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 .
Response to Arguments
The Amendment filed on September 16, 2025 has been entered. The examiner acknowledges that no claims were amended.
Rejections under 35 U.S.C. § 101: Applicant argues that the claims do not contain limitations that can be practically performed in the human mind. If that was in fact the acceptable rationale, any and all claims employing a computer to perform calculations would be allowable subject matter just based on the fact that a human cannot process at the speed of a computer.
In performing analysis for allowable subject matter, hardware and software are
removed from the initial analysis of the independent claim so that the root abstractions of the claims can be identified. This approach enables a focus on the more important aspects of allowable subject matter in the presence of abstract ideas, a practical application, most commonly seen as improving the functioning of a computer or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, MPEP § 2106.05(b).
Applicant argues that claims evaluate as having abstract ideas including [but not limited to] fundamental economic principles or practices include hedging, insurance, and mitigating risks do not include claims 1-14, but a fundamental economic principle would likely include considering costs as part of “by helping developers determine how to size the grid, the type of equipment, etc.,” [Arguments, p.8].
Applicant argues that the invention is recognized as providing an improvement. Examiner cites the earlier rebuttal that the presence of a computer performing calculations can be an improvement, but the practical application is based on improved functioning of the computer or using the judicial exception in conjunction with a particular machine that is integral to the claim. The knowledge derived from the invention does not appear to be acted upon by the invention beyond providing output to a human, “generating, with the at least one processor, a recommended operation or investment decision, [claim 1].” Applicant argues that “the claimed subject matter does not merely recite using a computer as a tool,[p.13]. Examiner disagrees, that is exactly what claim 1 describes.
The Examiner does not find the arguments for withdrawing the rejections to be compelling based on the above discussion. The request for rejection withdrawal and claim allowance is denied.
Rejections under 35 U.S.C. § 103: Applicant argues that prior art fails to suggest features of the claims. Examiner disagrees, noting that the Applicant’s claimed projection factors easily equate to statistics to characterize an expected response; the multi-year horizon (to forecast for the energy system) is not unlike the forward-looking approach of the prior art; impact of future forecasts using a discount rate synonymous to applying an agreed upon discount rate; determining an operation or investment would be hard to differentiate from making capital investments in infrastructure; and improving metrics for the multi-year horizon based on technology would likely entail calculations based on new infrastructure as stated in the prior art.
Examiner finds the arguments that prior art does not disclose or suggest similar features to be not compelling and maintains that prior art does in fact suggest that the features in the claims have been previously taught. The Examiner finds that the claims are not in condition for allowance and the request to withdraw the 35 U.S.C. § 103 rejections is denied.
Claim Rejections – 35 U.S.C. § 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 1-14 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-14 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-14 are directed to a process (method), machine (system), which are statutory categories of invention.
Step 2A
Claims 1-14 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of projecting forecasts for an energy system, based on data, over a multiyear horizon and determine an operation or investment to reduce costs or improve metrics and generating an investment decision.
Claim 1 discloses a method comprising:
obtaining, input data for an energy system; (following rules or instructions, observation, evaluation, judgement, opinion),
determining, one or more projection factors based on the input data, (following rules or instructions, observation, evaluation, judgement, opinion),
the one or more projection factors to condense forecasts for the energy system, over a multiyear horizon, into a single number to represent future conditions associated with the energy system, (following rules or instructions, observation, evaluation, judgement, opinion),
where the one or more projection factors tune the impact of the future forecasts using a discount rate; (following rules or instructions, observation, evaluation, judgement, opinion),
determining, an operation or investment associated with the energy system to achieve lower cost or improve one or more metrics of the energy system for the multiyear horizon based at least in part on the one or more projection factors and a description of technology or infrastructure of the energy system; (following rules or instructions, observation, evaluation, judgement, opinion),
generating, a recommended operation or investment decision for the energy system based at least in part on [data analysis and evaluation]; and storing, the recommended operation or investment decision, (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk).
