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 .
Status of Application
Claim(s) 1-20 were previously pending and were rejected in the previous office action. Claim(s) 1, 5-6, 9, 11, 14-15, 18-20 were amended. Claim(s) 2-4, 7-8, 12-13, 16-17 were left as originally/previously presented. Claim 10 was cancelled. Claim(s) 1-9 and 11-20 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 27, 2026, has been entered.
Information Disclosure Statement
The information disclosure statements (IDS’s) submitted on July 11, 2026, and August 19, 2026, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) are being considered by the examiner.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments, see page(s) 11-13 of Applicant’s Response, filed June 27, 2026, with respect to ‘Alice,’ 35 USC § 101 rejection of Claim(s) 1-9 and 11-20 have been fully considered but they are not persuasive.
First, Applicant argues, on page(s) 11-12, that the amended Independent
Claim(s) 1, 11,and 19, do not fall within the revised Step 2A prong 1 framework under the grouping of “Certain Methods of Organizing Human Activity.” As an initial matter, the above grouping along with its sub-groupings can encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-grouping(s).
In this case, Independent Claim 1 recites “receiving historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes and a transaction price,” “processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes and price indices generated from the transaction price,” “training, a price predication model using the training data, wherein the price prediction model comprises both a market rice model and a customer-specific price model,” “wherein training the price prediction model comprises training to generate a mapping between the features and the price indices,” “wherein the features used to train are not segmented and correspond with a plurality of products, product types, geographics, and customer sizes,” “optimizing, predictive capabilities by iteratively adjusting hyperparameters,” “receiving, a pricing request that describes a potential future transaction,” “generating a price prediction for the potential future transaction using the trained price prediction model,” and “sending, a report comprising the price prediction,” step(s) are merely certain methods of organizing human activity: fundamental economic practices or principles (e.g., mitigating risks and/or hedging) and/or commercial or legal interactions (e.g., sales activities and/or business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions).
Independent Claim(s) 11 and 19 recites “receive historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes and a transaction price,” “process the historical transaction data to generate training data comprising features extracted from the plurality of attributes and price indices generated from the transaction price,” “train, a price predication model using the training data, wherein the price prediction model comprises both a market price model and a customer-specific price model,” “wherein to train the price prediction model comprises to train to generate a mapping between the features and the price indices,” and “wherein the features used to train are not segmented and correspond with a plurality of products, product types, geographics, and customer sizes,” and “optimizing the predictive capabilities by iteratively adjusting hyperparameters,” function(s) are merely certain methods of organizing human activity: commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found
that that processing a credit application between a customer and dealer, where the
business relation is the relationship between the customer and the dealer during the
vehicle purchase was merely a commercial transaction, which, is a form of certain
methods of organizing human activity. In this case, the claim(s) are similar to a business
relationship between an entity and customers. The entity can receive transaction information and a pricing request. The entity can then generate pricing predictions/quotes, which the pricing can then be presented to the customer. Thus the claims are directed to the abstract idea of a business relationship such as providing pricing information to a customer. Also, see offer-based price optimization which was determined to be a fundamental economic principle or practice, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Furthermore, as an initial matter, the courts do not distinguish between mental processes that are performed by humans and claims that recite mental processes performed on a computer, see MPEP 2106.04(a)(2)(III). As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).
Similar to, Electric Power Group v. Alstom, S.A., when the court provided that a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps, which, were recited at a high level of generality such that they could practically be performed in the human mind.
Here, applicant’s claim limitations are recited at a high level of generality that can be performed in the human mind when the limitations recite receiving historical transaction data (i.e., collecting) and receiving a pricing request (i.e., collecting). The system can then process the historical transaction data to generate training data that includes attributes and price indices (i.e., analyzing). The system can then train the price predictions and generate a price prediction for potential future transactions (i.e., analyzing). The system will then send the price report (i.e., displaying), thus collecting data and analyzing that price training data set, which the system will then display the pricing information is merely related to a mental processes.
Also, see performing a mental process on a generic computer of Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01, and MPEP2106.04(a)(2)(III)(C). Therefore, the claim(s) recite at least an abstract idea of mental processes. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mentally. The claims would still fall under certain methods of organizing human activity, see the above analysis.
Second, Applicant argues, on page 12, that the invention provides that the application is now integrated into a practical application thus sufficient to amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, it is important to note that first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"), see MPEP 2106.04(d)(1). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Here, in this case the specification discloses a solution to enables the system to improve modeling technique to generate pricing recommendations without the use of data segmentation, see applicant’s specification paragraph 0015. This is at best an improvement to the abstract idea (e.g., determining and generating pricing recommendations for future transactions) itself rather than a technological improvement.
