DETAILED ACTION
The following is a Final Office Action in response to the Amendment received on 5 January 2026. Claims 1, 15 and 20 have been amended. Claims 1-20 remain pending in this application.
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 .
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.
Response to Arguments
Applicant's arguments filed 5 January 2026 have been fully considered but they are not persuasive.
In response to applicants’ argument that Wan et al. does not disclose “perform a second optimization with the multi-tiered model that predicts a second condition of the building based on a second control setting and the one or more first values of the first control setting while holding the one or more first values of the first control setting constant, the second optimization determining one or more second values of the second control setting;” Examiner disagrees. The Wan et al. reference discloses “predicting (at 302), based on a shading and lighting prediction model, a visual comfort condition and a lighting condition with respect to the region of the building; optimizing (at 304), based on a first multi-component cost function including a plurality of components relating to a plurality of lighting or thermal performance parameters with respect to the region of the building, one or more first control parameters for controlling the lighting system and the shading system based on the predicted visual comfort condition and the predicted lighting condition; predicting (at 306), based on a building dynamics model, a plurality of building response parameters based on the predicted visual comfort condition and the predicted lighting condition associated with the region of the building; optimizing (at 308), based on a second multi-component cost function including a plurality of components relating to the plurality of building performance parameters, one or more second control parameters for controlling the air-conditioning and/or heating system based on the predicted plurality of building response parameters.” See [0084] and figure 3.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-9, 15, 17, 18 and 20 is/are rejected under 35 U.S.C. 102(a)(1)/ 102(a)(2) as being anticipated by US Pub. No. 2022/80113688 A1 (USPN 12,066,798) to WAN et al.
As per claim 1, the WAN et al. reference discloses a building system comprising one or more memory devices (see [0100], “memory 402”) storing instructions (see [0101], “instructions (e.g., software modules)”) thereon that, when executed by one or more processors (“at least one processor 404”), cause the one or more processors (“at least one processor 404”) to: receive building data for a building (“building”) describing one or more conditions (see [0100], “a visual comfort condition and a lighting condition”) of the building (“building”); perform a first optimization (see [0101], “first optimization module (or a first optimization circuit) 408”) with a multi-tiered model (“a shading and lighting prediction model”) that predicts a first condition of the building (“building”) based on a first control setting (see [0101], “one or more first control parameters for controlling the lighting system and the shading system”), the first optimization (“first optimization module (or a first optimization circuit) 408”) determining one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”); perform a second optimization (“second optimization module (or a second optimization circuit) 412”) with the multi-tiered model (“building dynamics model”) that predicts a second condition (“plurality of building performance parameters”) of the building (“building”) based on a second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) and the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) while holding the one or more first values (see [0180], “convective heat transfer coefficient”) of the first control setting constant (“constant value”), the second optimization (“second optimization module (or a second optimization circuit) 412”) determining one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”); and operate building equipment (“the lighting system and the shading system, the air-conditioning and/or heating system”) based on the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) and the one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
As per claim 4, the WAN et al. reference discloses the first optimization (“first optimization module (or a first optimization circuit) 408”) optimizes the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) without consideration (see figure 6) of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
As per claim 5, the WAN et al. reference discloses the first condition (“a visual comfort condition and a lighting condition”) of the building (“building”) and the second condition (“building dynamics model”) of the building (“building”) are inversely proportional (see [0080], “proportional-integral-derivative (PID) controllers”).
As per claim 6, the WAN et al. reference discloses the first optimization (“first optimization module (or a first optimization circuit) 408”) is performed before, and separate from (see figure 4), the second optimization (“second optimization module (or a second optimization circuit) 412”) to prioritize the first condition (“one or more first control parameters for controlling the lighting system and the shading system”) over the second condition (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
As per claim 7, the WAN et al. reference discloses the first optimization (see [0081], “model predictive control (MPC)”) is a first closed-loop optimization (see [0226], “closed loop control”) and the second optimization (“model predictive control (MPC)”) is a second closed-loop optimization (“closed loop control”).
