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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/21/2025 has been entered.
Claims Status
Claim 16 is cancelled.
Claim 21 is newly added.
Claims 1-15 and 17-21 remain pending and stand rejected.
Response to Arguments
I. Applicant’s arguments made with respect to the rejection under 35 USC 101 have been fully considered but are not persuasive.
Initially, the Examiner acknowledges the amended features of receiving, via the graphical user interface, a selection of the final aggregate value, and, storing, in the electronic database, a record associated with the request for quotation, wherein the record comprises: the selection of the final aggregate value, the transmitted aggregate values and the respective win probabilities. Although the selection is received via the graphical user interface, the interface is merely the tool by which the selection is made (the selection forming part of the abstract idea). Furthermore, storing a record as claimed is nothing more than storing or retrieving data in memory and/or electronic recordkeeping (which are well0understood, routine and conventional activities).
While Applicant alleges that the claimed invention cannot be performed in the human mind, the Examiner reiterates that the category of abstract idea indicated in the prior rejection and reiterated below is ‘certain methods of organizing human activity’, including commercial processes and/or fundamental economic practices. The finding under Step 2A (Prong 1) is maintained.
Applicant further argues that the claimed invention “continuously evolves and improves its performance through machine learning and real-time feedback, resulting in a self-optimizing system that adapts to changing market conditions”, and asserts that the claimed invention integrates any recited exception into a practical application under Prong 2. The Examiner reiterates similar commentary as previously provided – namely, the improvements argued by Applicant are improvements in the abstract idea itself, and are achieved using generic computing components leveraged as a tool to perform the commercial process efficiently. Notably, the specification indicates that the machine learning techniques are implemented using generic computing equipment (e.g., 0051).
The claimed invention purports to solve problems arising in the realm of commerce, specifically to allow optimizing aggregate values for a request for quotation. Furthermore, the requirement for the model to be updated (retrained) with new data does not represent an improvement to the computer itself or another technology or technical field (e.g., machine learning). Instead, the process for updating as claimed merely uses or applies the machine learning model(s) to the abstract commercial process in order to expedite processing quotation data to “ensure pricing strategies adapt to evolving market conditions and customer behavior”. The Examiner further draws Applicant’s attention to Recentive Analytics, Inc v. Fox Corp (Fed Cir, 2023-2437, 4/18/2025), which held claims to the use of machine learning for generation of network maps and schedules for television broadcasts and live events to be ineligible. The court affirmed the district court in upholding the determination the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment. Similar to Recentive, the current claims seek to use machine learning concepts in a particular environment in an effort to improve the abstract idea rather than the underlying technology of machine learning. Notably, the circuit court maintained the finding despite the recitation of an “iterative” training process.
With respect to Step 2B similar logic as applied under Prong Two is applied herein. The claims, whether considered individually or in their ordered combination, do not provide additional elements or an inventive concept that would transform the mere application of machine learning training into something “significantly more” than the abstract idea itself.
Accordingly, the rejection under 35 USC 101 has been maintained and updated below in view of the amended features.
II. Applicant’s arguments made with respect to the rejection under 35 USC 103 have been fully considered but are moot in view of new grounds of rejection. Applicant’s amendment necessitated the new grounds of rejection.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 21, claim 21 recites establishing, by the second trained machine learning model, a sensitivity function for each item and quantity, wherein the sensitivity function determines changes in the respective win probability in response to variations in aggregate value from an aggregate value for which the win probability is highest among the one or more aggregate values assigned for the request for quotation.
The subject matter of the claim (recited above) does not conform to the disclosure in such a manner in which one of ordinary skill in the art would recognize that applicant actually had possession of at the time of the invention. The most pertinent portion of the disclosure is as follows:
[0048] At step 360, the second machine learning model 156 may establish a sensitivity function for a variety of items and quantities. The sensitivity function determines how the probability of winning a request for quotation is affected by variations in aggregate value from a highest win probability aggregate value.
While the specification may literally support the claimed limitation, merely reproducing the claim limitation in the specification or pointing to an original claim does not satisfy the written description requirement where the claim itself does not convey enough information to show that the inventor had possession of the claimed invention. Elements that are essentially a "black box" will not be sufficient.
