DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The amendment received on 01/21/2026 has been acknowledged and entered.
Claims 1, 3, and 11 have been amended.
Claims 5 and 15 have been canceled. No new claims have been added.
Claims 1-4, 6-14, and 16-20 are currently pending.
Response to Amendments and Arguments
Applicant's amendments filed 01/21/2026, with respect to the objection to the claim 11, have been fully considered and are persuasive. Thus, the objection to claim 11 has been withdrawn.
Applicant's arguments filed 01/21/2026 with respect to the rejection of claims 1-4, 6-14, and 16-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 9 -11 of 19) that regarding Step 2A, Prong One, The Office asserts the limitations of claim 1 are directed to certain methods of organizing human activity and directed to mental processes (Office Action Pg. 16 and 17). Applicant respectfully traverses this assertion….and regarding, Organizing Human Activity, Applicant respectfully asserts that the recited interaction between multiple machine learning models cannot properly be characterized as a method of organizing human activity. Amended claim 1, when considered as a whole, recites a sequence of operations performed by multiple machine-learning models in which a first machine-learning model generates a user projection and that projection is then retroactively utilized as training data to train a second machine-learning model that produces a price adjustment. These steps define a technical data-flow architecture in which intermediate computational outputs are repurposed as training inputs to control the behavior of another machine-learning model. This is not an activity that governs, structures, or organizes human conduct, relationships, or interactions, but rather a machine-implemented process for generating and refining model outputs based on learned correlations in digital data.
These steps describe how data is processed, transformed, and propagated between computational models to produce updated outputs, which is a technical process unique to computer systems and not something that can be meaningfully analogized to a human activity such as teaching, negotiating, selling, or following business rules. In particular, the retroactive use of a model-generated projection as training data for another model, and the training of that second model using historical price-adjustment outputs, reflects a feedback-driven machine- learning workflow that improves how the system generates future outputs. Such model-to- model interactions have no counterpart in the enumerated categories of "certain methods of organizing human activity." Accordingly, Applicant respectfully submits claim 1 does not recite a method of organizing human activity.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may
fall within the “certain methods of organizing human activity” grouping. Secondly, the Examiner has reviewed the specification and determined that added limitations are described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept. For instance, paragraph [0019] of the Specification-as-originally-filed discloses that the training is performed by math and a mental process. If a claim recites a limitation that can practically be performed in the human mind, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Further, the use of a physical aid (i.e., the pen and paper) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of this limitation. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, Pages 11-12 of 19) that regarding the Mental Process, … Applicant respectfully asserts that the claimed limitations cannot reasonably be construed as a mental process. Claims do not recite a mental process when they include limitations that cannot practically be performed in the human mind because the human mind is not equipped to carry out the recited operations. Claim 1 requires the operation of multiple machine-learning models, including generating a user projection using a first machine-learning model, retroactively utilizing that projection as training data, training a second machine-learning model using historical outputs of previous price adjustments, and generating a price adjustment using the trained second machine-learning model. These steps define a sequence of algorithmic operations between computational models in which the output of one model is used as training input to another model. The human mind does not contain machine-learning models, cannot generate model outputs in the form required by such models, and cannot use those outputs as training data to retrain another model. Likewise, the human mind cannot train a machine- learning model using historical outputs of prior runs to produce subsequent outputs.
