DETAILED ACTION
Notice of 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 .
Status of the Application
Claims 1-20 were pending and were rejected in the previous office action. Claims 1, 9, and 17 were amended. Claims 1-20 remain pending and are examined in this office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/18/2026 has been entered.
Information Disclosure Statement
The Information Disclosure Statement filed on 4/1/2026 has been considered.
Response to Arguments
35 USC § 101:
Applicant’s arguments regarding the previous § 101 rejection of claims 1-20 (pgs. 9-11, remarks filed 3/18/2026) have been fully considered but they are not persuasive.
Applicant’s arguments primarily focus on Step 2A Prong Two and Step 2B.
Applicant first argues (pg. 9, remarks) that:
“Prong Two: The Examiner asserted that the judicial exception is not integrated into a practical application. Applicant respectfully disagrees and submits that any additional elements reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field.
Applicant respectfully submits that the claimed invention includes a logistic model trainer implemented at least in part in hardware. The logistic model trainer obtains training data and uses the training data to train the logistic model. Applicant respectfully submits that the claimed invention reflects an improvement in the functioning of a computer and/or an improvement to other technology or technical field. Furthermore, the claimed invention cannot practically be performed in the human mind.
Therefore, as any abstract idea is fully integrated into a practical application, the claims are not directed to an abstract idea.”
However, the examiner respectfully disagrees. The additional elements discussed above (a logistic model trainer implemented at least in part in hardware, where the logistic model trainer obtains training data and uses the training data to train the logistic model) describes the use of machine learning technology in its ordinary capacity to perform model training, and does not add anything more than generic computer implementation used to apply the abstract idea. The claims do not improve machine learning technology itself or recite improvements to a particular type of machine learning model itself, but instead recite training a machine learning model (“the logistic model”) using logistic model trainer (which could be a generic computer processor) used to perform ordinary machine learning functions.
See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Applicant further argues (pg. 10-11, remarks) that:
“The Examiner asserted that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. More specifically, the Examiner asserted that the claims recite mere instructions to apply the abstract idea using generic computers/computer components and that the use of the processing device and client device to send or receive data describes generic computing devices being used in their ordinary capacity. Applicant respectfully disagrees.
As mentioned above, Applicant respectfully submits that the logistic model trainer implemented at least in part in hardware, where the logistic model trainer obtains training data and uses the training data to train the logistic model, is a practical application, but, in the alternative, this can be considered as sufficient to amount to significantly more than the judicial exception.
Applicant respectfully submits that the claims include additional elements that are sufficient to amount to significantly more than the judicial exception. For at least the above reasons, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.”
However, the examiner respectfully disagrees, for similar reasons discussed above. Using a logistics model trained to obtain training data and train a logistics model represents the generic application of machine learning technology to apply the abstract idea. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Considering this element in an ordered combination does not alter the analysis above, as the claims merely aim to apply generic computer implementation and/or computers operating in their ordinary capacity to apply the abstract idea.
Therefore, the previous § 101 rejection is maintained. Please see the current § 101 rejection of claims 1-20 below.
Note: Claiming a specific mechanism for training that provides an improvement over conventional machine learning training methods, or that somehow improves the machine learning model or machine learning technology itself, could potentially overcome the current § 101 rejection.
35 USC § 103:
Applicant’s arguments regarding the previous § 103 rejections of claims 1-20 (pgs. 11-13, remarks filed 3/18/2026) have been fully considered and are persuasive. Claims 1, 9 and 17 have overcome the prior art for the reasons discussed below (see “Novelty/Non-Obviousness” section below).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-8 recite “A method…” (i.e. a process); claims 9-16 recite “A system comprising: a memory; and a processing device, operatively coupled to the memory, the processing device to...” (i.e. a machine); and claims 17-20 recite “A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to…” (i.e. an article of manufacture). These claims fall under one of the four categories of statutory subject matter and as a result, pass Step 1 of the subject matter eligibility test. However, “Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection.” See MPEP 2106.04. Accordingly, the examiner continues the subject matter eligibility analysis below.