First, the step(s) of accomplishing this desired improvement in the specification is made in blanket conclusory manner by merely stating the system improve modeling technique to generate pricing recommendations without the use of data segmentation, which can make the use or memory more efficient while using less memory, see paragraph(s) 0015 and 0017, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
While applicant argues that the claimed invention reflects an improvement when the model trainer iteratively adjusts the hyperparameters of the neural network, which adjusts the structure of the neural network, which adjusts the structure of the neural network, resulting in optimal predictive capabilities of the neural network, see applicant’s arguments on page 12 and applicant’s specification paragraph 0043. Examiner, respectfully, disagrees. As an initial matter, applicants limitations lack the details as to how the neural network structure is changed and how that is an improvement to the neural network.
Furthermore, the court in, Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), stated “[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. Here, the neural network is not an improvement when the model trainer is merely iteratively training the neural network using previously determined hyperparameters, because merely using a neural network to iteratively train using hyperparameters is inherent to the nature of machine learning and is not, in itself, a technological improvement.
Furthermore, neural networks generated are recited as having been trained based on one or more hyperparameters varied using a hyperparameter tuning for the respective neural network. Such training is not a technological implementation detail. The hyperparameters are recited as comprising a number of hidden layers and the number of neurons in each hidden layer, a learning rate of each model, activation functions, or batch size to optimize in training each neural network, see applicant’s specification paragraph(s) 0043 and 0047. These are not technological implementation details. At best they are administrative goals for price predictions using a generic computer (e.g., neural network model).
Also, another important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP §2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, processing, training, optimizing, adjusting, receiving, generating, and sending, respectively, node information using computer components that operate in their ordinary capacity (e.g., a processing device, a neural network, three parallel subnetworks, a deep neural network, a cross network, a time network, a training module, a feature extraction module, a model trainer, a client device, a memory, a model trainer, and a non-transitory computer-readable storage medium), which are no more than “applying,” the judicial exception.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[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." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. In this case, there is no improvement to the neural network, merely providing an neural network to be used in determining price predictions for future transactions is not enough to be considered significantly more.
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), stated “[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. Here, the model trainer is not an improvement when the model trainer merely iteratively trains hyperparameters for the neural network is inherent to the nature of machine learning and is not, in itself, a technological improvement. Thus, applicant’s argument is not persuasive.
Third, Applicant argues on page(s) 12-13 of applicants’ arguments, that the Claims are not well-understood, routine, or conventional activity and amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: (1) Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; (2) Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): (3) Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re- evaluation finds that the element is unconventional or otherwise more than what is well- understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) Evaluate whether any additional element or combination of elements are other than what is well- understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d), see MPEP 2106.5(B)(II).
Examiner respectfully notes that in the Final Office Action mailed 04/07/2026 on page 16, the Step 2B prong was used to analysis the previous Step 2A Prong Two additional elements that merely amounted to describing how to generally “apply,” the abstract idea in a computer environment thus Examiner carried over the identification of the additional elements and conclusions of the additional elements that were analyzed under Step 2A Prong Two, which the analysis also explained how the limitations were not an improvement to the technology.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, processing, training, optimizing, adjusting, receiving, generating, and sending, respectively, node information using computer components that operate in their ordinary capacity (e.g., a processing device, a neural network, three parallel subnetworks, a deep neural network, a cross network, a time network, a training module, a feature extraction module, a model trainer, a client device, a memory, a model trainer, and a non-transitory computer-readable storage medium), which are no more than “applying,” the judicial exception.
It should also be noted that when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity, see MPEP 2106.05(d). In this case, examiner provided why these limitations are not sufficient to show an improvement (e.g., Affinity Labs v. DirecTv. and Recentive Analytics, Inc. v. Fox Corp.) and how the limitations amount to mere instructions to apply an exception, see the above analysis in the argument section(s). Thus, the claims do not provide an improvement to the vehicle selection optimization. Therefore, Applicant’s arguments are found to be unpersuasive.
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.
