DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is responsive to communication filed on 7/28/2026.
Claims 1-8 are pending and presented for examination.
Response to Arguments/Remarks
Regarding the Title of the Invention
Applicant Argues
The title has been amended in order to better reflect the subject matter of the claimed invention.
Examiner Responds
Applicant’s response has been considered and is persuasive; the objection to the title set forth in the previous Office action is withdrawn.
Regarding rejections under 35 U.S.C. §112(b)
Applicant Argues
The claims have been amended to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Examiner Responds
Applicant’s response has been considered and is persuasive in part with respect to certain issues raised under §112(b). However, the claims continue to lack clarity in at least one respect, as outlined below. Therefore, the rejection under §112(b) is maintained to that extent.
Regarding rejections under 35 U.S.C. §102
Applicant Argues
Amended independent claims are not anticipated by Carty. Carty does not explicitly disclose Applicant’s simulation result which is directed to the energy consumption of a specific equipment or indoor environment of each room.
Examiner Responds
Applicant’s arguments with respect to independent claims (1, 7 and 8) have been fully considered and are persuasive in part. The examiner agrees that Carty does not anticipate the claims as amended. However, the examiner disagrees that Carty does not disclose Applicant’s simulation result. Accordingly, an updated prior art mapping of the claims, including the amended limitations, is provided in the rejection under §103 below.
Claim Objections
Claim 5 IS objected to because of the following informalities:
Claim 5 recites “acquires a preset setting”, however the examiner believes this is a typo and should recite “acquires a present setting” in light of the following limitation which recites selecting “on a basis of the present setting.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites to obtain “for each of a plurality of candidate settings, a simulation result on a basis of a plurality of candidate settings”, however it is unclear whether the two recitations of “a plurality of candidate settings” refer to the same plurality of candidate settings or to two different plurality of candidate settings. The claim contains subsequent recitations of “the candidate setting”, “for each candidate setting”, and “the plurality of candidate settings”, however as these recitations could refer to either plurality of candidate settings, their scope cannot be determined. For the purpose of examination, the two pluralities are interpreted as referring to the same plurality.
Dependent claims are likewise rejected.
Claims 7 and 8 are rejected as per claim 1.
Claim Rejections - 35 USC § 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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over CARTY1 in view of JANG (US20220268479A1) in view of YAN (US20210080915A1) (combination hereinafter – “CARTY-JANG-YAN”).
Regarding claim 1, CARTY teaches an equipment control device comprising processing circuity [[Abstract] method and system for optimizing building energy usage and generating real-time control instructions; [0082] implemented via hardware], wherein the processing circuity:
obtains, for each of a plurality of candidate settings, a simulation result on a basis of a plurality of candidate settings which are a plurality of candidates for a setting to control one or more units of equipment installed in a building, the simulation result corresponding to a result of an operation of the building of a case where the candidate setting is applied to the one or more units of equipment, and indicating an energy consumption of the one or more units of equipment or an indoor environment of each room of the building [Fig. 9, #906 simulate building systems [0066] “integrated energy management strategy may include recommendations for the operation of target systems including set-points” [0070-0071] recommendations are generated based on simulated scenarios “simulating the building systems may comprise varying specific parameters based on the type of simulation suggested and utilizing the demand forecasts to make predictions regarding the outcomes of such changes in variables”; Fig.6 & [0052] “Steps 610 614 illustrate a method for generating demand models for heating and cooling subsystems. The method 600 first receives the subsystem demand models from 608, then calculates a thermal model and electrical load model for each subsystem relevant to the overall energy demand model”];
ranks the plurality of candidate settings comparing simulation outcomes implies ranking, e.g., comparing three outcomes against each other will produce results of most desirable, somewhat desirable, and least desirable #910 “Generate Recommendations Based on Comparison” based on the simulation outcome ranking, generate set-point recommendations],
selects a candidate setting to be applied to the one or more units of equipment as an applicable setting from among the plurality of new candidate settings, and applies the applicable settings to the one or more units of equipment [[0007] “energy management solutions […] to enable real-time recommendations or control actions” Fig. 2, #222 interface to building control systems, [0037] “analytical engine 200 may take a plurality of actions. In one embodiment, the analytical engine 200 may generate control instructions that may automatically adjust equipment settings and parameters […] the analytical engine 200 may utilize both automatic generation of control instructions and recommendations as determined by the building owner].