Step 2A Prong One:
Independent claims 1, 9 and 17 recite limitations for:
receiving historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes;
processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes;
wherein the features comprise continuous features and categorical features,
wherein continuous features are generated by normalizing continuous numerical attributes to a value within a specified range, and wherein categorical features are generated using a vector embedding technique that converts textual information to a vector representation;
converting transaction prices of the plurality of transactions to a plurality of prices per unit;
generating price indices of the plurality of transactions by normalizing the plurality of prices per unit;
providing the price indices, the predicted price, and a subset of the features at an input…and… generate a mapping from the predicted price and the subset of the features to parameters of a predicted win rate curve generated at an output
receiving […] a pricing request that describes a potential future transaction;
generating a predicted win rate curve for the potential future transaction […]; and
sending […] a report comprising the predicted win rate curve, wherein the report indicates, based on the predicted win rate curve, a price range associated with the potential future transaction
These limitations of independent claims 1, 9 and 17 above are determined to recite an abstract idea (i.e. receiving and analyzing historical transaction data and price indices, and generating and outputting a win rate curve and a report in response to a pricing request) for the reasons discussed in the following continued Step 2A Prong One analysis. Note that “An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016).
As per MPEP 2106.04(a)(2)(II), claim limitations which recite commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) fall into the “certain methods of organizing human activity” category of judicial exceptions. Therefore, since the processes described by the limitations above amount to a commercial interaction, e.g. marketing or sales behaviors (i.e. receiving and analyzing historical transaction data and price indices, and generating and outputting a win rate curve and a report in response to a pricing request), the claims fall into the “certain methods of organizing human activity” grouping of abstract ideas.
As described in MPEP 2106.04(a)(2)(III), “[T]he "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” and “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” The limitations recited by the representative independent claims 1, 9 and 17 above, under the broadest reasonable interpretation and but for the use of generic computer components, also cover concepts that can reasonably be performed in the human mind (e.g. observation, evaluation, judgment, and opinion) or by the human mind with the aid of simple tools such as pen and paper. For example, the steps to “receive historical transaction data” and “receive […] a pricing request that describes a potential future transaction” amount to observations, and the steps to “process the historical transaction data…,” “convert transaction prices…,” “generate price indices…,” “providing the price indices, the predicted price, and a subset of the features at an input…and…generate a mapping…at an output,” and “generate a predicted win rate curve…” steps amount to evaluations, judgments, or opinions that could be performed in the human mind or by hand with simple tools (e.g. pen and paper). Further, the step to “send […] a report comprising the predicted win rate curve, wherein the report indicates, based on a predicted win rate curve, a price range associated with the potential future transaction” is analogous to manually outputting, using tools such as pen and paper, a report comprising the same information. Therefore, as the processes above described by the representative independent claims 1, 9 and 17 can be characterized as mental processes (i.e. observation, evaluation, judgment, and opinion), but for the recitation of generic computer components in the claims, the claims fall under the “mental processes” category of judicial exceptions (i.e. abstract ideas).
While claims 1, 9 and 17 are identified by the examiner as reciting concepts that fall under more than one abstract idea grouping (i.e. “certain methods of organizing human activity” and “mental processes”), the examiner considers the limitations together as a single abstract idea for the purposes of the Step 2A Prong Two and Step 2B analysis according to MPEP 2106.04(II)(B).
Step 2A Prong Two:
The judicial exception (i.e. abstract idea) recited in claims 1, 9 and 17 is not integrated into a practical application because the claims recite mere instructions to apply the abstract idea (i.e. receiving and analyzing historical transaction data and price indices, and generating and outputting a win rate curve and a report in response to a pricing request) using generic computers/computer components (i.e. “a logistic model trainer implemented at least in part in hardware” “a client device,” and “using the trained logistic model” of claim 1; “A system comprising: a memory; and a processing device, operatively coupled to the memory, the processing device to…,” and “a logistic model trainer implemented at least in part in hardware,” of claim 9; and “A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to…,” and “a logistic model trainer implemented at least in part in hardware” of claim 17). The use of the processing device and client device to send or receive data are generic computing devices being used in their ordinary capacity. See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.”
