DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/25/2026 has been entered.
In response to Final Communications received 8/28/2025, Applicant, on 2/25/2026, amended Claim 1, cancelled Claim 17, and added Claims 8-20. Claims 1-20 are pending in this case, with Claims 8-20 being non-elected by original presentation. Claims 1-7 are considered in this application, and have been rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant states the amended limitations and that these amendments provide a technical improvement which cannot be performed in the human mind, and is not a mental process as it is executed on a hosted server. Examiner disagrees as the claims are directed at abstract processes of a “Mental Process” and a “Certain Method of Organizing Human Activity”, as the claims recite limitations for the purposes of a Commercial Interaction through observations, evaluations, and judgments, which is clearly both a Mental Process and Method of Organizing Activity as per the rejection below, and this is a mere allegation of eligibility under 101. A human mind can absolutely determine a price for the cost of fuel at a price pump. Further, the claims are not practically integrated, as the claim limitations merely utilize current technologies such as a hosted server in a cloud computing environment, graphical user interface, etc. to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for the purpose of determining a cost and displaying competitors’ reactions to the cost. There is no improvement to a technology or any technological process, as the Applicant has not even identified what technology this would be, and the server, interface, and other technologies utilized are not improved. This is “Applying It”, similar to Alice, on a generic computing system, and any purported improvement is part of the abstraction and thus this is not significantly more nor practically integrated.
Applicant asserts the claims are integrated into a practical application by citing the amended limitations particularly the use of a hosted server which enables rapid scaling of processing power and generation of optimized prices for daypart pricing at multiple retail levels. Examiner disagrees as the claims are not practically integrated, as the claim limitations merely utilize current technologies such as a hosted server in a cloud computing environment, graphical user interface, etc. to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for the purpose of determining a cost and displaying competitors’ reactions to the cost. There is no improvement to a technology or any technological process, as the Applicant has not even identified what technology this would be, and the server, interface, and other technologies utilized are not improved. This is “Applying It”, similar to Alice, on a generic computing system, and any purported improvement is part of the abstraction and thus this is not significantly more nor practically integrated. The enabling of rapid scaling of processing power is at best intended use, and the Specification does not support that this occurs, even stating that this “may” occur, but it also may not.
Applicant asserts the claims are significantly more by citing the amended limitations particularly the use of a hosted server in a cloud computing environment with rapid scaling of processing power that executes three distinct technical models. Examiner disagrees as the abstract claim limitations of using models to determine price changes and other analyzed information merely utilize current technologies such as a hosted server in a cloud computing environment, graphical user interface, etc. to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for the purpose of determining a cost and displaying competitors’ reactions to the cost. There is no improvement to a technology, any technological process, or any additional element, alone or in combination. This is “Applying It”, similar to Alice, on a generic computing system, and any purported improvement is part of the abstraction and thus this is not significantly more nor practically integrated.
Therefore, the arguments are non-persuasive, the Claims are ineligible, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/20/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Election/Restrictions
Claims 8-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being non-elected by original presentation, there being no allowable generic or linking claim, and the claims would be restricted for the reasons below:
Restriction to one of the following inventions is required under 35 U.S.C. 121:
Claims 1-7, drawn to a method for displaying competitor’s reactions to price proposals, classified in CPC class G06Q 30/0283, price estimation.
Claims 8-14, drawn to a method for outputting price proposals, classified in CPC class G06Q 30/0206, price determination based on market factors.
Claims 15-20, drawn to a system for user review of price proposals, classified in CPC class G06Q 10/0637, strategic management.
Inventions I – III are related as sub-combinations disclosed as usable together in a single combination. The sub-combinations are distinct if they do not overlap in scope and are not obvious variants and if it is shown that at least one sub-combination is separately usable. In the instant case, sub-combination I has separate utility such as processing, by a reaction model executed on the hosted server, the prices to predict competitors' price movements as a function of the store's price movements and determining one or more positions for the set of days for the store compared with at least a subset of the competitors' stores, whereas sub-combination II has separate utility such as obtaining price data including prices with time and date stamps for the store and one or more competitors' stores and outputting the price proposals and predicted competitors' reactions including lag times for the competitors' reactions, and sub-combination III has separate utility such as automatically adjusting a price at the store based on the price proposals without human intervention when the price proposals are within set boundaries; and requiring user review when the price proposals are outside the set boundaries.. See MPEP § 806.05(d).