As per claim 8, the WAN et al. reference discloses the multi-tiered model (“a shading and lighting prediction model, building dynamics model”) comprises a plurality of models (see [0191], “hybrid model 900”) comprising a first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) and a second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”); wherein the first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) receives at least some of the building data (see [0202], “measured data of real buildings”) and the first control setting as first inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”) and predicts the first condition (“indoor light conditions, visual comfort, lighting power”) of the building based on the first inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”); wherein the first optimization (“first optimization module (or a first optimization circuit) 408”) determines the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) that result in optimal predictions of the first condition (“indoor light conditions, visual comfort, lighting power”) of the building by the first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”); wherein the second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) receives at least some of the building data, the first control setting, and the second control setting as second inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”) and predicts the second condition (“indoor light conditions, visual comfort, lighting power”) of the building based on the second inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”); wherein the second optimization (“second optimization module (or a second optimization circuit) 412”) determines the one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) that result in optimal predictions of the second condition (“indoor light conditions, visual comfort, lighting power”) of the building by the second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”).
As per claim 9, the WAN et al. reference discloses the multi-tiered model (“a shading and lighting prediction model, building dynamics model”) comprises a plurality of models (see [0191], “hybrid model 900”) comprising a first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) that predicts the first condition (“indoor light conditions, visual comfort, lighting power”) of the building (“building”) and a second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) that predicts the second condition (“indoor light conditions, visual comfort, lighting power”) of the building (“building”).
As per claim 15, the WAN et al. reference discloses a method comprising: receiving, by a processing circuit (see [0100], “at least one processor 404”), building data for a building (“building”) describing one or more conditions (see [0100], “a visual comfort condition and a lighting condition”) of the building (“building”); performing, by the processing circuit (“at least one processor 404”), a first optimization (see [0101], “first optimization module (or a first optimization circuit) 408”) with a multi-tiered model (“a shading and lighting prediction model”) that predicts a first condition of the building (“building”) based on a first control setting (see [0101], “one or more first control parameters for controlling the lighting system and the shading system”), the first optimization (“first optimization module (or a first optimization circuit) 408”) determining one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”); performing, by the processing circuit (“at least one processor 404”), a second optimization (“second optimization module (or a second optimization circuit) 412”) with the multi-tiered model (“building dynamics model”) that predicts a second condition (“plurality of building performance parameters”) of the building (“building”) based on a second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) and the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) while holding the one or more first values (see [0180], “convective heat transfer coefficient”) of the first control setting constant (“constant value”), the second optimization (“second optimization module (or a second optimization circuit) 412”) determining one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”); and operating, by the processing circuit (“at least one processor 404”), building equipment (“the lighting system and the shading system, the air-conditioning and/or heating system”) based on the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) and the one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
As per claim 17, the WAN et al. reference discloses the first optimization (“first optimization module (or a first optimization circuit) 408”) is performed before, and separate from (see figure 4), the second optimization (“second optimization module (or a second optimization circuit) 412”) to prioritize the first condition (“one or more first control parameters for controlling the lighting system and the shading system”) over the second condition (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
As per claim 18, the WAN et al. reference discloses the multi-tiered model (“a shading and lighting prediction model, building dynamics model”) comprises a plurality of models (see [0191], “hybrid model 900”) comprising a first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) and a second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”); wherein the first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) receives at least some of the building data (see [0202], “measured data of real buildings”) and the first control setting as first inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”) and predicts the first condition (“indoor light conditions, visual comfort, lighting power”) of the building based on the first inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”); wherein the first optimization (“first optimization module (or a first optimization circuit) 408”) determines the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) that result in optimal predictions of the first condition (“indoor light conditions, visual comfort, lighting power”) of the building by the first model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”); wherein the second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”) receives at least some of the building data, the first control setting, and the second control setting as second inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”) and predicts the second condition (“indoor light conditions, visual comfort, lighting power”) of the building based on the second inputs (“luminous efficacy, shading optimization, solar radiation, tint level of the EC window”); wherein the second optimization (“second optimization module (or a second optimization circuit) 412”) determines the one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) that result in optimal predictions of the second condition (“indoor light conditions, visual comfort, lighting power”) of the building by the second model (“a daylight penetration model (or sub-model) 904, a visual comfort model (or sub-model) 908 and a lighting power model (or sub-model) 912”).
