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 .
Amendments
This action is in response to amendments filed November 11th, 2025, in which Claims 1, 8, 12, & 15 have been amended. No claims have been added and claims 6 & 13 have been cancelled. The amendments have been entered, and Claims 1-5, 7-12, & 14-15 are currently pending.
Response to Arguments
Regarding the applicant’s traversal of the 35 U.S.C. 101 rejections of the previous office action, the applicant’s arguments filed November 11th, 2025 have been fully considered, and are persuasive.
Applicant asserts that the claims are not directed to an abstract idea. The examiner respectfully submits, as previously cited in the previous office actions in regard to claim 1, “define at least first two order moments of the probabilistic distribution” recites an abstract idea because a person can mentally evaluate a distribution and make a judgement to determine the first two order moments, and further, “selecting a combination of control parameters… having the largest likelihood of being optimal at the probabilistic distribution of the performance function according to an acquisition function of the first two order moments of the probabilistic distribution” recites an abstract idea because a person can mentally evaluate the various combinations of parameters and make a judgement to determine which is most likely to be optimal based on the first order moments of a mathematical function, and further, “modify the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the corresponding cost of operation of the system at the current state” recites an abstract idea because a person can mentally evaluate the probabilistic distribution of a function on a selected combination of values to make a judgement to modify that distribution.
Further, however, applicant asserts that the claims recite an improvement in computer-implemented calibration and control of industrial systems through probabilistic meta-learning and digital twin simulation, citing [0072] and [0057-0059] of the specification as support, and further stating that the system improves the cost of operation of an industrial system by reducing the computational and operational resources needed to perform calibration, citing [0038-0040] of the specification as support.
Examiner respectfully submits that the cited portions of the specification indeed discuss specific techniques such as achieving results with fewer model simulations by leveraging information learned from prior calibration tasks, thus resulting in results with minimal sampling, resulting in an approach that directly improves the efficiency and cost of the operation while maintaining accuracy, which accordingly recites more than a general link to computer-controlled calibration and control of industrial systems, but actually an improvement to the field as well, which is found to be reflected in the claim as a whole. Therefore, the 35 U.S.C. 101 rejections have been subsequently withdrawn.
Regarding the applicant’s traversal of the 35 U.S.C. 103 rejections of the previous office action, the applicant’s arguments filed November 11th, 2025 have been fully considered, and are unpersuasive.
Applicant asserts that the cited references fail to teach “a digital twin of a building system having different model parameters, and wherein the controller is further configured to use a meta-learning algorithm to calibrate the digital twin to find optimal model parameters using Bayesian optimization by warm-starting the performance function.”
However, examiner respectfully asserts that KUMAR does teach “wherein the system is a digital twin of a building system having different model parameters, and wherein the controller is further configured… to calibrate the digital twin to find optimal model parameters…” :
([0040, lines 31-33 and line 1 of the following page] “The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating creation of a digital twin of a process unit which can perform constrained optimization of control parameters to minimize or maximize an objective function.”)
Further, KUMAR teaches “to use a meta-learning algorithm” to do the calibration to find the optimal parameters:
([0051, lines 4-7] “Thus, the system (110) is configured to create an end-to-end differentiable digital twin model that includes the causality learning engine (214) and the ML engine (216) of the industrial plant (120).”)
And further, that KOEHRSON, does teach “…using Bayesian optimization”:
([paragraph 1] “Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency:
Manual
Grid search
Random search
Bayesian model-based optimization”
It can be seen here that Bayesian optimization of hyperparameters is one of the most common methods, and that it is more efficient than random search, as HUANG used within its performance function.)
And that AWS does teach “…by warm-starting the performance function”:
([paragraph 1, sentence 1-3] “Use warm start to start a hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. Hyperparameter tuning uses either Bayesian or random search to choose combinations of hyperparameter values from ranges that you specify.”).
Therefore, the cited prior art does teach the limitations as claimed, and in the rejections below, the rationale to combine the references is also provided. Therefore, the 35 U.S.C. 103 rejections are maintained.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 8-10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, W. et al. “Using genetic algorithms to optimize controller parameters for HVAC systems.” Available in 1997 (hereafter, HUANG), and further in view of Kumar, Shailesh, et al. WIPO Publication No. WO 2023/073655 A1 (hereafter, KUMAR), and Morales, Juan M. et al. “Point Estimate Schemes to Solve the Probabilistic Power Flow.” Available on November 2007 (hereafter, MORALES), and further in view of Koehrsen, Will. “A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning” Available on June 24 2018 (hereafter, KOEHRSON) and further in view of Amazon Web Services’ Documentation. “Run a Warm Start Hyperparameter Tuning Job” Available on September 17 2019 (hereafter, AWS).