The examiner has required restriction between sub-combinations usable together.
Where applicant elects a sub-combination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable sub-combination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their different classification;
(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries);
(e) the inventions are likely to raise different non-prior art issues under 35 U.S.C. 101 and/or 35 U.S.C. 112, first paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-7 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 is directed at “executing machine learning processing on a hosted server in a cloud computing environment, wherein the hosted server provides rapid scaling of processing power for processing large data sets” and “wherein the rapid scaling of processing power enables generation of optimized prices for daypart pricing at multiple retail price levels throughout a day”. There is nothing in the Specification which shows how this rapid scaling of processing power occurs, nor does it explain how the hosted server provides rapid scaling of processing power, or how this accomplished. The only place in the Specification what states this is shown here:
“[0085] In an embodiment, machine learning algorithm and processing may be
incorporated into the realm of price optimization (e.g., retail fuel price optimization). The machine learning processing may be performed in a hosted server (e.g., processing server 310), which may provide rapid scaling of processing power when large data sets are being processed. For example, the hosted machine learning service (e.g., machine learning service 410) may be available in a cloud computing environment through the Internet or a network 301. Current hosted machine learning service providers include Amazon (see "Amazon Machine Learning", available at https://aws.amazon.com/machine-learning/, herein fully incorporated by reference); Microsoft (see "Machine Learning", available at https://azure.microsoft.com/en-us/services/machine-learning/, herein fully incorporated by reference); and Google (see "Google Cloud Machine Learning at Scale", available at https://cloud.google.com/products/machine-learning/, herein fully incorporated by reference).
[0086] In an embodiment, the rapid scaling of processing power enables optimized prices to be generated more rapidly. The rapid generation of optimized prices allows fuel retailers to take advantage of day part pricing, where they can price fuel at several retail price levels throughout the day, for example during the morning commute, over lunch, and during the evening commute. ”
Which is the only description what this is in the Specification, and this is a very generic description, but does not define how this would provide rapid scaling of processing power, how the processing power has anything to do with the Claims, or how these operations are performed. There are no details or description as to how a server provides rapid scaling of processing power, nor how this leads to optimized prices. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that a patent must describe the technology; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). The dependent Claims inherit the deficiencies of the independent claims and thus are similarly rejected.
Therefore, the claims and their dependent claims are rejected under 35 U.S.C. 112(a), written description, as being directed to non-statutory subject matter.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor.
Claim 1 recites the use of “executing machine learning processing on a hosted server in a cloud computing environment, wherein the hosted server provides rapid scaling of processing power for processing large data sets” and “wherein the rapid scaling of processing power enables generation of optimized prices for daypart pricing at multiple retail price levels throughout a day”. Applicant’s specification is silent as to what these are as per the specification above, and as best taken from above, this is a server using machine learning. For Examination purposes this will be taken as any hosting server with machine learning incorporated, which can perform the limitations of the claims.
The dependent claims inherit the deficiencies of the independent, and thus the dependents are similarly rejected.
Claim 1 recites the limitation of “executing machine learning processing on a hosted server in a cloud computing environment, wherein the hosted server provides rapid scaling of processing power for processing large data sets”. It is unclear here what “large data sets” would be, as the metes and bounds of this are unclear, and the specification is silent as to what “large” would be, and this would differ person to person as to what is considered a large data set. For examination purposes the term “large data set” will be taken as any data set.