As explained in Synopsys, a claim does not recite a mental process when it involves a "several-step manipulation of data that, except in its most simplistic form, could not conceivably be performed in the human mind or with pencil and paper." The multi-stage, model-driven processing recited in claim 1 falls squarely within this category. The generation of a user projection, the retroactive use of that projection as training data, the training of a second machine-learning model using historical price-adjustment outputs, and the generation of a subsequent price adjustment require computational architectures and iterative model operations that exist only in computer systems and cannot be meaningfully approximated by human thought or manual calculation.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Applicant argues (in REMARKS, Pages 12-14 of 19) that further, examples of claims that do not recite a mental process issued with or after the 2019 PEG are found at least in Example 39 (Method for Training a Neural Network for Facial Detection)" October Update, p. 7. Further details are provided in the Memorandum entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" dated August 4 2025… Applicant respectfully submits that the limitations of claim 1 detailed above are analogous to those of Example 39 because, like Example 39 that utilizes a first training set and a second training set based on the first one to iteratively train a neural network, steps recited in claim 1 disclose, for example, "retroactively utilizing the user projection as training data for a second machine learning model; training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model; and generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment" Thus, analogous to Example 39, claim 1 recites iterative training of a machine learning model through the use of successive training sets derived from prior model outputs, which cannot practically be performed in the human mind and does not recite any fundamental economic practice or other method of organizing human activity.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first Applicant’s claims have a different fact pattern than Example 39. Secondly, unlike Example 39, which uses “digital facial images” as data used train the model to which transformations are applied including “mirroring, rotating, smoothing, or contrast reduction to create a modified set of facial images,” Applicants training and generating steps appear to be related to mathematical calculations which can be performed in the human mind. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 14-16 of 19) that regarding Step 2A, Prong Two, even though claim 1 does not recite an abstract idea as alleged by the Examiner, the claim recites additional elements that integrate the judicial exception into a practical application. Applicant asserts that the Examiner's conclusion that the additional elements of claim 1 amount to no more than mere instructions to apply the exception using a generic computer component reflects an improper application of § 101 and an unduly reductive reading of the claim. The MPEP makes clear that claim language must be construed using the broadest reasonable interpretation that is consistent with the specification and drawings, rather than an interpretation that strips express limitations to their most generic, abstract form. See MPEP § 2111. Under BRI, the Office is not permitted to disregard the claim's recited, model-driven processing steps and recharacterize them as merely "receive," "generate," "train," and "determine" performed by generic components.
Here, claim 1 recites a specific, multi-model computational architecture including, inter alia, "retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model," "training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data," and "transmitting a pecuniary notification comprising a data structure containing price adjustments to a remote device." These limitations impose concrete operational constraints on how data is generated, repurposed, used for training, and output by the system, and thus cannot reasonably be dismissed as "mere instructions" to apply an abstract idea on a generic processor.
In response to Applicant’s arguments, the Examiner respectfully disagrees and notes that the claims use generic computer components as tools to implement the abstract idea of adjusting prices to adapt to market conditions. Generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application – see MPEP 2106.05(h). Further, the courts determined that "[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)); and the courts also determined that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Therefore, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, pages 16 of 19) further, to the extent the Examiner's analysis is premised on the notion that these limitations are "known," "conventional," or otherwise "generic," Applicant notes that novelty is not the test for subject matter eligibility… In light of this, Applicant submits claim 1, as amended, integrates any alleged abstract idea into a practical application and is thus patentable. Claim 11 has been similarly amended and is patentable for at least the reasons discussed above for claim 1.
In response to Applicants argument, the Examiner respectfully notes that first, the Applicant has not include the mechanism which provides an improvement to the system, processor, or models in the claims to provide significantly more. Secondly, the courts determined that "[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)); and "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Because courts have consistently held that claims simply placing an abstract idea into a new field of use do not transform it into a patent-eligible invention, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application.
Applicant argues (in REMARKS, page 18 of 19) that additionally, Applicant respectfully submits that claim 1 recites an inventive concept, at least because claims 1 contains limitation amounting to a non-conventional and non-generic arrangement of process steps. See BASCOM Glob. Internet Servs., Inc. v. AT&TMobility, LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016). "Examiners should keep in mind that the courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic." May 4 USPTO Memorandum at p. 4; see also DDR Holdings, 773 F.3d at 1257. Moreover, "an inventive concept may be found in the non-conventional and non-generic arrangement" even of generic computer operations on a generic computing device. Bascom, 827 F.3d at 1350.