Further, the limitations for “training, by a logistics model trainer implemented at least in part in hardware, a logistic model using the training data and a predicted price, wherein training the logistic model comprises providing the price indices, the predicted price, and a subset of the features at an input layer of a neural network and training the neural network to generate a mapping from the predicted price and the subset of the features to parameters of a predicted win rate curve generated at an output layer of the neural network” merely describes the use of generic machine learning models being applied in their ordinary capacity (i.e. training machine learning models, providing inputs to a machine learning model/neural network, and generate an output) to carry out the steps of the abstract idea. The claims do not improve machine learning technology itself or recite improvements to a particular type of machine learning model itself, but instead recite training a machine learning model (“the logistic model”) using logistic model trainer (which could be a generic computer processor) used to perform ordinary machine learning functions, and then uses the trained model for the purposes of generating a mapping of prices to a predicted win rate curve.
See the specification filed 1/22/2024 showing “The price predictions model 116 may be any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like” (¶ 0027) and “The logistic model 128 may be any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like” (¶ 0028). See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Therefore, because the claims, considered as a whole, do not recite anything that integrates the abstract idea into a practical application, the claims are directed to an abstract idea.
Step 2B:
Claims 1, 9 and 17 do not include additional elements, whether considered alone or as an ordered combination, that are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea) because as mentioned above, the claims recite mere instructions to apply the abstract idea (i.e. receiving and analyzing historical transaction data and price indices, and generating and outputting a win rate curve and a report in response to a pricing request) using generic computers/computer components (i.e. “a logistic model trainer implemented at least in part in hardware” “a client device,” and “using the trained logistic model” of claim 1; “A system comprising: a memory; and a processing device, operatively coupled to the memory, the processing device to…,” and “a logistic model trainer implemented at least in part in hardware,” of claim 9; and “A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to…,” and “a logistic model trainer implemented at least in part in hardware” of claim 17). The use of the processing device and client device to send or receive data describes generic computing devices being used in their ordinary capacity.
Further, the limitations for “training, by a logistics model trainer implemented at least in part in hardware, a logistic model using the training data and a predicted price, wherein training the logistic model comprises providing the price indices, the predicted price, and a subset of the features at an input layer of a neural network and training the neural network to generate a mapping from the predicted price and the subset of the features to parameters of a predicted win rate curve generated at an output layer of the neural network” merely describes the use of generic machine learning models being applied in their ordinary capacity (i.e. training machine learning models, providing inputs to a machine learning model/neural network, and generate an output) to carry out the steps of the abstract idea. The claims do not improve machine learning technology itself or recite improvements to a particular type of machine learning model itself, but instead recite training a machine learning model (“the logistic model”) using logistic model trainer (which could be a generic computer processor) used to perform ordinary machine learning functions, and then uses the trained model for the purposes of generating a mapping of prices to a predicted win rate curve.
See the specification filed 1/22/2024 showing “The price predictions model 116 may be any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like” (¶ 0027) and “The logistic model 128 may be any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like” (¶ 0028). See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Considering the additional elements above as an ordered combination does not alter the analysis above, or add anything that amounts to significantly more than the abstract idea.
Dependent Claims 2-8, 10-16, and 18-20:
Dependent claims 2-8, 10-16, and 18-20 are directed to the same abstract idea as independent claims 1, 9 and 17 above as they do not recite anything that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea.