Applicant’s specification provides only literal support sans any further disclosure of the functions of establishing a sensitivity function and determines changes in the respective win probability in response to variations in aggregate value from an aggregate value for which the win probability is highest among the one or more aggregate values assigned for the request for quotation.
Moreover, whether one of ordinary skill in the art could devise a way to accomplish these functions is not relevant to the issue of whether the inventor(s) has shown possession of the claimed invention. This is because the ability to make and use the invention does not satisfy the written description requirement if details of how a function (such as the claimed updating) is performed are not disclosed.
Disclosure of function alone is little more than a wish for possession and it does not satisfy the written description requirement. [See MPEP 2163: II(3)(a)(i), Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406]. Moreover, a specification which does little more than outline goals applicant hopes the claimed invention achieves does not satisfy the written description requirement [see MPEP 2163: II(3)(a)(i), In re Wilder, 736 F.2d 1516, 1521, 222 USPQ 369, 372-73 (Fed. Cir. 1984)]. Lastly, the specification lacks adequate description of a “representative number of species” which may satisfy the written description requirement [see MPEP 2163: II(3)(a)(ii)].
It is noted that this is not an enablement rejection. Applicant’s failure to sufficiently describe any algorithm, steps, or procedures taken to perform the claimed functions raises questions as to whether Applicant truly had possession of this feature at the time of filing.
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 1-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-15 and 17-21, under Step 2A claims 1-15 and 17-21 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites a method for optimizing aggregate values for a request for quotation, comprising
receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items;
assigning one or more aggregate values to the request for quotation based on the one or more items and the one or more quantities and a learned association between the one or more items, the one or more quantities, and the one or more aggregate values; and,
assigning a respective win probability for each of the one or more aggregate values based on a learned association between the one or more aggregate values and the respective win probability;
receiving a selection of the final aggregate value; and,
determining, in real-time, if a final aggregate value of the one or more aggregate values offered to the request resulted in a win or loss.
These limitations recite organizing human activity, such as by performing commercial interactions and/or fundamental economic principals or practices (see: MPEP 2106.04(a)(2)(II)). This is because claim 1 sets forth and/or describes optimizing aggregate values for a request for quotation. This represents the performance of a commercial interaction. This also describes concepts relating to the economy and commerce that represent fundamental economic practices.
Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including:
using a first trained machine learning model,
using a second trained machine learning model,
transmitting the one or more aggregate values and the respective probabilities associated with each of the one or more aggregate values to an electronic database; and
transmitting the one or more aggregate values and the respective win probabilities associated with each of the one or more aggregate values to a graphical user interface, and,
[receiving a selection] via the graphical user interface,
updating at least one of the first trained machine learning model or the second trained machine learning model based on the selection of the final aggregate value; and
storing, in the electronic database, a record associated with the request for quotation, wherein the record comprises: the selection of the final aggregate value, the transmitted aggregate values and the respective win probabilities.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited 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). This is further underscored by Applicant’s own disclosure, which describes generic processors, code, and machine learning models (see: 0017-0018, 0051). 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, the additional elements are insufficient to integrate the abstract idea into a practical application 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 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.
In addition to the above, the limitations of transmitting the respective aggregate values and the respective probabilities to an electronic database and/or GUI, and storing, in the electronic database, a record associated with the request for quotation, wherein the record comprises: the selection of the final aggregate value, the transmitted aggregate values and the respective win probabilities represent little more than extra-solution activity (e.g. data gathering and output, storing data) that contributes only nominally or insignificantly to the execution of the claimed method (see: MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 8, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). 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 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.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
electronic recordkeeping,
presenting data
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-7 and 21, dependent claims 2-7 and 21 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1.
Furthermore, certain dependent claims recite additional abstract ideas, such as claims 3-7, which recite mathematical concepts in the form of mathematical relationships, formulas or calculations (e.g., multiplying the optimal value by a respective quantity, summing optimal values, calculating a win probability, generating a graph with aggregate values).
As such, claims 2-7 and 21 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above, and because they set forth further abstract ideas (e.g., mathematical concepts).
Under prong 2 of step 2A, the additional elements of dependent claims 2-7 and 21 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, claims 2-7 and 21 utilize similar additional elements as recited in claim 1, while setting forth additional transmitting limitations. As with claim 1, the additional elements of 2-7 and 21 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). This includes the use of a “sensitivity function” in performing further operations of the 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).
