DETAILED ACTION
Status of the Claims
The following is a Final Office Action in response to amendments and remarks filed 29 October 2025.
Claims 1, 6-7, 12-13, and 18-19 have been amended.
Claims 1-20 are pending and have been examined.
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
Applicants argue that the 35 U.S.C. 101 rejection under the Alice Corp. vs. CLS Bank Int’l be withdrawn; however the Examiner respectfully disagrees. The Examiner notes that in order to be patent eligible under 35 U.S.C. 101, the claims must be directed towards a patent eligible concept, which, the instant claims are not directed. As an initial note, the Examiner again asserts that method claim 1 is devoid of structure whatsoever and thus cannot amount to anything more than an abstract idea. Next, and more specifically, (and contrary to Applicant’s arguments), the newly amended “receiving...” and “sending...” steps do not tie the claims to a particular machine, but are simply insignificant extrasolution data gathering and post solution output activities. Regarding the “calculating...” step, the Examiner notes that power distribution/optimization, load balancing, emissions optimization are all functions that have traditionally performed/provided for electrical systems such as power grids. Next, the claims are not directed to a practical application of the concept. The claims do not result in improvements to the functioning of a computer or to any other technology or technical field. They do not effect a particular treatment for a disease. They are not applied with or by a particular machine. They do not effect a transformation or reduction of a particular article to a different state or thing. And they are not applied in some other meaningful way beyond generally linking the use of the judicial exception (i.e., generating an energy emissions impact report) to a particular technological environment (i.e., with the use of generic computers or generic computing opponents). Here, again as noted in the previous rejection, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept - MPEP 2016.05(f). These arguments appear to be whether or not the use of computer or computing components for increased speed and efficiency makes the claims eligible; however the Examiner respectfully disagrees. Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)). A such, arguments are not persuasive, and the claim(s) is/are not patent eligible.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Applicant’s remarks with respect to the prior art have been fully considered but are moot on grounds of new rejections, necessitated by amendments.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). However, the claim(s) recite(s) generating an energy emission impact report based upon determined emission intensity, calculated potential emissions and determined reductions which is an abstract idea of a mental process as well as the abstract idea of performing computations in accordance with a mathematical formula on that data.
The limitations of “using the energy-related data, determining geographically localized energy emission intensities of the electrical power system; determining a desired reduction in energy emission for each of the plurality of distributed energy resources over a temporal period based on the dispatching parameter; generating an energy emission impact report, wherein the plurality of parameters comprises a maximum hours per dispatch, a maximum number of dispatches per day, a maximum number of days per week for dispatch, defined no-touch hours during which dispatching is prohibited, defined no-touch days during which dispatching is prohibited, and an hours per month goal for dispatching calculating, based on the geographically localized energy emission intensities and one or more parameters from a plurality of parameters a dispatching parameter for each of at least a subset of the plurality of distributed energy resources ” as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components (Step 2A Prong 1). That is, other than reciting “A non-transitory computer-readable medium storing a program for energy usage management for an electrical power system, which when executed by a computer, configures the computer to:,” in claim 7 or “A system for energy usage management for an electrical power system, comprising: a processor; and a non-transitory computer readable medium storing a set of instructions, which when executed by the processor, configure the processor to:” in claim 13 nothing in the claim element precludes the step from practically being performed in the mind. Method claim 1 is devoid of structure whatsoever and thus can only amount to an abstract idea. For example, but for the “configures the computer to:” “configure the processor to:” language, “determining” “calculating,” “determining, and “generating” in the context of this claim encompasses the user manually collecting, monitoring, and analyzing electrical power grid data which is a mental process similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, while some of the limitations may be based on mathematical concepts, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea (Step 2A, Prong One: YES).
This judicial exception is not integrated into a practical application (Step 2A Prong Two). The “receiving” and “sending” steps are simply insignificant extrasolution data gathering and post solution output activities. Method claim 1 is devoid of structure whatsoever and thus does not integrate the claims into a practical application. Next, claims 7 and 13 only recites one additional element – using a computer or processor to perform the steps. The computer and processor in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of collecting information, analyzing it, and displaying certain results of the collection and analysis) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 1 is devoid of structure whatsoever and thus does not amount to significantly more. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a computer or processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Reevaluating here in step 2B, the “receiving” and “sending” step(s) which are insignificant extrasolution activities are also determined to be well-understood, routine and conventional activity in the field. The Symantec, TLI, and OIP Techs court decisions in MPEP 2106.05(d)(II) indicate that the mere receipt or transmission of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic manner (as is here). Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-6, 8-12, and 14-19 recite(s) the additional limitation(s) further limiting the data (dispatch parameter, reports, geographical sites) which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 7, and 13, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 20 recite(s) the additional limitation(s) which include an interface for an insignificant post solution output, which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 7, and 13, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-20 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US PG Pub. 2023/0198258, hereinafter Shi’258) further in view of Shi et al. (US PG Pub. 2020/0372588, hereinafter Shi’588).
