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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/20/2026 has been entered.
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, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Claim 1 recites a process, which is a statutory category of invention (Step 1: YES). Claim 11 recites a system, which is a statutory category of invention (Step 1: YES). Claim 20 recites a product (apparatus), which is a statutory category of invention (Step 1: YES).
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a)). Yes.
The claims are analyzed to determine whether it is directed to a judicial exception. The following claims identify the limitations that recite additional elements in bold and the abstract idea without bold. Underlined claim limitations denote newly added claim limitations:
Claim 1, 11 and 20 recite a processor-implemented method of generating an optimal portfolio for seller bidding strategy, the comprising: providing by a seller to multi-energy markets via one or more hardware processor prior to bidding day at least one energy type available with the seller, a bidding price of corresponding energy type, and at least one energy type required by a market participant, wherein the bidding day includes a plurality of timeslots at regular intervals for bidding in at least one energy type among the multi-energy markets; forecasting using a neural hierarchical interpolation for time series forecasting (NHITS) via the one or more hardware processors an energy market price uncertainties of at least one energy type at the plurality of timeslots on the bidding day in the multi-energy markets based on historical market prices, wherein the historical market prices are obtained prior to the bidding day as input, and the NHITS predicts consecutive day market clearing price (MCP) for each energy type market at every timeslot among the plurality of timeslots; providing by the seller to the multi-energy markets via the one or more hardware processors a volume of at least one energy type generated based on a plurality of parameters, wherein the seller being an energy participant provides at least one energy type using a multi-carrier energy (MES) system at locations of a power supplier to the multi- energy markets, wherein the plurality of parameters includes a previous timeslot, a maximum energy limit, a minimum energy limit, a ramp up limit, a ramp down limit and a natural gas input limit; determining by the seller for each energy type in the multi-energy markets via the one or more hardware processors a risk factor based on a market price risk and a forecasting risk based on inputting the historical market price and an actual energy market price of corresponding energy type and computing a total risk for the energy types generated by the seller; and generating via the one or more hardware processors an optimal portfolio for each energy type for the seller to bid in at least one energy market among the multi-energy markets based on the energy market price forecasted for each energy type, the volume of energy type generated by the seller, the plurality of total risks, a conversion cost, a conversion efficiency of energy converter and computing a cumulative return for the multi- energy markets, wherein generating the optimal portfolio comprises solving a quadratic optimization problem by using a portfolio optimization objective function for providing optimal allocations and maximining total sum of revenues depending on portfolio allocations into different energy type markets and the corresponding risk associated due to the MCP fluctuations across different energy type markets, and an equality constraint that ensures the portfolio allocations into multi-energy markets type and equal to supply bid volume at all timeslots, wherein the conversion efficiency of the energy converter is used to convert one unit of a particular energy type (i) to an another energy type j where I (does not equal) j, wherein the optimal portfolio splits the generated volume of each energy type to bid at every timeslot of the multi-energy markets, wherein optimal split of generated energy among different energy types is determined using the optimal portfolio which considers the seller such as revenue or returns, risk taking ability, and asset constraints to maximize revenue risk and tradeoff for a combined cycle gas turbine (CCGT), wherein every risk associated with portfolio options is quantified by a covariance function between the MCP from muti-energy markets and further a risk aversion coefficient k = 0 indicating that the seller has no intend of the risk aversion, with increase in the risk aversion coefficient value k, the risk taken by the seller decreases; simulating, via the one or more hardware processors, the returns with the risk aversion coefficient using data with samples of risk aversion coefficient values to handle volatility with slight or no reduction to the returns until a predefined risk coefficient value, wherein in the MES setup consisting of different energy type sellers, an asset adheres to operational threshold limits to ensure asset safety and operational performance by modelling the asset constraints that ensure asset level ramp and capacity limits, wherein seller capacity adheres to the asset level ramp and capacity conditions for each day ahead market bidding timeslot, wherein the asset includes at least one of a heat pump, the CCGT, and a combined heat and power generator (CHP),wherein each energy generator is provided with access to different energy type markets, wherein transmission line capacity is infinite with no loss in transmission, no cost associated with transmission of energy and linear conversion costs and conversion efficiencies; computing, for each timeslot, an un-utilized natural gas volume based on a contractual natural gas volume, a heat market and an electricity market portfolios using a fuel efficiency factor, and the un-utilized natural gas volume along with the price is traded by the seller into a natural gas energy market, thereby effectively utilizing the natural gas and increasing returns of the energy generator; and using the generated optimal portfolio to bid the generated volume of each energy type at every timeslot of the multi-energy markets by splitting the generated volume of each energy type and optimize by the MES energy flows of different energy types between a plurality of assets including the heat pump, CHP, boilers, electrolysis, fuel cells. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity, but for the recitation of generic computer components. Under human activity, the limitations are commercial interactions, such as sales activities and business relations. Also, under methods of organizing human activity, the claims are managing interactions between people, such as following rules. Lastly, the currently recited claim limitations are mathematical concepts. Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: Yes. The claims recite an abstract idea).