Lastly, under step 2B, claims 2-7 and 21 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
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.
In view of the above, claims 2-7 and 21 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 8-14 (system) and claims 15 and 17-20 (non-transitory computer readable medium), claims 8-14, 15, and 17-20 recite at least substantially similar concepts and elements as recited in claims 1-7 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Acknowledgement is also made of the express recitation of one or more processors (e.g., claims 8-14) and the executable instructions (by a processor) of claims 15 and 17-20. The elements, which are recited only at a high level of generality, are analyzed in accordance with the findings above under Step 2A (Prong 2) and Step 2B. Accordingly, claims 8-14 and claims 15 and 17-20 are rejected under at least similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary (US 2014/0310065 A1) in view of Bryant (US 2023/0334544 A1) and Mitra (US 2020/0226675).
Regarding claim 8, Chowdhary discloses a system for optimizing aggregate values for a request for quotation, the system comprising:
one or more processors configured to perform operations including (see: Fig. 9, 0122):
receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items (see: 0018-0019; see also: 0003);
assigning, using a first trained machine learning model, respective aggregate values to the request for quotation based on the one or more items…and a learned association between the one or more items…and the one or more aggregate values (see: 0030, 0098, 0103 (package price), 0115 (price optimization), Fig. 4 (410 – package price), Fig. 8);
assigning, using a second trained machine learning model, a respective win probability for each of the one or more aggregate values based on a learned association between the one or more aggregate values and the respective win probability (see: 0023, 0104, 0115 (win probability), Fig. 4 (410));
transmitting the one or more aggregate values and the respective win probabilities associated with each of the one or more aggregate values to a graphical user interface (see: Fig. 4 (412, 422), 0116).
Examiner Note: The utility-based model is calibrated by recursively considering the correlation among different commodities and product families, and a choice model is based on the utility function (see: 0023). The utility model may also be refined (e.g., 0070) and the parameters recursively adjusted (e.g., 0103). The utility-based model and choice model are each taught to be used in obtaining an optimal package price (one or more aggregate values) and estimating purchase probability/win probability (a respective win probability) (see: 0022, 0023, 0053, 0080, 0103). Collectively, these models represent a first trained machine learning model and a second trained machine learning model. Note that Applicant’s specification, 0019, discloses that “The machine learning model for the aggregate value determination and the machine learning model for the win/loss probabilities may be separate machine learning models, or they may be sub-models of a single machine learning model”.
Though disclosing all of the above, Chowdhary does not disclose where the one or more aggregate values are assigned based on the one or more quantities and a learned association between the one or more items and the one or more quantities.
Chowdhary also fails to disclose:
transmitting the one or more aggregate values and the respective win probabilities associated with each of the one or more aggregate values to an electronic database,
determining, in real-time, if a final aggregate value of the respective aggregate values offered to the request resulted in a win or loss, and,
receiving, via the graphical user interface, a selection of the final aggregate value;
updating at least one of the first trained machine learning model or the second trained machine learning model based on the final aggregate value, and,
storing, in the electronic database, a record associated with the request for quotation, wherein the record comprises: the selection of the final aggregate value, the transmitted aggregate values and the respective win probabilities.
Notably, Chowdhary does disclose quantity as a parameter of the RFQ (e.g., 0019), learned associations (e.g., 0024, 0030, 0104), and a process which at least alludes to some form of feedback and refinement of the models (e.g., 0023, 0030, 0070, 0103).
To this accord, Bryant discloses a quotation system employing machine learning that is configured to receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items (see: Fig. 1 (110: select product, quantity), 0018). More importantly, Bryant discloses
assigning, using a first trained machine learning model, one or more aggregate values to the request for quotation based on the one or more items and the one or more quantities (see: 0021 (pricing data is estimated based on desired quantity), 0033 (pricing for desire quantity)).
Bryant also discloses a win probability (e.g., probability/likelihood of purchase or conversion of a quote – see: 0024 (predict quote conversion), 0034-0035) and transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to an electronic database (see: 0019 receive feedback signals related to conversion events to improve future performance), 0045, 0052-0053 (retrain one or more machine learning models to improve outputs of price values, probability of conversion, accuracy, and/or other metrics), 0062 (tuned over time based on actual conversion data), Fig. 2A-B (266, 270, 280), Fig. 6 (620, 624, 626)).