Claim(s) 1-9 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim(s) 1-9 recite “A method…,” (i.e., a process); Claim(s) 11-18 recite “A system comprising: a memory; and a processing device, operatively coupled to the memory, the processing device to:…,” (i.e., a machine); and Claim(s) 19-20 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 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 1: Independent Claim(s) 1, 11, and 19 recites an entity that receives historical transaction data, which is the historical data and other parameters are used to train a model to then generate and output a pricing report. Independent Claim(s) 1, 11, and 19, as a whole recites limitation(s) that are directed to the abstract idea(s) of certain methods of organizing human activity: fundamental economic practices or principles (e.g., mitigating risks and/or hedging) and/or commercial or legal interactions (e.g., sales activities and/or business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions) and/or mental processes (e.g., observation, evaluation, judgment, and/or opinion).
Independent Claim 1 recites “receiving historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes and a transaction price,” “processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes and price indices generated from the transaction price,” “training, a price predication model using the training data, wherein the price prediction model comprises both a market rice model and a customer-specific price model,” “wherein training the price prediction model comprises training to generate a mapping between the features and the price indices,” “wherein the features used to train are not segmented and correspond with a plurality of products, product types, geographics, and customer sizes,” “optimizing predictive capabilities by iteratively adjusting hyperparameters,” “receiving, a pricing request that describes a potential future transaction,” “generating a price prediction for the potential future transaction using the trained price prediction model,” and “sending, a report comprising the price prediction,” step(s) are merely certain methods of organizing human activity: commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions) and/or mental processes (e.g., observation, evaluation, judgment, and/or opinion).
Independent Claim(s) 11 and 19 recites “receive historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes and a transaction price,” “process the historical transaction data to generate training data comprising features extracted from the plurality of attributes and price indices generated from the transaction price,” “train, a price predication model using the training data, wherein the price prediction model comprises both a market price model and a customer-specific price model,” “wherein to train the price prediction model comprises to train to generate a mapping between the features and the price indices,” “wherein the features used to train are not segmented and correspond with a plurality of products, product types, geographics, and customer sizes,” and “, “optimizing predictive capabilities by iteratively adjusting hyperparameters,” function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles (e.g., mitigating risks and/or hedging) and/or commercial or legal interactions (e.g., sales activities and/or business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions) and/or mental processes (e.g., observation, evaluation, judgment, and/or opinion).
Furthermore, as, explained in the MPEP and the October 2019 update, where a series of step(s) recite judicial exceptions, examiners should combine all recited judicial exceptions and treat the claim as containing a single judicial exception for purposes of further eligibility analysis. (See, MPEP 2106.04, 2016.05(II) and October 2019 Update at Section I. B.).For instance, in this case, Independent Claim(s) 1, 11, and 19, are similar to an entity that receives historical information for generating training data for predicting pricing information. The entity can then generate a pricing report after receiving a query for a pricing request by a user. The mere recitation of generic computer components (Claim 1: a processing device, a client device, a neural network, three parallel subnetworks, a feature extraction module, a deep neural network, a cross network, model trainer, a training module, and a time network; Claim 11: a memory, a processing device, a neural network, a model trainer, a training module, a feature extraction module, three parallel subnetworks, a deep neural network, a cross network and a time network; and Claim 19: a non-transitory computer-readable storage medium, a feature extraction module, a training module, a model trainer, a processing device, a neural network, three parallel subnetworks, a deep neural network, a cross network and a time network) do not take the claims out of the enumerated grouping certain methods of organizing human activity and/or mental processes. Therefore, Independent Claim(s) 1, 11, and 19, recites the above abstract idea(s).
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the claims as a whole describes how to generally “apply,” the concept(s) of “receiving,” “processing,” “training,” “training,” “optimizing,” “adjusting,” “generating,” “training,” “receiving,” “generating,” and “sending,” respectively, information in a computer environment. The limitations that amount to “apply it,” are as follows (Claim 1: a processing device, a client device, a neural network, three parallel subnetworks, a feature extraction module, a deep neural network, a cross network, model trainer, a training module, and a time network; Claim 11: a memory, a processing device, a neural network, a model trainer, a training module, a feature extraction module, three parallel subnetworks, a deep neural network, a cross network and a time network; and Claim 19: a non-transitory computer-readable storage medium, a feature extraction module, a training module, a model trainer, a processing device, a neural network, three parallel subnetworks, a deep neural network, a cross network and a time network). Examiner, notes that the processing device, client device, neural network, three parallel subnetworks, feature extraction module, deep neural network, cross network, model trainer, training module, time network, memory, and non-transitory computer-readable storage medium, respectively, are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely receiving, processing, training, training, optimizing, adjusting, generating, receiving, generating, and sending, information using computer components that operate in their ordinary capacity (e.g., processing device, client device, neural network, memory, non-transitory computer-readable storage medium, three parallel subnetworks, deep neural network, model trainer, a feature extraction module, a training module, cross network and time network), which are no more than using instructions to implement the abstract idea using generic computer components wherein the focus of the claim as a whole is directed to a result or effect that itself is the abstract idea thus merely amounting to “applying,” the judicial exception.