The dependent claims inherit the deficiencies of independent claims they rely on and thus are similarly rejected.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is directed to the limitations to obtaining prices, the prices including a price for each day of a set of days within a time period for each of the stores and one or more of competitors' stores (Collecting Information, an Observation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); obtaining volume data for the set of days for the store and the competitors' stores (Collecting Information, an Observation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); processing, by a reaction model executed on the hosted server, the prices to predict competitors' price movements as a function of the store's price movements, including expected price moves and timing of expected price moves (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); processing, by a positioning model executed on the hosted server, the prices and the volume data to determine price positions for achieving an objective for the product or service (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); processing, by a forecast model executed on the hosted server, the prices and the volume data using machine learning techniques to generate volume forecasts (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); determining, using the reaction model, the positioning model, and the forecast model, one or more variables for the set of days from data for the product or service, wherein the variables are used for determining an objective for the product or service (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); determining one or more positions for the set of days for the store compared with at least a subset of the competitors' stores (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); determining conditional probabilities for attaining the objective based on the prices, the volume data, the variables, and the positions (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); generating one or more price proposals for the store and competitors' reactions to each of the price proposal based on the conditional probabilities for attaining the objective, wherein the predicted competitors’ reactions include a lag time for the competitors’ reactions (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); wherein the rapid scaling of processing power enables generation of optimized prices for daypart pricing at multiple retail price levels throughout a day (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity); and displaying the competitors' reactions including the lag time to each of the price proposal (Transmitting the Analyzed Information, a Judgment, a Mental Process; a Commercial Interaction, a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of a commercial interaction, but for the recitation of generic computer components. That is, other than reciting executing machine learning processing on a hosted server in a cloud computing environment, wherein the hosted server provides rapid scaling of processing power for processing large data sets and a graphical user interface, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Commercial Interaction. For example, determining one or more variables for the set of days from data for the product or service… encompasses an employee or manager identifying/picking variables to use in a model, an evaluation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, as described above, the claims recite limitations for a Commercial Interaction, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The graphical user interface, hosted server, and cloud computing environment are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above are insignificant extra-solution activity as these are receiving and transmitting data as per the MPEP 2106.05(d). Accordingly, 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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0053] In one embodiment, any number of conventional techniques for data transmission, signaling, data processing, network control, and the like as one skilled in the art will understand may be used. Further, detection or prevention of security issues using various techniques known in the art, e.g., encryption, may be also be used in embodiments of the invention. Additionally, many of the functional units and/or modules, e.g., shown in the figures, may be described as being "in communication" with other functional units and/or modules. Being "in communication" refers to any manner and/or way in which functional units and/or modules, such as, but not limited to, input/output devices, computers, laptop computers, PDAs, mobile devices, smart phones, modules, and other types of hardware and/or software may be in communication with each other. Some non-limiting examples include communicating, sending and/or receiving data via a network, a wireless network, software, instructions, circuitry, phone lines, Internet lines, fiber optic lines, satellite signals, electric signals, electrical and magnetic fields and/or pulses, and/or the like and combinations of the same.”
Which shows that any desktop computer, smartphone, PDA, etc. can be used to perform the abstract limitations of the Claims, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the hosted server, cloud computing equipment, etc., nor the receiving and transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-7 contain the identified abstract ideas, further narrowing them, with no new additional elements when considered under prong 2A as part of a practical application or under 2B, and thus not practically integrated nor significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-7 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejection and any other rejections.