In summary, CARTY teaches simulating building systems with varying equipment parameters/settings such that simulation outcomes can be compared. The inputs to the simulation are the candidate settings/varying equipment parameters and the outputs of the simulation correspond to the thermal state of the building and the energy cost. Based on the comparison, new set-point recommendations are generated that may automatically adjust equipment settings and parameters via the building control systems interface. CARTY is not relied on rank the plurality of candidate settings on a basis of a satisfaction level of each candidate setting and the constrain level of each candidate setting.
However, JANG in an analogous art teaches to control an HVAC unit with optimal settings “based on the objective function obtained using the thermal dissatisfaction calculated by the dissatisfaction calculator 400 and the operation cost calculated by the operation cost calculator 500” [0102]. JANG additionally teaches “objective function may be calculated based on the thermal dissatisfaction calculated by the dissatisfaction calculator 400 and the operation cost calculated by the operation cost calculator 500 by applying respective weights to them. The predictive controller 300 may control the HVAC unit 200 based on the objective function calculated from the thermal dissatisfaction and the operation cost for the building temperature control system 10. For example, the predictive controller 300 may control the HVAC unit 200 such that the objective function is minimized” [0051], implying that the dissatisfaction corresponds to a first index value for a demand of a stakeholder (such as an occupant, see [0079]) and the operation cost corresponds to a second index value expressing a degree of how much an operation constraint (such as power consumption, see [0080]) (additional support for teachings found in [0066-0108]. YAN in an analogous art teaches to rank setting configuration information on the basis of thermal comfort and the associated energy usage at the setting configuration [Fig. 12, [0125]].
CARTY, JANG, and YAN are each analogous art to the claimed invention because they are from the same field of predictive modeling of building systems. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of JANG and YAN to CARTY according to known methods such that CARTY’s comparison of simulation outcomes [Fig. 9, #908] calculates, for each simulated scenario, a thermal dissatisfaction corresponding to a first index value for a demand of a stakeholder in the building, and an operation cost corresponding to a second index value expressing a degree of how much an operation constraint of the building is satisfied, as taught by JANG [0051, 0079-0080]. The comparison then ranks the simulated scenarios on the basis of both values, as taught by YAN [Fig. 12]. CARTY’s set-point recommendations [Fig. 9, #910] are then generated on the basis of the ranking results for the purposes of implementing control actions [0037]. This modification combines prior art elements according to known methods to yield predictable results, where each element performs the same function in combination as it does separately. CARTY already compares simulated scenarios to generate set-point recommendations, JANG provides the known metrics of comfort and cost, and YAN provides the known technique of scoring based on the metrics of comfort and cost to produce rankings. One of ordinary skill in the art would have been motivated to make such a modification for the purpose of generating equipment settings that reduce energy cost while maintaining occupant comfort [CARTY [0007], JANG [0051]] with a reasonable expectation of success because CARTY’s simulation already outputs the thermal and energy data required by JANG and YAN.
Regarding claims 7 and 8, CARTY teaches an equipment control method [[Abstract] “method and system for optimizing building energy usage”], and a non-transitory computer readable medium recorded with an equipment control program [[0083] computer software]. The remaining limitations of claims 7 and 8 are substantially the same as claim 1 and are rejected as per claim 1.
Regarding claim 2, CARTY-JANG-YAN teaches the elements of claim 1 as outlined above. CARTY also teaches or at least suggest to calculate, for each candidate setting, a plurality of satisfaction levels about a plurality of indices, as the satisfaction level of the stakeholder [[0044] “energy management strategies 410 may comprise various strategies that the building manager or owner may wish to employ when optimizing the models […] building management may specific various occupant comfort constraints such as temperature, humidity, and ventilation requirements”], and
integrates, for each candidate setting, two or more satisfaction levels about two or more particular indices out of the plurality of satisfaction levels included in the satisfaction level of the stakeholder, into one satisfaction level ([0064]: “optimization of the modeled demand forecasts may be performed based on a user defined prioritization of optimization criteria […] to find a solution space/set that satisfies all criteria simultaneously by weighting each optimization criterion according to user-defined prioritization”).