As per claim 20, the WAN et al. reference discloses a building system comprising one or more memory devices (see [0100], “memory 402”) storing instructions (see [0101], “instructions (e.g., software modules)”) thereon that, when executed by one or more processors (“at least one processor 404”), cause the one or more processors (“at least one processor 404”) to: receive building data for a building (“building”) describing one or more conditions (see [0100], “a visual comfort condition and a lighting condition”) of the building (“building”); determine one or more first values of a first control setting (see [0101], “one or more first control parameters for controlling the lighting system and the shading system”) with a multi-tiered model (“a shading and lighting prediction model”) that predicts a first condition of the building (“building”) based on the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”); determine one or more second values of a second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) with the multi-tiered model (“building dynamics model”) that predicts a second condition (“plurality of building performance parameters”) of the building (“building”) based on the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”) and the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) while holding the one or more first values (see [0180], “convective heat transfer coefficient”) of the first control setting constant (“constant value”); and operate building equipment (“the lighting system and the shading system, the air-conditioning and/or heating system”) based on the one or more first values of the first control setting (“one or more first control parameters for controlling the lighting system and the shading system”) and the one or more second values of the second control setting (“one or more second control parameters for controlling the air-conditioning and/or heating system”).
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.
Claim(s) 2, 3, 12-14, 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2022/80113688 A1 (USPN 12,066,798) to WAN et al. in view of US Pub. No. 2014/0365017 A1 to Hanna et al.
As per claim 2, the WAN et al. reference does not expressly disclose the further limitations taught by the Hanna et al. reference, namely: the first condition (see [0026], “environmental conditions”) of the building (“building”) is indoor air quality (IAQ) (“indoor air quality (IAQ)”) of the building (“building”) and the second condition (see [0058], “energy consumption”) is energy consumption (“energy consumption”) of the building (“building”); wherein the first control setting (see [0068], “operating mode”) includes ventilation actions (“use ventilation equipment”) and the second control setting (see [0048], “user defined target temperature”) includes temperature setpoint actions (“user defined target temperature”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the building performance parameters taught by the WAN et al. reference to include the building performance parameters taught by the Hanna et al. reference.
One of ordinary skill in the art would have been motivated to modify the building performance parameters to include additional building performance parameters to improve building performance and/or occupant comfort.
As per claim 3, the WAN et al. reference does not expressly disclose the further limitations taught by the Hanna et al. reference, namely: the first condition (see [0026], “environmental conditions”) is indoor air quality (IAQ) (“indoor air quality (IAQ)”) of the building (“building”) and the second condition (see [0061], “energy metrics”) is carbon emissions (“greenhouse gas emissions”) associated with the building (“building”); wherein the first control setting (see [0068], “operating mode”) includes ventilation actions (“use ventilation equipment”) and the second control setting (see [0048], “user defined target temperature”) includes temperature setpoint actions (“user defined target temperature”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the building performance parameters taught by the WAN et al. reference to include the building performance parameters taught by the Hanna et al. reference.
One of ordinary skill in the art would have been motivated to modify the building performance parameters to include additional building performance parameters to improve building performance and/or occupant comfort.
As per claim 12, the WAN et al. reference does not expressly disclose the further limitations taught by the Hanna et al. reference, namely: the first condition (see [0026], “environmental conditions”) of the building (“building”) is indoor air quality (IAQ) (“indoor air quality (IAQ)”) of the building (“building”) and the second condition (see [0058], “energy consumption”) is energy consumption (“energy consumption”) of the building (“building”); wherein the multi-tiered model (see [0041], “predictive models”) includes: an occupancy model (“predictive models”) configured to predict occupancy of the building (“predicting future occupancy of the building”); an indoor air quality (IAQ) model (“predictive models”) configured to predict the IAQ (see [0074], “target indoor state”) of the building (“building”) based on the occupancy (“occupancy predictions”) of the building (“building”) predicted by the occupancy model (“predictive models”) and planned ventilations (“control signals for HVAC equipment”); and an energy model (“predictive models”) configured to predict the energy consumption (see [0048], “energy consumption”) of the building (“building”) based on the occupancy of the building (“building”) predicted by the occupancy model (“predictive models”) and planned ventilations (“control signals for HVAC equipment”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the building performance parameters taught by the WAN et al. reference to include the building performance parameters taught by the Hanna et al. reference.
One of ordinary skill in the art would have been motivated to modify the building performance parameters to include additional building performance parameters to improve building performance and/or occupant comfort.