The closest prior art of record are Fox (U.S. Publication No. 2015/0081392), Hoffberg (U.S. Publication No. 2010/031,7420), and Keller (U.S. Publication No. 2017/009,8257). Fox, a competitor prediction tool, teaches receiving historical prices, through a network, from one or more sources, the historical fuel prices including a fuel price for each day of a set of days within a time period for each of the one or more fuel stores and one or more of competitors' fuel stores, receiving historical fuel volume data for the set of days for the one or more fuel stores and the one or more competitors' fuel stores, outputting the fuel price forecast for the one or more fuel stores, receiving a selection of a fuel price based on one or fuel prices in the fuel price forecast, transmitting, through a network, to the one or more fuel stores, data for automatically changing one or more of a machine-controlled fuel sign and a point of sale system at the one or more fuel stores the selected fuel price, generating a fuel price forecast/prediction model for the one or more fuel stores, wherein the fuel price forecasts are output through an optimization model, wherein the forecast model is processed through learning trained which is trained with vectors such as in one or more of the following inputs for generation of the fuel price forecast: store number, date, day of week, week, month, holiday, store price, lowest competitor price, competitor reaction to price change, average competitor price, highest competitor price, closest competitor prices, differentials with competitors prices, actual cost, actual margin, actual cost change, volume average, replacement margin, replacement cost, replacement cost change, last year volume, and volume, it does not explicitly state this is for correlation, nor does it teach the fuel historical fuel prices of the competitors fuel stores are received via third-party services. Hoffberg, a system and method for bidding and auctioning fuel teaches determining variables for the purpose of determining a goal the variables and constraints are determined which are determined relevant for finding an objective using determined probabilities and conditional probabilities which are performed by machine learning using correlation, and outputting the fuel price forecast for the one or more fuel stores, the fuel data is transmitted for the fuel prediction, receiving a selection of a fuel price based on one or fuel prices in the fuel price forecast where a selected path by a user for the determination of fuel and the model and transmitting, through a network, to the one or more fuel stores, data for changing one or more of a machine-controlled fuel sign and a point of sale system at the one or more fuel stores the selected fuel price, but it does not explicitly state a machine-controlled sign, or use of third-party services for receiving of historical fuel sales figures. Keller, a system and method for optimizing retail fuel stores, teaches the proposed prices are propagated/transmitted to the electronic signs based on the optimization point which also uses a learning model to achieve this optimization, but not the use of three models, a reaction model, positioning model, and forecast model, to determine one or more variables, processing prices to predict competitors’ price movements as a function of the store’s price movements, and determining price positions for achieving an objective for a product/service. None of the above prior art explicitly teaches this use of three models, a reaction model, positioning model, and forecast model, to determine one or more variables, processing prices to predict competitors’ price movements as a function of the store’s price movements, and determining price positions for achieving an objective for a product/service., as claimed, in combination with the other limitations as pointed out by Applicant in the Remarks of 2/25/2026 on pgs. 4 and 5, and these are the reasons which adequately reflect the Examiner's opinion as to why Claim 1 is allowable over the prior art of record, with the Claim and it’s dependents objected to as provided above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170098257 A1
Keller; John Windsor
System and Method for Optimizing Retail Fuel Stores
US 20150081392 A1
Fox; David et al.
COMPETITOR PREDICTION TOOL
US 20100317420 A1
Hoffberg; Steven M.
SYSTEM AND METHOD
US 20200380569 A9
Keller; John Windsor
System and Method for Optimizing Retail Fuel Stores
US 20200057982 A1
CARROLL; Gideon et al.
SYSTEM AND METHOD FOR FUEL STORAGE TANK INVENTORY MANAGEMENT
US 20180068358 A1
Hoffberg; Steven M.
SYSTEM AND METHOD FOR DETERMINING CONTINGENT RELEVANCE
US 20150332298 A1
Ettl; Markus R. et al.
PRICE MATCHING IN OMNI-CHANNEL RETAILING
US 20150317653 A1
Ettl; Markus R. et al.
OMNI-CHANNEL DEMAND MODELING AND PRICE OPTIMIZATION
US 20140344021 A1
Thalken; Jason
REACTIVE COMPETITOR PRICE DETERMINATION USING A COMPETITOR RESPONSE MODEL
US 20140222518 A1
Adkins; Stephen
METHODS AND SYSTEMS FOR SETTING OPTIMAL HOTEL PROPERTY PRICES
US 20140188589 A1
Call; Brad F. et al.
Price Match Loyalty Program
US 20140081793 A1
Hoffberg; Steven M.
SYSTEM AND METHOD
US 20130297428 A1
Chatter; Mukesh et al.
System For And Method Of Automatic Optimizing Quantitative Business Objectives Of Sellers (Advertisers) With Synergistic Pricing, Promotions and Advertisements, While Simultaneously Minimizing Expenditures and Discovery and Optimizing Allocation Of Advertising Channels That Optimize Such Objectives
US 20130097664 A1
Herz; Frederick et al.
SECURE DATA INTERCHANGE
US 20110004513 A1
Hoffberg; Steven M.
SYSTEM AND METHOD
US 20100235285 A1
Hoffberg; Steven M.
GAME THEORETIC PRIORITIZATION SYSTEM AND METHOD
US 20090254971 A1
Herz; Frederick S. M. et al.
SECURE DATA INTERCHANGE
US 20090099902 A1
Chatter; Mukesh et al.
System for and method of automatic optimizing quantitative business objectives of sellers (advertisers) with synergistic pricing, promotions and advertisements, while simultaneously minimizing expenditure discovery and optimizing allocation of advertising channels that optimize such objectives
US 20080249920 A1
Kirch; Michael et al.
FUEL OFFERING AND PURCHASE MANAGEMENT SYSTEM
US 8706311 B2
Kosaka; Yoko et al.
Electric power demand/supply planning apparatus and method for the same
US 8538795 B2
Fell; Robert M. et al.
System and method of determining a retail commodity price within a geographic boundary
US 11354758 B2
Racusin; Michael A.
Online system for retail gas sales
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 6/24/2026
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