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d)). No.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a hardware processor, multi-carrier energy system (MES), system, memory, communication interfaces, and non-transitory machine-readable information storage mediums. The additional elements of a hardware processor, multi-carrier energy system (MES), system, memory, communication interfaces, and non-transitory machine-readable information storage mediums, are just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The computer components are recited at such a high-level of generality (i.e. as a generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. (Step 2A-Prong 2: NO. The judicial exception is not integrated into a practical application).
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05). No.
The claims are next analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements of (a hardware processor, multi-carrier energy system (MES), system, memory, communication interfaces, and non-transitory machine-readable information storage mediums) in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO; The claims do not provide significantly more, and are not patent eligible).
Claim 3 recites wherein the seller generates at least one energy type using energy producing resources comprising renewable energy, electricity, heat, natural gas, and hydrogen. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 4 recites the fuel efficiency factor is a ratio of a target energy generated to an amount of natural gas fuel consumed by the energy generator. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 5 recites wherein the cumulative return is a product of an expected return of each energy type in corresponding energy market and an amount of energy type trade in corresponding energy market and the conversion efficiency. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 6 recites wherein the market price risk determines the risk due to energy type market price uncertainties. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 7 recites wherein the forecasting risk determines the risk due to a forecasting error of the market price. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 8 recites wherein the forecasting error is determined using the historical market prices and the actual market prices. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 9 recites wherein the total risk is computed based on summing the forecasting risk and the market price risk. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 10 recites wherein the risk factor determines each energy type market price based on variability of the energy type market price and the market price forecasting error. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 13 recites wherein the seller generates at least one energy type using energy producing resources comprising renewable energy, electricity, heat, natural gas, and hydrogen. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 14 recites the fuel efficiency factor is a ratio of a target energy generated to an amount of natural gas fuel consumed by the energy generator. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 15 recites wherein the cumulative return is a product of an expected return of each energy type in corresponding energy market and an amount of energy type trade in corresponding energy market and the conversion efficiency. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 16 recites wherein the market price risk determines the risk due to energy type market price uncertainties, wherein the forecasting risk determines the risk due to a forecasting error of the market price. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 17 recites wherein the forecasting error is determined using the historical market prices and the actual market prices. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 18 recites wherein the total risk is computed based on summing the forecasting risk and the market price risk. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Claim 19 recites wherein the risk factor determines each energy type market price based on variability of the energy type market price and the market price forecasting error. These limitations are also part of the abstract idea identified in claim 11, and are similarly rejected under the same rationale as claim 11, supra.
Response to Arguments
Applicant's arguments filed 4/13/2026 have been fully considered but they are not persuasive.
Applicant argues that the currently recited claims are not directed to an abstract idea. Examiner disagrees. The claim recites abstract ideas in at least two enumerated groupings. First, mathematical concepts, as the claim expressly states a neural hierarchical interpolation for time series forecasting of market clearing prices; a risk factor computed from market-price risk and forecasting risk; a covariance function of MCPs across energy-type markets; a risk aversion coefficient k; solving quadratic optimization problem using portfolio optimization objective function; and conversion efficiencies, fuel-efficiency factors, and related calculations that produce unused natural-gas volume. These are mathematical relationships, formulas, and calculations as recited. Naming NHITS or quadratic optimization does not change the grouping. The 2024 AI examples treated naming training algorithms (backpropagation and gradient descent) as recited mathematical concepts when they appear in the claim.
The claim is also directed to a a seller’s bidding strategy in multi-energy markets; such as receiving available and required energy types and prices, generating an “optimal portfolio” that allocates bid volumes across markets and timeslots, maximizing the sum of revenues subject to risk, and then using that portfolio to bid and to trade unused gas. This is a fundamental economic practice (risk managed allocation and bidding in a market) as well as a commercial interaction.
The claims are not similar to Example 39. Example 39 did not recite a judicial exception with “training a neural network.” Conversely, the currently recited claims involve a method and using an optimal bidding portfolio, and the limitations set forth are mathematical relationships, calculations and a named optimization problem. The currently recited claims also do not involve machine learning, which Example 39 does.
Applicant argues case law with XY, LLC v. Trans Ova Genetics. Applicant is reminded that Examiner is to refer to examination principles found in the MPEP when determining when a claim is eligible or ineligible under 101.
Applicant also argues that the currently recited claims integrate the exception into a practical application (Pg. 15-23). Examiner disagrees. As a whole, the additional elements (generic hardware processor recited as a told to perform forecasting, risk calculation and quadratic program, a multi-carrier energy system described at a high level (heat pump, CCGT, CHP, boilers, electrolysis, fuel cells), conventional operating parameters (such as timeslots, gas-input limit, etc.), and the post-solution act of bidding the computed volumes and trading unused gas. These elements do not integrate the exceptions into a practical application. There is no improvement to the functioning of a computer or to the NHITS model itself (See Enfish or Desjardins). Also, the recited benefits in the specification (such as seller returns, better utilization of contract gas, or optimized energy flows) are business solutions, and not technological improvements to how the plants or computer operates.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON M DUCK whose telephone number is (469)295-9049. The examiner can normally be reached 8am - 5pm.
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/BRANDON M DUCK/Examiner, Art Unit 3693
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