As per claim 13, the Hanna et al. reference discloses the occupancy model (see [0054], “occupancy prediction model”) receives at least one of a time of day (“time of day”), a day of week (“day of week”), a holiday schedule, or a meeting schedule and predicts the occupancy (“predict future occupancy level”) of the building (“building”) based on at least one of the time of day (“time of day”), the day of week (“day of week”), the holiday schedule, or the meeting schedule.
As per claim 14, the Hanna et al. reference discloses the energy model (see [0072], “prediction models”) is configured to predict the energy consumption (“energy consumption”) of the building (“building”) based on the occupancy (“occupancy information”) of the building (“building”) predicted by the occupancy model (“predictive models”), the planned ventilations (“HVAC equipment”), outdoor conditions (“outdoor sensors and/or weather service information”) of the building (“building”), and planned setpoint actions (“user settings”) of the building (“building”).
As per claim 16, the WAN et al. reference does not expressly disclose the further limitations taught by the Hanna et al. reference, namely: the first condition (see [0026], “environmental conditions”) of the building (“building”) is indoor air quality (IAQ) (“indoor air quality (IAQ)”) of the building (“building”) and the second condition (see [0058], “energy consumption”) is energy consumption (“energy consumption”) of the building (“building”); wherein the first control setting (see [0068], “operating mode”) includes ventilation actions (“use ventilation equipment”) and the second control setting (see [0048], “user defined target temperature”) includes temperature setpoint actions (“user defined target temperature”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the building performance parameters taught by the WAN et al. reference to include the building performance parameters taught by the Hanna et al. reference.
One of ordinary skill in the art would have been motivated to modify the building performance parameters to include additional building performance parameters to improve building performance and/or occupant comfort.
As per claim 19, the WAN et al. reference does not expressly disclose the further limitations taught by the Hanna et al. reference, namely: the first condition (see [0026], “environmental conditions”) of the building (“building”) is indoor air quality (IAQ) (“indoor air quality (IAQ)”) of the building (“building”) and the second condition (see [0058], “energy consumption”) is energy consumption (“energy consumption”) of the building (“building”); wherein the multi-tiered model (see [0041], “predictive models”) includes: an occupancy model (“predictive models”) configured to predict occupancy of the building (“predicting future occupancy of the building”); an indoor air quality (IAQ) model (“predictive models”) configured to predict the IAQ (see [0074], “target indoor state”) of the building (“building”) based on the occupancy (“occupancy predictions”) of the building (“building”) predicted by the occupancy model (“predictive models”) and planned ventilations (“control signals for HVAC equipment”); and an energy model (“predictive models”) configured to predict the energy consumption (see [0048], “energy consumption”) of the building (“building”) based on the occupancy of the building (“building”) predicted by the occupancy model (“predictive models”) and planned ventilations (“control signals for HVAC equipment”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the building performance parameters taught by the WAN et al. reference to include the building performance parameters taught by the Hanna et al. reference.
One of ordinary skill in the art would have been motivated to modify the building performance parameters to include additional building performance parameters to improve building performance and/or occupant comfort.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2022/80113688 A1 (USPN 12,066,798) to WAN et al. in view of US Pub. No. 2020/0356857 A1 to Lee et al.
As per claim 10, the WAN et al. reference does not expressly disclose the further limitations taught by the Lee et al. reference, namely: the first model (see [0095], “predictive model(s) 604”) and the second model (“predictive model(s) 604”) are sequence to sequence neural networks (“deep learning model”) configured to receive a sequence of data inputs (“large dataset 602”) and predict a sequence of data outputs (“predictive model for the second building 610”) based on the sequence of data inputs (“large dataset 602”), wherein the sequence of data inputs (“large dataset 602”) are the building data (“first building”) and the sequence of data outputs (“predictive model for the second building 610”) are one of the first control setting or the second control setting (see [0110], “temperature setpoints”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the predictive models taught by the WAN et al. reference with the deep learning model taught by the Lee et al. reference.
One of ordinary skill in the art would have been motivated to modify the predictive models with the deep learning model to transfer learning of deep neural networks for building dynamics.
As per claim 11, the Lee et al. reference discloses the sequence to sequence neural networks (see [0101], “deep learning model”) are long-short term memory (LSTM) sequence to sequence neural networks (“Long Short Term Memory Network based Sequence to Sequence (LSTM S2S) model”).
Conclusion
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.
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/CRYSTAL J BARNES-BULLOCK/Primary Examiner, Art Unit 2117 21 March 2026