Also, see "[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." See, Recentive Analytics, Inc. v. Fox Corp. Here, nothing in the claims or specification indicates that applicant has improved machine learning technology itself, but instead merely uses it for the purpose of generating a different type of price predictions. See, applicants specification paragraph 0025 that merely provides “the price predictions models 116 may be any suitable type of artificial intelligence model, machine learning model, artificial neural network, and the like….”
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), stated “[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.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). Each of the above limitations simply implement an abstract idea that is no more than mere instructions to apply the exception using a generic computer component, which, is not a practical application of the abstract idea. Therefore, when viewed in combination these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the above abstract idea(s).
Step 2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted previously, the claims as a whole merely describe how to generally “apply,” the abstract idea in a computer environment. Thus, even when viewed as a whole, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible.
Claim(s) 2, 4, and 12 : The various metrics of Dependent Claim(s) 2, 4, and 12 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Independent Claim(s) 1 and 11, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 3 and 13: The additional limitation(s) of describing “converting,” and “scaling,” are further directed to a method of organizing human activity and mental processes, as described above for Independent Claim(s) 1 and 11, respectively.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely converting and scaling, information which is no more than “applying,” the judicial exception. The recitation(s) of “converting the transaction price to a unit price that describes a price per unit of a product identified in the transaction,” and “scaling the unit price by a normalizing value,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 3 and 13, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 5 and 14: The additional limitation(s) of describing “training,” is further directed to a method of organizing human activity and mental processes, as described above for Independent Claim(s) 1 and 11, respectively. The limitations that amount to “apply it,” are a deep neural network, and cross network. Examiner, notes that the deep neural network and cross network, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely training, information which is no more than “applying,” the judicial exception. The recitation(s) of “train using the cross features and the side features,” and “train using only cross features,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 5 and 14, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 6 and 15: The additional limitation(s) of describing “training,” is further directed to a method of organizing human activity and mental processes, as described above for Independent Claim(s) 1 and 11, respectively. The limitations that amount to “apply it,” are a deep neural network and a time network. Examiner, notes that the deep neural network and time network, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely training, information which is no more than “applying,” the judicial exception. The recitation(s) of “train using the features,” and “train using time features, wherein the time features are a subset of the features that exhibit a greater time-dependent effect on prices due to trend or seasonality effects,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 6 and 15, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 7 and 16: The additional limitation(s) of describing “multiplying,” is further directed to a method of organizing human activity and/or mathematical concepts and/or mental processes, as described above for Independent Claim(s) 1 and 11, respectively. The limitations that amount to “apply it,” are a time network. Examiner, notes that the time network, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely multiplying, information which is no more than “applying,” the judicial exception. The recitation(s) of “multiplying an output by one or more time functions to capture the trend and seasonality effects of the subset of the features,” falls within certain methods of organizing human activity and/or mental processes and/or mathematical concepts.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 7 and 16, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 8 and 17: The additional limitation(s) of describing “passing,” and “capturing,” are further directed to a method of organizing human activity and/or mental processes, as described above for Independent Claim(s) 1 and 11, respectively. The limitations that amount to “apply it,” are a layer. Examiner, notes that the layer, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely passing, information which is no more than “applying,” the judicial exception. The recitation(s) of “passing the time functions directly to the price prediction model to capture the seasonality and trend across all of the features,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 8 and 17, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 9 and 18: The additional limitation(s) of describing “providing,” “outputting,” and “passing,” are further directed to a method of organizing human activity and/or mental processes, as described above for Independent Claim(s) 1 and 11, respectively. The limitations that amount to “apply it,” are a parallel subnetworks and last layer. Examiner, notes that the parallel subnetworks and last layer, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely providing, outputting, and passing, information which is no more than “applying,” the judicial exception. The recitation(s) of “provide an output to a price prediction model,” and “passing the price prediction model to an activation function to generate the price prediction,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim(s) 9 and 18, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 20: The additional limitation(s) of describing “training,” are further directed to a method of organizing human activity and/or mental processes, as described above for Independent Claim 19, respectively. The limitations that amount to “apply it,” are a deep neural network, cross network, and time network. Examiner, notes that the deep neural network, cross network, and time network, are generically claimed that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely training, information which is no more than “applying,” the judicial exception. The recitation(s) of “train using the cross features and the side features,” “train using only the cross features, and “train using the time features,” falls within certain methods of organizing human activity and/or mental processes.
Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747- 48; and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). For the reasons described above with respect to Claim 20, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2-9, 12-18, and 20 above do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) in the dependent claim(s) above are no more than mere instructions to apply the exception using generic computer component(s), which, do not provide an inventive concept. Therefore, Claim(s) 1-9 and 11-20 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Neural network hyperparameter optimization for prediction of real estate prices in Helsinki,” by Jussi Kalliola, Jurgita Kapociute-Dzikiene, and Roertas Damasevicius, April 19, 2021, (hereinafter Neural). Neural teaches using hyperparameters to train a neural network. The neural network hyperparameters consist of batch size, optimization algorithm , and learning rate, loss function, dropout, and the number or epochs. The system can then predict a price for the real estate. However, Neural, doesn’t explicitly teach a neural network that includes a three parallel subnetwork, a deep neural network, a cross network, and a time network, that provides output to a last layer of the prediction model.
“Hyperparameter-Optimized RNN, LSTM, and GRU Models for Airline Stock Price Prediction: A Comparative Study on THYAO and PGSUS,” by Funda H. Sezgin, Omer Algorabi, Gamze Sart, and Mustafa Guler, November 7, 2025, (hereinafter RNN). RNN teaches using hyperparameters such as number of layers, learning rate, batch size, activation functions, and dropout rates for optimizing machine learning models. The system can then predict stock prices. However, RNN, doesn’t explicitly teach a neural network that includes a three parallel subnetwork, a deep neural network, a cross network, and a time network, that provides output to a last layer of the prediction model.
Benkreira et al. (US 2021/0224810 A1). Benkreira et al. teaches a transaction management platform that trains the third data model based on the set of features. The neural network an input layer, one or more intermediate layers (e.g., a fully connected layer, a convolutional layer, a pooling layer, a recurrent layer, and/or the like), and an output layer. The set of features may be hyperparameters that are used as part of a cost function of the neural network. Benkreira et al., further, teaches the transaction management platform may perform a feedforward technique to provide particular transaction characteristic values and/or merchant review data values as input to the neural network, and the neural network may output one or more risk scores that represent, based on the particular transaction characteristic values and/or the merchant review data values, a level of risk associated with the transaction, a likelihood of a particular transaction being a legitimate or illegitimate transaction. However, Benkreira et al., doesn’t explicitly teach a neural network that includes a three parallel subnetwork, a deep neural network, a cross network, and a time network, that provides output to a last layer of the prediction model. Benkreira et al., also, doesn’t explicitly teach predicting a price based on a future transaction and then generating a pricing report.
Mohanty et al. (US 2023/0028266 A1). Mohanty et al. teaches a neural network that is tuned based on hyperparameters. The hyperparameters can change a loss function, optimizer algorithm, and/or change the neural network architecture by adding more hidden layers. However, Mohanty et al., doesn’t explicitly teach a neural network that includes a three parallel subnetwork, a deep neural network, a cross network, and a time network, that provides output to a last layer of the prediction model. Mohanty et al., also, doesn’t explicitly teach predicting a price based on a future transaction and then generating a pricing report.
Jin et al. (US 2020/0020014 A1). Jin et al. teaches an artificial neural network is characterized by features of its model, the features including an activation function, a loss function or cost function, a learning algorithm, an optimization algorithm, and so forth. Jin et al., also, the hyperparameters are set before learning, and model parameters can be set through learning to specify the architecture of the artificial neural network. The structure of an artificial neural network may be determined by a number of factors, including the number of hidden layers, the number of hidden nodes included in each hidden layer, input feature vectors, and target feature vectors. Jin et al., further, teaches hyperparameters may include various parameters which need to be initially set for learning, much like the initial values of model parameters. Jin et al., also, the model parameters may include various parameters sought to be determined through learning. The hyperparameters may include initial values of weights and biases between nodes, mini-batch size, iteration number, learning rate, and so forth. Jin et al., also, teaches the model parameters may include a weight between nodes, a bias between nodes, and so forth. The system can adjust the model parameters to reduce the loss function. However, Jin et al., doesn’t explicitly teach a neural network that includes a three parallel subnetwork, a deep neural network, a cross network, and a time network, that provides output to a last layer of the prediction model.
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