As per claims 1, 7, and 13, Shi discloses a method, non-transitory computer-readable medium storing a program for energy usage management for an electrical power system, which when executed by a computer, configures the computer to: and a system for energy usage management for an electrical power system, comprising: a processor; and a non-transitory computer readable medium storing a set of instructions, which when executed by the processor, configure the processor to: the method comprising (an exemplary embodiment of an apparatus 100 for optimizing carbon emissions in a power grid network is illustrated. Apparatus 100 includes a processor 104. Processor 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Processor 104 may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone, Shi’258 ¶20):
receiving energy-related data in real time from a plurality of distributed energy resources in the electrical power system (A grid monitoring device may alternatively or additionally include a device operated by an institution, such as without limitation the U.S. Energy Information Administration (EIA), that reports power output quantities and/or related data with regard to one or more grid networks and/or grids incorporating one or more grid networks. Such institutions and/or devices operated thereby may report power output quantities and/or related data at any rate described herein, including without limitation on an hourly basis; in an embodiment, where one local grid monitoring device, such as an ISO and/or RTO reports data infrequently or irregularly, an institution such as EIA may report more frequently and/or in real time or near real time. For instance, EIA may report data in substantially real time, but with a one-day lag in reporting. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various types of grid monitoring devices capable of producing at least a power datum as described in this disclosure, Shi’258 ¶25; power flow data reported, ¶27);
using the energy-related data, determining geographically localized energy emission intensities of the electrical power system (calculate carbon intensity, Shi’258 ¶32 and ¶34-¶35; Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs, Shi ¶36; In embodiments, grid network comprises a local power grid, ¶24) (Examiner notes that the carbon intensity for a power grid as including a geographically localized emission intensity).
determining a desired reduction in energy emission for each of the plurality of distributed energy resources over a temporal period based on the dispatching parameter (attempting to achieve a carbon offset, Shi’258 ¶50; In a nonlimiting example, processor 104 may be configured to generate a grid modification 156 for a set of power generators based on the minimization of the objective function of a carbon flow 140 as a function of the objective function 148. In another nonlimiting example, grid modification 156 may include optimal times of power consumption, such as during the day when solar panels are producing power. The grid modification is described in more detail in FIG. 2 and throughout this disclosure, ¶76; objective function, variables, ¶56-¶57) (Examiner interprets the minimization of the objective function as the ability to determine a desired reduction in energy emissions);
sending signals to each of the distributed energy resources to modify energy output during the temporal period, based on the corresponding desired reduction in energy emission (modifying grid parameter, Shi’258 ¶77; controls connected to grid network, ¶42); and
generating an energy emission impact report, wherein the plurality of parameters comprises a maximum hours per dispatch, a maximum number of dispatches per day, a maximum number of days per week for dispatch, defined no-touch hours during which dispatching is prohibited, defined no-touch days during which dispatching is prohibited, and an hours per month goal for dispatching (Results may be transmitted via a client interface, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface may provide a two-way communication interface with client devices, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API, Shi’258 ¶50; A “grid parameter,” as used herein, is a parameter related to power generator outputs from a plurality of power generators. In an embodiment, modifying grid parameter 160 may include identifying and selecting power generators with low carbon intensity. In some embodiments, modifying grid parameter 160 may include using Volt/Var control to identify excess voltage and reduce power losses in the grid. In an embodiment, modified grid parameter 160 may include using the energy storage carbon emission model. In a nonlimiting example, grid parameter may be modified based on the available excess electricity available from energy storages, such as during peak consumption times. In some embodiments, processor 104 may be configured to reduce power losses as a function of a Volt/Var control, described in more detail further above. Alternatively or additionally, grid parameter may be modified according to one or more changes at power stations and/or on power lines, including without limitation Volt/Var control, improvements to impedance matching, reductions in line losses, rerouting lines to reduce overall path lengths, or the like, ¶77; Referring to FIG. 5, an illustrative graphical representation 500 of an exemplary embodiment of an optimized carbon flow is presented. In this illustrative example, the optimized carbon flow is given a set of inputs 504, where the graphical representation shows the optimized path of power generation that reduces carbon footprint while retaining the optimizations of OPF model and the predicted outputs 508. In some embodiments, processor 104 may be configured to generated a graphical representation of optimized carbon flow, ¶89) (Examiner notes the grid parameters as including a maximum hours per dispatch, a maximum number of dispatches per day, a maximum number of days per week for dispatch, defined no-touch hours during which dispatching is prohibited, defined no-touch days during which dispatching is prohibited, and an hours per month goal for dispatching, as these are the different types of control parameters or options to be used in order to reduce carbon emission intensity and thus the adjustable variables within the objective function of Shi’258).