Claims 2-8, 10-16, and 18-20 recite the following limitations which do not do not add any additional elements but merely further describe the abstract idea above by reciting limitations for: “wherein the predicted price is a predicted market price generated by a price prediction model” (claim 2, similar to claim 10); “wherein the predicted price is a predicted market price generated by a price prediction model scaled by a customer-specific price generated by the price prediction model” (claim 3, similar to claim 11); “wherein the features are input to a price prediction model to generate the predicted price, and wherein the subset of the features is selected from the features based on a feature importance score computed for each of the features” (claim 4, similar to claim 12); “wherein the features used to train the price prediction model and the logistic model are not segmented by product, product type, geography, or customer size” (claim 5, similar to claims 13, 18); “minimizing a loss function that characterizes a difference between a predicted price distribution for wins produced from the predicted win-rate curve and an actual price distribution of paid prices in the historical transaction data” (claim 6, similar to claims 14, 19); “wherein the predicted win rate curve is generated without the use of loss data that describes failed transactions” (claim 7, similar to claims 15 and 20); and “wherein processing the request comprises converting attributes of the potential future transaction to additional features, inputting the additional features to the price prediction model to generate an additional price prediction, and inputting a subset of the additional features to the trained logistic model to generate the predicted win rate curve” (claim 8).
The examiner further notes that the limitations for training a logistics model, a price prediction model, or neural network (claims 5-6, 13-14, 18-19) are additional elements performed using computers/processors, but they are merely representative of generic computer models and/or machine learning models being used in their ordinary capacity (e.g. training using input data, receiving input data, and generating output data), without any indication of improving machine learning technology or a neural network itself. See Recentive Analytics, Inc. v. Fox Corp., as cited above. As per the specification at ¶ 0027-0028 the price prediction model and logistics model may be represented by “any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like,” which clearly indicates the claimed invention does not aim to improve the technologies any machine-learning or artificial intelligence models.
Therefore, claims 1-20 are ineligible under § 101.
Novelty/Non-Obviousness
Claims 1-20 are novel and nonobvious over the prior art, for the following reasons:
The closest previously cited prior art includes:
US 20230360118 A1 to Seo et al. (Seo) which teaches a system/method (Seo: ¶ 0002, ¶ 0009-0010, ¶ 0020, ¶ 0030, ¶ 0036, and Fig. 4, Fig. 10) comprising: receiving historical transaction data comprising a plurality of transactions (Seo: ¶ 0012, ¶ 0014, ¶ 00160-0060, and ¶ 0073-0075), each transaction comprising a plurality of attributes (Seo: ¶ 0014 and ¶ 0075); processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes (Seo: ¶ 0084-0086, ¶ 0108-0112); training, by a processing device, a logistic model using the training data and a predicted price (Seo: ¶ 0113, ¶ 0065, ¶ 0075, ¶ 0123, and ¶ 0119-0120), wherein training the logistic model comprises providing the predicted price and a subset of the features at an input layer of a neural network and training the neural network (Seo: ¶ 0113 training the artificial intelligence model; and ¶ 0134) to generate a mapping from the predicted price and the subset of the features to parameters of a predicted win rate curve generated at an output layer of the neural network (Seo: ¶ 0116, ¶ 0118, ¶ 0093, Fig. 5, ¶ 0134); generating a predicted win rate curve for the potential future transaction using the trained logistic model (Seo: ¶ 0093, ¶ 0120-0123); and sending a report comprising the predicted win rate curve (which includes a to an external server (Seo: ¶ 0118-0119), wherein the report indicates, based on the predicted win rate curve, a price range associated with the potential future transaction (Seo: Fig. 9, ¶ 0112-0115 and Figs. 5-8, ¶ 0093-0108).