Lastly, Bryant also discloses determining, in real-time, if a final aggregate value of the one or more aggregate values offered to the request resulted in a win or loss (see: Fig. 2 (260), 0043),
receiving, via the graphical user interface, a selection of the final aggregate value (see: 0021, 0042-0043, Fig. 2A (270-280), Fig. 2B (260)),
automatically updating at least one of the first trained machine learning model or the second trained machine learning model based on the final aggregate value (see: Fig. 2 (270-280), Fig. 3B (264), 0043, 0045-0046), and,
storing, in the electronic database, a record associated with the request for quotation, wherein the record comprises: the transmitted aggregate values and the respective win probabilities (see: 0063, Fig. 4 (410), Fig. 6 (620)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chowdhary to have utilized the known technique for generating quote data using machine learning in order to have improved Chowdhary’s ability to predict quote conversion and to have improved user interaction event volume with certain products (see: Bryant: 0024).
Though disclosing the above, neither Chowdhary nor Bryant discloses the storage of the selection of the final aggregate value, though Bryant clearly discloses storing a record of quotations. Although the Examiner asserts that the data within the record itself may be considered non-functional, one of ordinary skill would have understood that the storage of such data for use in subsequent training – as detailed in Bryant – was well-established at the time of invention and would have been obvious.
For example, Mitra teaches storing a selection of the final value (see: 0085 (historical winning bids, winning price), 0096 (historical bid data, winning price), 0114 (historical bid data 1016), Fig. 10 (1016)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chowdhary in view of Bryant to have utilized the known technique for storing final values as taught by Mitra in order to have flexibly and accurately generate a digital bid for the digital bid request at significant price reduction with only minimal reductions in success rate to improve overall return (see: Mitra: 0027).
9. The system of claim 8, wherein the data associated with the request for quotation includes information regarding the requestor and historical data associated with prior requests for quotation associated with the one or more items (see: 0044, 0100, 0104, 0118, Fig. 4 (416, 418)).
Regarding claims 1-2 (method) and claim 15 (non-transitory computer readable medium), claims 1-2 and claim 15 recite at least substantially similar concepts and elements as recited in claims 8-9 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 1-2 and claim 15 are rejected under at least similar rationale.
Claim(s) 3-6, 10-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary in view of Bryant and Mitra as applied to claims 1, 8, and 15 above, and further in view of Peterson (US 2001/0011232 A1).
Regarding claim 10, Chowdhary in view of Bryant and Mitra discloses all of the above as noted including wherein the step of assigning the one or more aggregate values comprises:
determining a baseline value for each of the one or more items using the first trained machine learning model (see: Bryant: 0019 (customer alternative sourcing estimates – e.g., benchmark pricing), 0031 (estimated customer alternative or forecasted customer alternative price values));
tuning the baseline value, using the first trained machine learning model, to arrive at an optimal value for each of the one or more items (see: Bryant: 0033-0034 (first price value, which is output based on the customer alternative pricing by the machine learning algorithm));
Furthermore, Chowdhary discloses a quantity (e.g., 0019) as well as an approved price for the entire bundle (e.g., 0027). In the other hand, Bryant discloses a total cost in relation to a quantity of products to be delivered (e.g., Fig. 1A (120), 0065 (outstanding quote total)) The combination, however, does not disclose:
multiplying the optimal value for each of the one or more items by the respective quantity for each of the one or more items;
summing the each of the optimal value and quantity for each of the one or more items to arrive at the one or more aggregate values.
To this accord, and also in the field of quote generation, Peterson discloses multiplying the optimal value for each of the one or more items by the respective quantity for each of the one or more items (see: 0152 (line item total cost (unit cost multiplied by the quantity required)), 0209, 0263);
summing the each of the optimal value and quantity for each of the one or more items to arrive at the one or more aggregate values (see: 0152 (net cost is the total of line item total costs)), 0240 (net cost is the total of item total costs), 0264 (net cost (exclusive of shipping, handling, and tax)).