While Shi’258 does discloses the ability to control and allocate the power flow of a local power grid in order to optimize carbon emissions (¶42, ¶87, and ¶89 of Shi’258), however Shi’258 does not expressly disclose calculating, based on the geographically localized energy emission intensities and one or more parameters from a plurality of parameters a dispatching parameter for each of at least a subset of the plurality of distributed energy resources.
However, Shi’588 teaches calculating, based on the geographically localized energy emission intensities and one or more parameters from a plurality of parameters a dispatching parameter for each of at least a subset of the plurality of distributed energy resources (Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid, Shi ¶36; The outputs of optimization engine may include dispatches sent to control energy resources. Future input signals such as price signals, grid service signals, and grid carbon intensities may be input; for instance, each such signal may be generated using processes described above. A forecast model for a base load may be customer's load, which may be calculated, without limitation, by aggregating past consumption-based energy consumption and/or emissions, including without limitation according to any method described herein. Alternatively or additionally, a machine-learning process as described above may be trained using training entries 132 as described above, with training data and/or inputs correlating past consumer loads to other data such as weather data, time of day, other circumstantial things, day of week, market, holiday, and/or season data, and/or any other grid-related and/or extrinsic datum as described above. A load for user may be forecasted using a machine-learning process equipped to account for extensive volatility that may be expected in an individual, institutional-level, and/or building level load. Machine-learning process may include a deep-learning and/or deep neural net approach such as without limitation a long short-term memory (LSTM) recurrent neural network (RNN). LSTM, as used herein is a neural net learning algorithm that can learn both long-term and short-term temporal connections through a deep neural network. In an embodiment, computing device 104 may generate forecasts for a plurality of prediction horizons are sometimes; for example, a demand charge is based on a peak demand of each month, so monthly prediction may be necessary, ¶76).
Both the Shi’258 and Shi’588 references are analogous in that both are directed towards/concerned with energy use and reductions. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Shi’s ability to calculate a dispatch parameter in Shi’258’s system to improve the system and method with reasonable expectation that this would result in an energy management system that is able to reduce emissions.
The motivation being that minimal carbon emissions are an increasingly vital objective in industry and technology. However, lack of real-time visibility and transparence owing to existing static data collection and estimation techniques significantly limits ability to monitor and manage emissions efficiently. Therefore, existing solutions render it difficult to meet the increasing demands on corporate sustainability (Shi’588 ¶3).
As per claims 2, 8, and 14, Shi’258 and Shi’588 disclose as shown above with respect to claims 1, 7, and 13. Shi’258 further discloses wherein the energy emission is greenhouse gas or carbon emissions (calculate carbon intensity, Shi’258 ¶32 and ¶34-¶35; Real-time data streams may be as inputs continuously fed to models, such as real-time models 220 for production of current values such as current carbon intensity and/or cumulative past carbon tonnage and forecast models 224 used for predicting future carbon intensity, past or future carbon tonnage, past or future avoided carbon tonnage, and/or costs, Shi ¶36).