Additional limitations specify the input data is a state of the energy system representing technology assets on-site or in the energy system, system constraints, and a forecast of inputs over the multiyear horizon, (following rules or instructions, observation, evaluation, judgement, opinion – claim 2), where forecast inputs are generated, (following rules or instructions, observation, evaluation, judgement, opinion – claim 3), where projection factors are determined for each timestep of the forecasts for time dependent input data, (following rules or instructions, observation, evaluation, judgement, opinion – claim 4), where a single projection factor is determined across all time-steps of the multiyear forecasts, (following rules or instructions, observation, evaluation, judgement, opinion – claim 5), where new investments from the method are added to the input data of follow-on iterations, (following rules or instructions, observation, evaluation, judgement, opinion – claim 6), determining using an adaptive multiyear approach, accurate dispatch for each year of the multiyear horizon based on the investment, or an incremental dispatch by combining the adaptive multiyear approach and the one or more projection factors, (following rules or instructions, observation, evaluation, judgement, opinion – claim 7),
Each of these claimed limitations employ mental processes involving managing personal behavior, following rules or instructions, mental processes, judgement, observation, evaluation and opinion, as well as organizing human activity - fundamental economic principles based on mitigating risk.
Claims 8-14 recite similar abstract ideas as those identified with respect to claims 1-7.
Thus, the concepts set forth in claims 1-14 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-14 recite additional limitations which are hardware or software elements such as at least one processor, memory storing instructions that when executed by the at least one processor, cause the at least one processor to perform operations and a machine learning model, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-14 are directed to abstract ideas.
Step 2B
Claims 1-14 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-14 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 are rejected under 35 U.S.C. § 103 as being taught by Crabtree (US 20100332373 A1) hereafter Crabtree, “System and Method for Participation in Energy-related Markets,” in view of Pecenak, (US 20200334609A1), hereafter Pecenak, “Adaptive, Multiyear Planning Method For Energy System, Microgrid, Or Distributed Energy Resource (DER), Involves Generating Recommended Operation Or Investment Decision For System For Specified Time Horizon Through Output Device”.
Regarding Claim 1, A method comprising:
obtaining, with at least one processor, input data for an energy system; Crabtree teaches, (a multidimensional energy decision system, comprising a plurality of server systems, including at least a statistics server and an interface adapted to receive and send digital information from a client system and further adapted to communicate with at least some of the plurality of server systems via a packet-based data network, [0034]),
determining, with the at least one processor, one or more projection factors based on the input data, (statistics server 1030 calculates a plurality of statistics based on data take from or derived from one or more of a configuration database 1022, a transaction database 1021, and an event database 1020, [ ] statistics server 1030 is used to characterize an expected response profile of a plurality of end users of a digital exchange1000, which response profile may be for a particular period of time or for any period of time; optionally time-specific and time-independent response profiles for a plurality of end users may both be calculated. According to another embodiment of the invention, statistics server 1030 is used to characterize expected response from a response package built up from a plurality of end user response profiles, which expected response may be for a particular period of time or for any period of time; optionally time-specific and time-independent response forecasts for a plurality of response packages may both be calculated, [0105]), the one or more projection factors to condense forecasts for the energy system, over a multiyear horizon, Crabtree does not teach, Pecenak teaches, (forecast driven input data (601) and a first description of technology or infrastructure of a current system, [0049], and longer time horizons provide the benefit of incorporating price or regulatory changes. The conventional method of extending the time horizon is referred to hereafter, as the “forward-looking” approach, [0004] and the effect of the single year projection is that all input data is considered frozen for the entire project lifetime, which is unrealistic. However, Microgrid and DER project planning decisions produce recommendations for investment, operation, and placement that apply to equipment and infrastructure with lifespans up to 50 years which can be significantly different than a single year projection in terms of capital investment or financial agreements, [0005]), into a single number to represent future conditions associated with the energy system,
The use of a single number to represent future conditions associated with the energy system provides a benchmark with no criteria, therefore the setting of such a requirement would be a design choice with no benefit to the utility of the invention. Regardless, Crabtree teaches (a response profile reflects an amount of load likely to be actually reduced (or generated) by a user, when requested, [0079], and a number of response profiles are combined to create a response package. Because statistical behavior of users whose profiles are combined in the response package is known, and because a large number of profiles are normally combined into a package, it is possible according to the invention to estimate with good accuracy how much load reduction (or generation) each response package represents, [0081]), it would be obvious for one of ordinary skill in the art to rearrange parts of an invention, in this case choose appropriate predictive information to enable a future forecast. (Because a large number of profiles are normally combined into a package, it is possible according to the invention to estimate with good accuracy how much load reduction (or generation) each response package represents, Crabtree [0005]), (MPEP 2144.04 I, 2144.04 VI (C)).