One of ordinary skill in the art would have recognized that the known technique of Peterson would have been applicable to the invention of Chowdhary in view of Bryant and Mitra as both share common functionality and purpose - namely, to facilitate quotation processing. Moreover, Chowdhary in view of Bryant and Mitra also relates to inventory management, such as by assessing available inventory for quoting purposes (e.g., Bryant: 0003, 0019, 0029).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the invention of Chowdhary in view of Bryant and Mitra in view of the teachings of Peterson in order to have enabled more efficient planning of shipping of items to those areas where supplies are low and demand is high, and to have enabled better usage of the knowledge of the quantity of an item in a vendor level of a distribution network (see: Peterson: 0035).
11. The system of claim 10, wherein the step of assigning the one or more win probabilities comprises:
receiving historical data associated with wins and losses of historical requests for quotations that included at least one of the one or more items in the request for quotation (see: Chowdhary: 0104, Fig. 4 (418); Bryant: 0017 (win loss ratio), 0031 (hit rates, which may be represented by wins and losses with respect to conversion of quotes), 0049 (win / loss or hit rate analysis));
using the second trained machine learning model, calculating a win probability for each of the one or more aggregate values based on a learned association between the aggregate value and the historical data associated wins and losses of the historical requests for quotations (see: Chowdhary: 0097, 0103-0104; Bryant: 0034, 0051).
12. The system of claim 11, wherein a first aggregate value of the one or more aggregate values represents a high aggregate value with a low win probability, a second aggregate value of the one or more aggregate values represents a low aggregate value with a high win probability, and a third aggregate value of the one or more aggregate values represents an optimal aggregate value with a medium win probability (see: Chowdhary: 00091, 0093, 0097; Bryant: 0033, 0044, Fig. 2A (240), 2B (240, 262, 264)); and
Note: the optimized price of Chowdhary represents the second aggregate value, and the other bundle prices are either higher or lower, and represent first, third, and so on prices with respect win probabilities. Bryant further discloses an optimal price (first price value) and updated optimal price (second price value).
the step of transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to an electronic database comprises: transmitting the first aggregate value, the second aggregate value, and the third aggregate value to the electronic database (see: 0019 (receive feedback signals related to conversion events to improve future performance), 0045, 0052-0053 (retrain one or more machine learning models to improve outputs of price values, probability of conversion, accuracy, and/or other metrics), 0062 (tuned over time based on actual conversion data), Fig. 2A-B (266, 270, 280)).
13. The system of claim 11, wherein a first aggregate value of the one or more aggregate values represents a high aggregate value with a low win probability, a second aggregate value of the one or more aggregate values represents a low aggregate value with a high win probability, and a third aggregate value of the one or more aggregate values represents an optimal aggregate value with a medium win probability (see: Chowdhary: 00091, 0093, 0097; Bryant: 0033, 0044, Fig. 2A (240), 2B (240, 262, 264)); and
Note: the optimized price of Chowdhary represents the second aggregate value, and the other bundle prices are either higher or lower, and represent first, third, and so on prices with respect win probabilities. Bryant further discloses an optimal price (first price value) and updated optimal price (second price value).
the step of transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to a graphical user interface comprises: transmitting the first aggregate value, the second aggregate value, and the third aggregate value to the graphical user interface (see: Chowdhary: Fig. 4 (412, 422), 0116; Bryant Fig. 4 (likelihood of purchase), 0064).
Regarding claims 3-6 (method) and claims 17-20 (non-transitory computer readable medium), claims 3-6 and claims 17-20 recite at least substantially similar concepts and elements as recited in claims 10-13 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 3-6 and claims 17-20 are rejected under at least similar rationale.
Subject Matter Allowable Over the Prior Art
Though rejected on other grounds, claim 7 and parallel claim 14 are objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Although previously rejected over Chowdhary, Bryant, Peterson and Vasta, the Examiner no longer finds the position that claim 7 and 14 are rendered obvious as tenable. That is, the Examiner emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not have been obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Though rejected on other grounds, claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rao (US 2024/0249333) discloses utilization of machine learning in relation to brand sensitivity (see: abstract, 0066)
PTO form 892-U discloses application of ML Models for optimizing online auction strategies (see: abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
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WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/Primary Examiner, Art Unit 3619