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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 01, 2026 is 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 § 112
Applicant’s amendments and arguments, see page 9 of Applicant’s Response, filed March 17, 2026, with respect to the rejection under 35 U.S.C. 112(b) has been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection has been withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s arguments, see page(s) 9-11 of Applicant’s Response, filed March 17, 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) 9-10, 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,” “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).
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,” 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, 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.
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. 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(s) 10-11, 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.
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, 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 client device, a memory, 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). Therefore, applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, see page(s) 11-14, filed March 17, 2026, with respect to the 35 U.S.C. 103 have been fully considered and are persuasive. The 35 U.S.C. 103 has been withdrawn.
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 is 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: 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 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,” “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,” and “wherein the features used to train are not segmented and correspond with a plurality of products, product types, geographics, and customer sizes,” 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) 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 deep neural network, a cross network and a time network; Claim 11: a memory, a processing device, a neural network, 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 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,” “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 deep neural network, a cross network and a time network; Claim 11: a memory, a processing device, a neural network, 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 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, memory, non-transitory computer-readable storage medium, three parallel subnetworks, deep neural network, cross network and time network, 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, 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, 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 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.
Novelty/Non-Obviousness
For the reasons outlined below, Independent Claim(s) 1 and 11 are distinguished from the art.
Zagorin et al. (US 12,051,041 B2). Zagorin et al. teaches a system that uses a machine learning model that receives transaction data, which includes historical transaction data collected from any number of various purchaser entities over a time period. The transaction data includes historical line-item data including prices or products of products or services previously procured, quantities of the products or services previously procured, unit of measure of the products or services. Zagorin et al., also, teaches a data ingestion server collects and then provides the transaction data to a relational database, which is used training the one or more machine-learning models. The transaction data includes prices of products, quantities of the product, and units of measure for the products. The transaction data can be inputted to the one or more predicative procurement models, which iteratively train to generate predictions of price quotations for procuring particular products or services. Zagorin et al., also, teaches the predictive procurement model can receive the newly proposed transaction, which can then generate a prediction of a price quotation for the transaction However, Zagorin et al., doesn’t explicitly teach training the price prediction model based on market prices and customer specific prices. The neural network can generate mapping between the features and the price. The neural network includes three parallel subnetworks, a deep neural network, a cross network, and a time network, which they provide output to a last layer of the price predication model. The features are trained in the neural network, which the features are not segmented and correspond to products, product types, geographics, and customer sizes.
Hauser (US 2023/0153850 A1)(filed on November 15, 2021). Hauser teaches a system that uses a neural network that list products to establish a linkage between product description and price. Hauser, also, teaches a neural network model can use a regression analysis or other suitable statistical methods to establish the relationship between product description and price form the product. Hauser, further, teaches the model is trained using training data to include input from any number of products and/or at a city, county, state, national, international, or global level sufficient to obtain reliable statistical data. Hauser, also, teaches that the neural network model uses the linkage between the product description and price to determine a product list and applies it on the product description to make a pricing prediction. The product descriptions can include any product type(s) currently existing or non-existing product.
Wang (CN-112765482-A). Wang teaches the deep network contains the multiple connected layers of that includes the cross network. The deep network is used to capture deeper non-linear relationships between users and the products. The cross network uses the cross features to establish side features. Wang, also, teaches a cross network that is used to learn cross features. Wang, further, teaches that the cross network uses features vectors to obtain the cross feature. However, Wang, doesn’t explicitly teach a price prediction model that uses both a market price model and a customer-specific price model. Wang, also, doesn’t explicitly teach a neural network that includes a three parallel subnetwork and a time network that provides output to a last layer of the prediction model.
Emura (US 2013/0041774 A1). Emura teaches a seasonal coefficient that is multiplied by a usage time ratio. The system can determine that products usage time varies based on the calendar day and season. Emura, further, teaches that the system can multiply the seasonal coefficient Q by the usage time ratio coefficient c[n] by the date and time coefficient T[n]. The system can use this equation to determine the usage end date and time for the product, which can be used to determine purchase of a product based on certain timing. However, Emura, doesn’t explicitly teach a price prediction model that uses both a market price model and a customer-specific price model. Emura, also, 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.
Zhou (CN-117217804-A). Zhou teaches a neural network that includes layers. The neural network builds a model that is built based on received trends and seasonality. The neural can determine an optimal price for the products. The neural network can dynamically adjust the product price based on the season information, such as certain clothing brands are in demand in certain seasons. However, Zhou, 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.
“Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022,” by Zhang, C., Sjarif, N. N. A., & Ibrahim, R., May 14, 2023, (hereinafter Deep). Deep teaches a deep neural network that includes multiple parallel hidden layers. The deep neural network also includes input layer, three hidden layers, and an output layer. The deep neural network model is used for price forecasting. Deep, also, teaches the deep neural network includes a hidden layer and multiple other hidden layers. The layer can receive neuron information, which an activation function is applied to the output of the neuron through the layer. The deep neural networks include models for price forecasting. However, Deep, doesn’t explicitly teach a price prediction model that uses both a market price model and a customer-specific price model. Deep, also, doesn’t explicitly teach a neural network that includes a cross network and a time network that provides output to a last layer of the price prediction model.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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