Without conceding that any limitation of claims 1 is generic or conventional, Applicant respectfully asserts that, taken as a whole, limitations to claims 1 amount to a non-conventional and non-generic arrangement of computer and functions and other technical limitations, because the instant Application does not contain any information to suggest that the elements and/or the combination thereof are conventional. There is no evidence to indicate that the retroactive utilization of outputs of a first machine learning model to be used as training data for a second machine learning model would be considered conventional. No other evidence has been provided indicating that such elements and/or their combination is conventional, and Applicant does not admit that the elements and/or their combination are conventional. Applicant therefore respectfully submits that claim 1 recites limitations amounting to an inventive concept, and thus to significantly more than the abstract idea to which claims 1 is allegedly drawn. At least for these additional reasons, Applicant respectfully submits claim 1 recites patent eligible subject matter.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that unlike Bascom, which claimed a “technology based solution”, Applicant has presented an abstract-idea-based solution implemented with generic technical components (machine learning models) a conventional way (feeding input/generating output) to make price adjustments. For instance, determining price adjustments by improving the accuracy of inputs into a machine learning model used to generate price adjustments is not an improvement a technical field. The claims as amended appears to provide an improvement or business solution to a business problem. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, page 19 of 19) that amended independent claim 11 recites limitations similar to amended independent claim 1 and overcomes this rejection for at least the same reasons as discussed above with reference to claim 1. Claims 2-4 and 6-10 depend, directly or indirectly, on claim 1 and thus recite all the same elements as claim 1. Claims 12-14 and 16-20 depend, directly or indirectly, from claim 11 and thus recite all the same elements as claim 11. Applicant therefore submits claims 2-4, 6-10, 12- 14 and 16-20 overcome these rejections for at least the same reasons as discussed above with reference to amended claims 1 and 11.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-4 and 6-10 are directed to an apparatus (i.e., a machine); and Claims 11-14 and 16-20 are directed to a method (i.e., a process). Therefore, claims 1-4, 6-14, and 16-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1 and 11 substantially recite:
receive/receiving user data, wherein the user data comprises an activity record containing a user identification correlated with a plurality of historical services;
receive/receiving system data, wherein the system data comprises a plurality of system geographic locations;
receive/receiving cluster data;
generate/generating at least a trend as a function of the system data and the cluster data;
classify/classifying the plurality of historical services to a service type;
generate/generating a user profile as a function of the service type, wherein generating the user profile comprises:
identifying/identifying at least a corresponding system geographic location from the plurality of system geographic locations as a function of the service type; and
generating/generating the user profile by matching the user identification with the at least a corresponding system geographic location; and
determine/determining a price adjustment as a function of the at least a trend and the user profile comprising:
generating/generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model;
training/training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein the assessment training data correlates the user data and system data t trends and price historical data; and
generating/generating the price adjustment using the trained second model, wherein the second model is configured to receive the output of the first model and generate the price adjustment; and
transmit/transmitting a pecuniary notification comprising a data structure containing price adjustments. The claims as a whole recite a method or organizing human activity. The aforementioned limitations, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation by a certain method of organizing human activity (e.g. method of managing personal behavior or relationships or interactions between people (“receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” and “transmit/transmitting”) and/or commercial activity (“receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining,” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” and “transmit/transmitting”) and/or mental process (“identifying/identifying at least a corresponding system geographic location,” “determining/determining a price adjustment”, “generating/generating,” “utilizing/utilizing,” and “training/training”).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional element, “an apparatus,” “at least a processor,” “a memory,” “instructions,” and “a remote device”; and claim 11 recites the additional element, “at least a processor” and “a remote device” to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” and “transmit/transmitting” steps.
Further, in regards to the “at least a processor” ... “receive/receiving,” “receive/receiving,” “receive/receiving,” “generate/generating,” and “generate/generating,” “generating/generating,” “generating/generating” and “transmit/transmitting” limitations are just more mere data gathering, and also are characterized as transmitting or receiving data over a network; and are also recited at a high level or generality, and merely automates the “receive/receiving,” “receive/receiving,” “receive/receiving,” “generate/generating,” and “generate/generating” “generating/generating,” “generating/generating,” and “transmit/transmitting” steps.
The claimed computer components in the steps are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “an apparatus,” “at least a processor,” “a memory,” “instructions,” “first machine learning model, “ and “second machine learning model,” and “a remote device” in claim 1; and “at least a processor,” “first machine learning model,” “second machine learning model,” and “a remote device” in claim 11 to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining” “generating/generating,” “utilizing/utilizing,” “training/training,” “generating/generating,” and “transmit/transmitting” steps), such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations in claims 1 and 11 is no more than mere instructions to apply the exception using the generic computer components (the apparatus, computing device, processor, first machine learning model, second machine learning model). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are not patent eligible.