where the one or more projection factors tune the impact of the future forecasts using a discount rate; Crabtree teaches, (these participants agree ahead of time that, when they fail to take a requested action which they should, according to their established preferences have taken, then their accounts will be decremented by the same high price or the same price with an agreed upon discount rate. That is, they have to pay when they fail to meet their obligations, [0148]),
determining, with the at least one processor and based on a machine learning model, an operation or investment associated with the energy system to achieve lower cost or improve one or more metrics of the energy system for the multiyear horizon based at least in part on the one or more projection factors and a description of technology or infrastructure of the energy system; Crabtree teaches, (a wide variety of machine learning techniques may be employed in concert with a network-connected controls network that is capable of monitoring and controlling facilities through a variety of industrial automation systems and controls technologies that are well established in the art, [0276], and one method of lowering the cost of electricity may be to make capital investments in infrastructure in order to relieve the congestion. Simulation and modeling server 2300 is used to calculate a likely return on investment (ROI) of the new infrastructure by comparing a cost of building the new infrastructure against expected savings in electricity costs over a specified time period, [0197]),
generating, with the at least one processor, a recommended operation or investment decision for the energy system based at least in part on output of the machine learning model; (A wide variety of machine learning techniques may be employed in concert with a network-connected controls network that is capable of monitoring and controlling facilities through a variety of industrial automation systems and controls technologies that are well established in the art. Advantages of the instant invention pertain, among other things, to the ability of systems according to the invention to use expert user content in conjunction with observed information and analysis to provide forward-looking business intelligence, or decision-support, [0276]), and storing, with the at least one processor, the recommended operation or investment decision, (According to the invention, configuration changes may also constitute events and be stored in event database 1020, enabling an audit trail to be maintained (that is, configuration database 1022 stores the current configuration but event database 1020 will have a complete record of changes to configuration database 1022), and all of these exemplary events are stored in event database 1020, [0102]).
Crabtree and Pecenak are both considered analogous to the claimed invention because they are in the field of energy system planning production and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy system economic planning of Crabtree with the multi-year forward looking approach of Pecenak to produce recommendations for investment, operation, and placement that apply to equipment and infrastructure with lifespans up to 50 years which can be significantly different than a single year projection in terms of capital investment or financial agreements, Pecenak, [0005].
Regarding claim 2, the method of claim 1, wherein the input data is a state of the energy system representing technology assets on-site or in the energy system, system constraints, and a forecast of inputs over the multiyear horizon, Crabtree does not teach, Pecenak teaches, (Referring to FIG. 1 the adaptive method is initialized with a timeseries of input data 102 that includes forecast driven and / or user - specified input data 102a including, but not limited to: climate resource, utility cost, technology costs, energy demand, regulatory tariffs and taxes which is either user specified or generated from an Al - enabled, statistical, or deterministic forecast. Input data 102 also includes existing technology / infrastructure 102b at the energy site. A year index “y” can be initialized to zero during initialization and is incremented until the year equals a specified time horizon “Y”, [0029}.
Crabtree and Pecenak are both considered analogous to the claimed invention because they are in the field of energy system planning production and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy system economic planning of Crabtree with the multi-year horizon of Pecenak to determine operation and investment to achieve lowest cost and improve metrics over the current system for the one to “Y” year time horizon, Pecenak, [0030].
Regarding claim 3, the method of claim 2 wherein the forecast of inputs is generated using machine learning, Crabtree teaches, (By utilizing a combination of machine learning approaches to information extraction and user content monitoring, it is possible to generate more robust relation-specific data which can help train algorithms to better respond to exogenous events and information or local phenomena occurring on a site or in a building. It is also possible, according to the invention, to use similar data to repetitively, and systematically, improve user interaction mechanisms for providing feedback and controlling the decision support or automated actions which are completed on their behalf, [0245]).
Regarding claim 4, the method of claim 1, wherein the one or more projection factors are determined for and applied to each timestep of the multiyear forecasts for input data which is time dependent, Crabtree teaches, (A statistics server and an interface adapted to receive and send digital information from at least a client system, and further adapted to optionally communicate with the digital exchange [ ] where the server systems periodically optimize operational parameters used by a client system for a specific time period and a specific energy asset from client system based on forecasted conditions, Crabtree [0034]).
Claims 5 -7 are rejected under 35 U.S.C. § 103 as being taught by Crabtree (US 20100332373 A1) hereafter Crabtree, “System and Method for Participation in Energy-related Markets,” in view of Pecenak, (US 20200334609A1), hereafter Pecenak, “Adaptive, Multiyear Planning Method For Energy System, Microgrid, Or Distributed Energy Resource (DER), Involves Generating Recommended Operation Or Investment Decision For System For Specified Time Horizon Through Output Device” in view of Pecenak, “Efficient multi-year economic energy planning in microgrids,” hereafter Pecenak NPL, Applied Energy, Volume 255, 2019, 113771, ISN0306-2619, https://doi.org/10.1016/j.apenergy.2019.113771.