Regarding claim 3, CARTY-JANG-YAN teaches the elements of claim 1 as outlined above. CARTY also teaches or at least suggests to simulate the operation of the building on a basis of each of a plurality of virtual settings for the one or more units of equipment, thereby obtaining a simulation result for each virtual setting [Fig. 9, #906 [0071] simulations performed on building systems with varying parameters];
learns, for each virtual setting, a set of the virtual setting and the simulation result, thereby generating a learned model [[0045] optimizer may use various optimization techniques including artificial neural networks (ANN), [0052] ANN is used for generating models for heating and cooling subsystems]; and
obtains, for each candidate setting, the simulation result of the candidate setting with using the learned model [Fig. 9 #908 implies obtaining such that they can be compared].
Regarding claim 4, CARTY-JANG-YAN teaches the elements of claim 1 as outlined above. CARTY also teaches or at least suggests to acquire a past setting applied to the one more of equipment, and an operation result of the building obtained when the past setting is applied, compares a simulation result under the past setting with the operation result, and corrects a simulation parameter on a basis of a comparison result [[0034]: “to generate demand models forecasting and estimation engine 214 receives data from real-time external data storage 204, historical data 206, on-site energy resources storage 208, real-time energy availability storage 210, and client energy approaches 212. In addition to data from storage modules 202-212, forecasting and estimation engine 214 may additionally receive feedback from the optimization engine 216 in order to refine the generated demand models further”, [0032]: “historical data storage 206 may contain various historical data regarding the building or campus including, but not limited to building zone conditions (e.g., temperature, humidity, CO.sub.2), occupancy history, HVAC conditions (e.g., temperature, humidity, air flow), weather conditions (e.g., solar radiation, temperature, humidity, wind speed) and energy rates)”, i.e., past settings and sensor data are saved and used in forecasting and estimation engine such that outputs can be used as inputs to optimization engine; Fig. 9 #908 compare simulation outcomes].
Regarding claim 5, CARTY-JANG-YAN teaches the elements of claim 1 as outlined above. YAN also teaches or at least suggests to acquire a present setting being applied to the one or more units of equipment, and selects the applicable setting on a basis of the present setting [[0082] “similar zone matcher 243 may be configured to utilize the output of the zone occupancy predictor 242 to search for one or more similar zones from other pre-configured (e.g. optimized) or un-configured buildings based on the historical BMS data 270, and determine the zone air configurations 271 and/or equipment configurations 272 (e.g.: AHU-VAV) based on the matched zones”].
Regarding claim 6, CARTY-JANG-YAN teaches the elements of claim 1 as outlined above. CARTY also teaches or at least suggests to optimize the plurality of candidate settings with using each of a plurality of optimization algorithms, thereby generating a plurality of candidate setting groups each formed of a plurality of new candidate settings [FIG. 8A, #808 [0064]: “method 800a optimizes the received, modeled demand forecasts based on the previously described constraints and priorities. In the illustrated embodiment, the method 800a may use various optimization techniques including, but limited to, non-linear programming techniques including genetic algorithms, simulated annealing, artificial neural networks, or other techniques or linear approximation techniques including Tailor series expansions or artificial neural network”], and
selects one out of the plurality of candidate setting groups, and selects the applicable setting from among the plurality of new candidate settings that constitute a plurality of selected candidate setting groups [FIG. 8B, #820 and #824, i.e., post optimization routine, select and apply equipment settings].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Franco, A., et al., (“Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings”, published 5/14/2021, retrieved on 9/30/2026, retrieved from https://www.mdpi.com/1996-1073/14/10/2847) discloses defining, balancing, and linking the elements for quantification of energy consumption of a building and of a comfort index of an occupant.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael V Farina whose telephone number is (571)272-4982. The examiner can normally be reached Mon-Thu 8:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.V.F./Examiner, Art Unit 2115
/KAMINI S SHAH/ Supervisory Patent Examiner, Art Unit 2115
1 CARTY is a prior art reference cited in the previous office action.
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