Step 2B
The independent claims 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 of using
“an apparatus,” “at least a processor,” “a memory,” “instructions” “first machine learning model,” “second machine learning model,” and “a remote device” in claim 1; and “at least a processor” “first machine learning model, “ “second machine learning model,” and “a remote device” in claim 11 to perform the “receive/receiving,” “receive/receiving,” “receive/receiving” “generate/generating,” “classify/classifying,” “generate/generating,” “identifying/identifying,” “generating/generating,” “determining/determining” “generate/generating,” “utilizing/utilizing,” “training/training,” “generate/generating,” and “transmit/transmitting” steps amounts to no more than mere instructions to apply the exception using a generic computer component or insignificant extra - solution activity. Mere instructions to apply an exception using a generic computer component and merely indicating insignificant extra-solution activity cannot provide an inventive concept. Thus, when viewed as an ordered combination, the independent claim is not patent eligible.
As per dependent claims 2 and 12, the recitations, “generating a notification…”; and “transmitting the notification…” is further directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generating” and “transmitting” limitations are just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation of “a graphical user interface” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 3 and 13, the recitations, “identifying one or more classes of users as a function of the user profile’; “assigning a weight to the one or more classes of users”; and “generating the price adjustments as a function of the assignments” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 11, respectively. Similar to above, the “generating” limitations are just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 4 and 14, the recitation, “receiving the cluster data…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “receiving” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Further, the recitation of “a web crawler” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 6 and 16, the recitation, “generate a relevance score…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generate” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 7 and 17, the recitation, “identifying one or more common incentives… is directed to a method of organizing human activity and/or mental process as described in claims 1 and 11, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 8 and 18, the recitation, “generating one or more restrictions…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “generate” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 9 and 19, the recitations “training… using training data…”; and “classifying the plurality of historical services to the service type using the trained classifier”…. are directed to a method of organizing human activity as described in claims 1 and 11, respectively. Further, the recitation of “a classifier” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 10 and 20, the recitation, “receiving the user data…” is directed to a method of organizing human activity as described in claims 1 and 11, respectively. Similar to above, the “receiving” limitation is just more mere data gathering, and also characterized as transmitting or receiving data over a network, and hence not significantly more. Further, the recitation of “a chatbot” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 11, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
Dependent Claims 2-4, 6-10, 12-14, and 16-20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-4, 6-10, 12-14, and 16-20, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Prior Art Discussion
As per Independent claims 1 and 11, the best prior art,
1) Girija et al. (US PG Pub. 2023/0206265 A1) discloses an optimized dynamic pricing engine which displays on an e-commerce portal, a personalized price of a product according to a price elasticity/price sensitivity of a specific user.
However, Girija et al. alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
As per Independent claims 1 and 11, the best Foreign prior art,
1) MacDonald et al. (CA 3171252 A1) discloses methods and systems for concierge network ; and
2) Chikkaveerappa et al. (CA 3048577 A1) discloses a system and method for generating enhanced distributed online registry.
However, MacDonald et al. and Chikkaveerappa et al., alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
As per Independent claims 1 and 11, the best NPL prior art,
1) Anubhav Kumar Prasad et al., “Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase”, January 2023, International Journal on Recent and Innovation Trends in Computing and Communication 11(1s):43-51, discloses AI can detect both micro- and macro-trends faster than any human; and fancy advertisements won't work if the goods you sell are considerably more expensive than what potential customers are willing to pay. Therefore, a number of e-commerce platforms and online retailers use machine learning to boost personalization through personalized discounts and other promotions (i.e. dynamic pricing) while also optimizing their pricing tactics.
However, Anubhav Kumar Prasad et al., alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The allowable features include:
determine a price adjustment as a function of the at least a trend and the user profile comprising:
generating a user projection using a first machine learning model, wherein the first machine learning model is configured to receive the user profile as an input and generate the user projection as an output;
retroactively utilizing the user projection as training data for a second machine learning model, wherein the user projection is retroactively used as an input of training data to the second machine learning model;
training the second machine learning model using historical data of previous price adjustments generated from the second machine learning model, wherein price assessment training data correlates the user data and system data to trends and price historical data; and
generating the price adjustment using the trained second machine learning model, wherein the second machine learning model is configured to receive the output of the first machine learning model and generate the price adjustment
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Duckworth et al. (US PG Pub. 2023/0099627 A1) discloses machine learning model for predicting an action.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDA A. NELSON whose telephone number is (571)272-7076. The
examiner can normally be reached Monday-Friday, 10:00am - 6:30pm.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628