As per claims 3, 9, and 15, Shi’258 and Shi’588 disclose as shown above with respect to claims 1, 7, and 13. Shi’258 further discloses wherein calculating the dispatching parameter comprises determining time periods in which no energy is used (A “grid parameter,” as used herein, is a parameter related to power generator outputs from a plurality of power generators. In an embodiment, modifying grid parameter 160 may include identifying and selecting power generators with low carbon intensity. In some embodiments, modifying grid parameter 160 may include using Volt/Var control to identify excess voltage and reduce power losses in the grid. In an embodiment, modified grid parameter 160 may include using the energy storage carbon emission model. In a nonlimiting example, grid parameter may be modified based on the available excess electricity available from energy storages, such as during peak consumption times. In some embodiments, processor 104 may be configured to reduce power losses as a function of a Volt/Var control, described in more detail further above. Alternatively or additionally, grid parameter may be modified according to one or more changes at power stations and/or on power lines, including without limitation Volt/Var control, improvements to impedance matching, reductions in line losses, rerouting lines to reduce overall path lengths, or the like, ¶77; Referring to FIG. 5, an illustrative graphical representation 500 of an exemplary embodiment of an optimized carbon flow is presented. In this illustrative example, the optimized carbon flow is given a set of inputs 504, where the graphical representation shows the optimized path of power generation that reduces carbon footprint while retaining the optimizations of OPF model and the predicted outputs 508. In some embodiments, processor 104 may be configured to generated a graphical representation of optimized carbon flow, ¶89) (Examiner notes the grid parameters as including determining time periods in which no energy is used, as these are the different types of control parameters or options to be used in order to reduce carbon emission intensity and thus the adjustable variables within the objective function of Shi’258).
As per claims 4, 10, and 16, Shi’258 and Shi’588 disclose as shown above with respect to claims 1, 7, and 13. Shi’258 further teaches wherein generating an energy emission impact report comprises determining an offset parameter for designated emissions (offsets, Shi’258 ¶50 and ¶102).
As per claims 5, 11, and 17, Shi’258 and Shi’588 disclose as shown above with respect to claims 1, 7, and 13. Shi’258 further discloses changing the dispatching parameter based on seasonality (forecasting, Shi’258 ¶50-¶51; vary by season, ¶34 and ¶52).
As per claims 6, 12, and 18, Shi’258 and Shi’588 disclose as shown above with respect to claims 1, 7, and 13. Shi’258 further discloses determining a subset of the plurality of distributed energy resources with the highest energy emissions (prioritize eliminating high emission losses, Shi’258 ¶42).
As per claim 19, Shi’258 and Shi’588 disclose as shown above with respect to claim 1. Shi’258 further discloses performing an energy dispatching operation over the temporal period based on the desired reduction in energy emission for the plurality of distributed energy resources (Results may be transmitted via a client interface, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface may provide a two-way communication interface with client devices, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API, Shi’258 ¶50; A “grid parameter,” as used herein, is a parameter related to power generator outputs from a plurality of power generators. In an embodiment, modifying grid parameter 160 may include identifying and selecting power generators with low carbon intensity. In some embodiments, modifying grid parameter 160 may include using Volt/Var control to identify excess voltage and reduce power losses in the grid. In an embodiment, modified grid parameter 160 may include using the energy storage carbon emission model. In a nonlimiting example, grid parameter may be modified based on the available excess electricity available from energy storages, such as during peak consumption times. In some embodiments, processor 104 may be configured to reduce power losses as a function of a Volt/Var control, described in more detail further above. Alternatively or additionally, grid parameter may be modified according to one or more changes at power stations and/or on power lines, including without limitation Volt/Var control, improvements to impedance matching, reductions in line losses, rerouting lines to reduce overall path lengths, or the like, ¶77; Referring to FIG. 5, an illustrative graphical representation 500 of an exemplary embodiment of an optimized carbon flow is presented. In this illustrative example, the optimized carbon flow is given a set of inputs 504, where the graphical representation shows the optimized path of power generation that reduces carbon footprint while retaining the optimizations of OPF model and the predicted outputs 508. In some embodiments, processor 104 may be configured to generated a graphical representation of optimized carbon flow, ¶89).
As per claim 20, Shi’258 and Shi’588 disclose as shown above with respect to claim 19. Shi’258 further discloses wherein the energy dispatching operation is performed using a grid intensity application programming interface (Results may be transmitted via a client interface, which may perform one or more optimization, recommendation and/or forecasting outputs in textual and/or graphical form. Results may alternatively or additionally be communicated using an API, for instance as described in further detail below. Client interface may provide a two-way communication interface with client devices, including without limitation by means of graphical user interfaces, industry communications protocols such as Modbus, BACnet, IEC 61850, TCP/IP, other proprietary protocols, and/or an API, Shi’258 ¶50).
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 extension fee 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 ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629