Regarding claim 5, the method of claim 1, wherein a single projection factor is determined for and applied across all time-steps of the multiyear forecasts, Crabtree does not teach, Pecenak NPL teaches, (Load data from the UC San Diego campus microgrid was selected, specifically the recreational facilities buildings. The selected load provides a good proxy for a real microgrid load, as it is open 24 h per day and follows the trajectory of typical load curve, where there is a peak in the evening (Fig. 3). The optimization began in the year 2010 and was carried out through 2030, by using the historical and forecasted data presented in Fig. 1a., [Pecenak NPL, p. 5, sec 4.1].
Crabtree and Pecenak are both considered analogous to the claimed invention because they are in the field of energy system planning production and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy system economic planning of Crabtree with forward-looking model of Pecenak to represent the lowest possible cost or highest benefit over the “Y” years, Pecenak, NPL [p.5, sec 3.3].
Regarding claim 6, The method of claim 1, wherein new investments resulting from a first iteration of the method are added to the input data in a following iteration of the method. Crabtree does not teach, Pecenak NPL teaches, (The Adaptive method does not change the problem formulation model. Instead, it solves a single year optimization repeatedly, where each solution represents a different year and is dependent on the prior simulation (year). The input parameters, such as demand, prices, and regulatory constraints, for each optimization are updated to match the forecasted value for the given year. Investment decisions made in previous years are considered fixed and carried into the current year optimization, where new investments can be made. When a piece of technology reaches its useful lifetime, the technology is discarded and the optimizer is able to invest freely to fill the void (or not invest at all if it makes economic sense), [Pecenak NPL- p.5, sec 3.4]).
Crabtree and Pecenak are both considered analogous to the claimed invention because they are in the field of energy system planning production and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy system economic planning of Crabtree with the adaptive method of multi-year optimization of Pecenak to return only additional investment and dispatch recommendations that will save cost with changing conditions and generate only a linear time increase, which is computationally desirable as optimization run-times grow nonlinearly with the number of timesteps (and subsequent increase in the number of possible solutions), Pecenak, NPL, [p.5, sec 3.4]].
Regarding claim 7, The method of claim 1, further comprising: determining, using an adaptive multiyear approach, accurate dispatch for each year of the multiyear horizon based on the investment, or an incremental dispatch by combining the adaptive multiyear approach with the machine learning model and the one or more projection factors. Crabtree teaches, (a wide variety of machine learning techniques may be employed in concert with a network-connected controls network that is capable of monitoring and controlling facilities through a variety of industrial automation systems and controls technologies that are well established in the art, [0276], and statistics server 1030 calculates a plurality of statistics based on data take from or derived from one or more of a configuration database 1022, a transaction database 1021, and an event database 1020, [0105], representing projection factors, but does not teach an adaptive method. Pecenak teaches, (This method is called Adaptive, since it only uses the single year model, applying current data, to make investment decisions while assuming nothing about the future at each timestep. As a result, data from the first years of the project are inherently weighted more heavily than future conditions, as investment decisions are made sequentially. Since decisions from previous years are considered fixed, the method returns only additional investment and dispatch recommendations that will save cost with changing conditions. Given a future forecast (assuming a perfect forecast, which does not exist), the method does not guarantee the lowest cost solution over the planning horizon. [Pecenak NPL, p.5 sec 3.4]).
Crabtree and Pecenak are both considered analogous to the claimed invention because they are in the field of energy system planning production and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the energy system economic planning and machine learning of Crabtree with the multi-year adaptive approach of Pecenak to deliver an improvement to the status quo of economic planning (i.e. optimization of the system based on a single year), Pecenak NPL, [p.7, sec 4.4].
Claims 8-14 are rejected for reasons corresponding to those provided for Claims 1-7. In these claims, the addition of at least one processor, memory storing instructions that when executed by the at least one processor, cause the at least one processor to perform operations and a machine learning model does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Crabtree teaches the system comprises at least one iNodes comprise at least a processor 241 such as a standard microprocessor or a customized processor (both very common in the art), and a network interface 240, which is connected to data network 201. [0087], a storage medium (not shown) coupled to
processor 311 [0090], and the system is characterized by the fact that a combination of machine learning (or any other artificial intelligence method) may be applied to observed data in conjunction with user-supplied information such that consumers' energy choices can be refined, automated, or both, [0228]).
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624