicles by the same customer and/or similar customers)… the financial data and the vehicle data may also be inputs to the machine learning mode. The machine learning model may generate respective weights associated with loan information, the financial data, and/or vehicle data. In some embodiments, the weights may be generated during training of the machine learning model. For instance, during training, the machine learning model may assign weights and/or bias to the loan information, the financial data, and/or vehicle data”). In summary, the machine learning model is trained by historical data of whether or not loan information have been accepted (list of approved/disapproved applications) by similar customers (customer profile and financial information for each of the approved/disapproved applications).
Regarding Claim 9: Gaur discloses the limitations of claim 1 above.
Gaur further discloses wherein determining the third value indicative of the profitability in the purchase of the vehicle and the one or more products or services further based on an original equipment manufacturer (OEM) incentive. (Gaur: [0036] – “the computer system may adjust an interest rate and/or vehicle value based on original equipment manufacturer (OEM) rebates and/or incentives. The computer system may reduce an interest rate and/or a price for a vehicle such that volumes of vehicles that are most likely to be purchased may meet an upcoming sales target and earn OEM rebates and/or incentives”; Gaur: claim 3 – “wherein determining that the second value exceeds the profitability threshold based at least in part on an original equipment manufacturer (OEM) incentive”).
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 3-4 are rejected under 35 U.S.C. 103 as being unpatentable by Gaur (US 20210233164 A1) in view of (PTO-892 Reference U).
Regarding Claim 3: Gaur discloses the limitations of claim 1 above.
Gaur does not explicitly teach wherein determine the loan parameters comprises:
determining, by the device, a buy lending rate;
determining, by the device, a sale lending rate based on the buy lending rate.
Notably, however, Gaur does disclose determining loan parameters based on profitability (Gaur: [0131]).
To that accord, PTO-892 Reference U does teach wherein determine the loan parameters comprises:
determining, by the device, a buy lending rate; (PTO-892 Reference U – “Buy Rate: This is the interest rate that the lender, often a bank or a financial institution, charges the dealership for the loan. It’s essentially the wholesale rate of the loan. This rate is not usually disclosed to the customer and serves as the base cost of the loan for the dealership).
determining, by the device, a sale lending rate based on the buy lending rate. (PTO-892 Reference U – “Sell Rate: The sell rate, also known as the contract rate, is the interest rate that the dealership offers to the customer. This rate is often higher than the buy rate. The difference between the sell rate and the buy rate is a primary way through which dealerships make a profit on financing… Dealerships can mark up the interest rate provided by the lender. For instance, if the buy rate is 4%, the dealership might offer the loan to the customer at a 6% sell rate. The additional 2% becomes profit for the dealership”).
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 invention of Gaur disclosing the system for determining a deal structure based on probability of acceptance and profitability with the buy rate and sell rate as taught by PTO-892 Reference U. One of ordinary skill in the art would have been motivated to do so in order to allow for the dealership to make a profit on financing (PTO-892 Reference U).
Regarding Claim 4: Gaur in view of PTO-892 Reference U discloses the limitations of claim 3 above.
Gaur does not explicitly teach determining, the profitability as a difference between the sale lending rate and the buy lending rate. Notably, however, Gaur does disclose determining loan parameters based on profitability (Gaur: [0131]).
To that accord, PTO-892 Reference U does teach determining, the profitability as a difference between the sale lending rate and the buy lending rate. (PTO-892 Reference U – “The difference between the sell rate and the buy rate is a primary way through which dealerships make a profit on financing”).
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 invention of Gaur disclosing the system for determining a deal structure based on probability of acceptance and profitability with the profitability determined by the difference of the sale lending rate and the buy lending rate as taught by PTO-892 Reference U. One of ordinary skill in the art would have been motivated to do so in order to allow negotiation for a rate for customers based on the markup of the dealer (PTO-892 Reference U).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable by Gaur (US 20210233164 A1) in view of Lahrichi (US 20200134716 A1).
Regarding Claim 7: Guar discloses the limitations of claim 1 above.
Guar does not explicitly teach training the second machine learning model based on historical loan application approval data to determine the first value indicative of the probability of acceptance of the loan parameters by at least one lending entity, the historical loan application approval data comprising a list of approved/disapproved loan applications, the customer profile for each of the approved/disapproved loan applications, and the financial data for each of the approved/disapproved loan applications. Notably, however, Guar does disclose using historical loan application information to determine probabilities of buyers purchasing the vehicle and the one or more product or services based on the loan information (Guar: [0036]), and receiving customer identifier and financial information (Guar: [0122-0123]).