US 20230245154 A1 to Vuyyuri et al. teaches converting transaction prices of the plurality of transactions to a plurality of prices per unit (Vuyyuri: ¶ 0066-0069 receiving store and online purchase data and including “an identification of one or more items being purchased; a price of each item being purchased”, wherein particular data from the store purchase data and online purchase data is parsed, extracted and stored within the database; for example, ¶ 0069 showing “DFPR computing device 102 may parse online purchase data 310 and extract data associated with the purchase, and store the extracted data within database 116….store the extracted information, which may include one or more of the item IDs, item prices” thereby converting the received purchased data, to extracted and stored data including a price of each item purchased; also see ¶ 0089); generating price indices of the plurality of transactions by normalizing the plurality of prices per unit (Vuyyuri: ¶ 0089 showing “DFPR computing device 102 generates a first plurality of features based on the first sales data and the second sales data. The first plurality of features may include an input dataset, and an expected output dataset. The input dataset may characterize, for example, one or more of a number of each of the plurality of items sold during the first temporal period, a price that each one of the number of each of the plurality of items was sold for, a total amount that each of the plurality of items was sold for, an average amount that each of the plurality of items was sold for; and ¶ 0095 “DFPR computing device 102 generates a plurality of features based on the sales data. For example, the plurality of features may characterize…an average amount that each of the plurality of items was sold for”); and providing generated features (which include an average price per unit/price indices) as input for training a machine learning model (Vuyyuri: ¶ 0091 “DFPR computing device 102 trains a time-series model based on the first plurality of features. For example, DFPR computing device 102 may input the first plurality of features to an executed time-series model…” wherein as per ¶ 0089, ¶ 0095 above, the generated features include an average amount that each of the plurality of items was sold for; at least ¶ 0065 specifies that the model is a machine learning model).
US 20120173344 A1 to Zhang et al. (Zhang) receiving, from a client device, a pricing request that describes a potential future transaction (Zhang: ¶ 0019-0023 showing receiving a bid price request for a transaction), and further teaches in response to receiving a pricing request from a client device, determining and sending bidding price information back to the client device (Zhang: ¶ 0032 “the estimated bid price for the target keyword is returned to the bidder/advertiser as the recommended bid price” and ¶ 0033 “the estimated bid price for the target keyword is returned to the client as the recommended bid price, without further processing. For example, the estimated bid price can be displayed to the bidder at a client device as a recommended bid price that the bidder can accept, ignore, or modify (e.g., via a selection on the display)”).
However, the previously cited prior art does not teach all of the limitations of claims 1 and 9, including, in the context of the claims as a whole: processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes, wherein the features comprise continuous features and categorical features, wherein continuous features are generated by normalizing continuous numerical attributes to a value within a specified range, and wherein categorical features are generated using a vector embedding technique that converts textual information to a vector representation.
Beyond the prior art previously cited, the closest prior art to these particular features is:
US 20230031738 A1 to Zheng et al. teaches processing text features and extracting text information for text embedding (Zheng: ¶ 0010), and performs feature processing to obtain feature vectors (Zheng: ¶ 0056). Zheng also teaches normalized numerical features (Zheng: ¶ 0057).
US 20230050538 A1 to Wang et al. teaches continuous and categorical features, wherein numerical features are converted into vector embeddings in order to process numerical and ordinal features and convert them to categorical features (Wang: ¶ 0038, ¶ 0048-0053).
US 20240403708 A1 to Dang teaches extracting a plurality of features from a machine learning model, normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range (Dang: ¶ 0007, ¶ 0037, ¶ 0043, ¶ 0099, ¶ 0110), and teaches generating feature vectors and updating feature vector with normalized feature vectors (Dang: ¶ 0107-0112).
However, one of ordinary skill in the art still would not have found it obvious to further modify the combination of Seo, Vuyyuri, and Zhang above with a combination of the references identified above in order to arrive at the claimed invention as a whole, including the limitations for processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes, wherein the features comprise continuous features and categorical features, wherein continuous features are generated by normalizing continuous numerical attributes to a value within a specified range, and wherein categorical features are generated using a vector embedding technique that converts textual information to a vector representation. The other previously cited references do not cure the deficiencies above.
Therefore, claims 1, 9 and 17 (and respective dependent claims 2-8, 10-16, and 18-20) are novel and nonobvious over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hunter Molnar whose telephone number is (571)272-8271. The examiner can normally be reached Monday - Friday, 7:30 - 4:00 EST.
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/HUNTER MOLNAR/Examiner, Art Unit 3628
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