To that accord, Lahrichi does teach training the second machine learning model based on historical loan application approval data to determine the first value indicative of the probability of acceptance of the loan parameters by at least one lending entity, the historical loan application approval data comprising a list of approved/disapproved loan applications, the customer profile for each of the approved/disapproved loan applications, and the financial data for each of the approved/disapproved loan applications. (Lahrichi: [0110] – “the transaction data and the loan history data may be used to train a first MLA that predicts the likelihood of a request for a loan being approved, such as the MLA 370. The first MLA may be trained using all or a portion of the loan history data and the synthetic loan data. The first MLA may be trained using all or a portion of the transaction data metrics stored at step 408. The transaction data metrics and the loan history data may be correlated by user ID”; Lahrichi: [0111] – “For each entry in the loan history data, the MLA may be provided the loan history data entry and the transaction data metrics for the individual identified in the loan history data entry”; Lahrichi: [0087] – “For each entry in the loan history data, the MLA may be provided the loan history data entry and the transaction data metrics for the individual identified in the loan history data entry”). In summary, the likelihood of a request for a loan being approved is based on loan history data, including approved and disapproved loans, and transaction data metrics for the each entry, including individual (customer profile and financial data).
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 invention of Gaur disclosing the system for determining a deal structure based on probability of acceptance and profitability with the training the model with historical loan application of a list of loan applications and customer profile and financial data as taught by Lahrichi. One of ordinary skill in the art would have been motivated to do so in order to more accurately predict the likelihood of borrower payments and increase lender profitability (Lahrichi: [0002]).
Subject Matter Free of Prior Art
Claims 5 and 6 are determined to have overcome the prior art of rejection and are free of prior art, however, the claims remain rejected under 35 U.S.C. 101, as set forth above.
Claims 5-6 are found to overcome the prior art rejection for the reasons set forth below.
Claim 5 recites the claimed feature of training the first machine learning model based on historical loan application approval data to predict the buy lending, the historical loan application approval data comprising a list of accepted loan applications, a buy lending rate for each of the accepted loan applications, the customer profiled for each of the accepted loan applications, and the financial data for each of the accepted loan applications.
Claim 6 recites training the first machine learning model based on historical loan application approval data and the buy lending rate to predict the sale lending rate, the historical loan application approval data comprising a list of accepted loan applications, the buy lending rate for each of the accepted loan applications, and the sale lending rate for each of the accepted loan applications.
The closes prior art was found to be as follows:
Gaur (US 20210233164 A1) recites [0036] - “during training, the machine learning model may assign weights and/or bias to the loan information, the financial data, and/or vehicle data. The machine learning model may adjust weights in order to optimize a loss function or a cost function such that an error between an output of the machine learning model and an expected output is reduced. The weights may be proportional to the likelihood that a product or service, or information associated with a purchase, will influence the customer to purchase or not purchase a vehicle with a product or service”.
Price (US 20210374616 A1) recites [0011] - “The machine learning model may be trained using data generated by the pricing model, and the data may include historical vehicle information, historical customer information, and historical dealership information” and [0034] – “Training data 129 may be stored in database 207. Computing device 101 may use pricing model 130 to determine a loan amount and loan details (e.g. a loan package or structure) to give to a customer based on the customer and the vehicle. In some embodiments, the loan package may be based on dealership information as well. Computing device 101 may use pricing model 130 to determine certain loan details (e.g. an annual percentage rate, length or loan term, monthly payment) charged for a loan for the customer”.
Venuraju (US 20200126135 A1) recites [0076] – “the offer package system 110 may receive customer financial information. The customer financial information may include a customer's credit history, loan history, employment information, and the like. In some embodiments, the system may be further configured to determine the proposed offer package based on preferred user financing preferences. For example, in some embodiments, the customer may value a lower monthly payment more so than a lower overall purchase price. The personalized financing information may include specific loan terms that the customer has been approved for as they apply to the vehicle of choice” and [0040] – “offer package system 110 may determine personalized financing information for the at least one vehicle based on the customer financial information and the determined promotion recommendation. Accordingly, the offer package system 110 may provide inventory items meeting the customer financial information and display personalized financing information including details such as loan term, loan interest rate, and the monthly payment”.
De La Moutte (US 20080065532 A1) recites [0013] – “FRB allows banks to earn significant profits through the application of two principles of banking: (a) leverage (currently 10:1 in the USA; 20:1 in Canada; 12.5:1 in Europe, etc) of depositor funds that can be loaned and (b) favorable interest rate differences or, the "discounting" of loans at an interest rate that is lower (the wholesale rate at which a bank borrows from its Central Bank) than the rate at which funds are lent or placed into the market (retail rate)”.
PTO-892 Reference V discloses challenges in prediction accuracy of credit defaults in portfolios of loans, and addressing these challenges with machine learning methods adapted to specific needs of credit assessment. Two machine learning models are utilized to identify defaulting and non-defaulting loan contracts, and then use logistic regression to predict a contract’s probability of default.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claims 5 and 6 in combination that overcome the prior art are:
predict the buy lending, the historical loan application approval data comprising a list of accepted loan applications, a buy lending rate for each of the accepted loan applications, the customer profiled for each of the accepted loan applications, and the financial data for each of the accepted loan applications.
predict the sale lending rate, the historical loan application approval data comprising a list of accepted loan applications, the buy lending rate for each of the accepted loan applications, and the sale lending rate for each of the accepted loan applications.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims 5 and 6 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 7:30 - 5:00.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 2/12/2026