Regarding claim 1, HUANG teaches “A controller for optimizing a controlled operation of a system performing a task”:
([Abstract, sentence 1] “This paper presents an adaptive learning algorithm based on genetic algorithms (GA) for automatic tuning of proportional, integral and derivative (PID) controllers in Heating Ventilating and Air Conditioning (HVAC) systems to achieve optimal performance.”)
Further, HUANG teaches “access, before beginning the controlled operation, a probabilistic distribution of a performance function trained to provide a relationship between different combinations of control parameters for controlling the system”:
([3. Optimization, paragraph 1] “In this study, a GA program based on Goldberg's "Simple Genetic Algorithm" [8] was developed in order to provide a genetics-based method for obtaining the optimal parameter values of a PI controller in an HVAC system. The derivative term was not included in the investigation since the derivative action has little effects on a thermal system due to the large time constant present in most cases [4]. The P (population) and I (individual) values are encoded into binary numbers and concatenated to form a binary string which represents an individual in the population (combinations of control parameters). The roulette wheel selection method was chosen for implementation, in which a probability Pi is assigned to each individual li such that
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-> A performance function
Thus, before the operation is controlled, probabilistic distributions of a performance function in comparison to various combinations of parameters are gathered.
Further, HUANG teaches “select a combination of control parameters from the different combinations of control parameters, such that the selected combination of control parameters is having the largest likelihood of being optimal at the probabilistic distribution of the performance function…”:
([3. Optimization, paragraph 2-3 “A parent is then randomly selected based on this probability. The two-point crossover method is chosen for use in the development of the GA program. This operator is similar to one-point crossover except that two points were picked randomly in the parent strings and the portion between the two points of parent 1 is replaced by the counterpart of parent 2.
A bit-by-bit mutation method is used in the GA program. A variation of the generational replacement method, called elitism, is adopted, which can preserve the best individual in the previous generation while replacing the worst individual in the current generation. Therefore, the best individual in all generations can be found.”) Here, a process is described which leads to the system selecting the most optimal combination of the parameters based on the output of the function (acquisition function) seen before which uses the probabilistic distribution to calculate the best option.
Further, HUANG teaches “control the system using the selected combination of the control parameters, thereby changing a current state of the system resulting in a corresponding cost of operation; and modify the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the… system at the current state”:
([5. Conclusions, lines 9-16] “Using the genetic algorithm tuning program, the optimal controller parameter values for the HVAC system were determined successfully, with which the system yielded a satisfactory performance. From the output curve, we can see that the overshoot is equal to 0.38 I°C and the settling time 14.5 s, while using Ziegler-Nichols method, the overshoot and the settling time are equal to 0.922°C and 358 s respectively.”) Here, we see the system being controlled using the selected combination of parameters, and the current state of the system changing from it.
HUANG fails to explicitly teach “corresponding costs of operation of the system”, “comprising: at least one processor; and a memory having instructions stored thereon that, when executed by the processor, cause the controller to:”, “…learned from training data collected from different systems performing tasks as the task of the system under control…”, “wherein the probabilistic distribution is… to define at least first two order moments of the probabilistic distribution”, “…according to an acquisition function of the first two order moments of the probabilistic distribution”, “wherein the system is a digital twin of a building system having different model parameters, and wherein the controller is further configured… to calibrate the digital twin to find optimal model parameters…”, “…using Bayesian optimization” and “…by warm-starting the performance function.” .
However, analogous art of a system and method for optimizing constraints of an industrial unit, KUMAR, does teach “comprising: at least one processor; and a memory having instructions stored thereon that, when executed by the processor, cause the controller to:”:
([0014, lines 24-27] “the present disclosure provides for a system for facilitating constrained optimization on non-linear attributes to find the optimal control-parameters for an industrial plant (The “system”, in this case, is the controller and the “industrial plant” equivalates to the claimed “system”). The system may include one or more processors coupled with a memory that stores instructions which when executed by the one or more processors causes the system to: …”)
Further, KUMAR teaches “…learned from training data collected from different systems performing tasks as the task of the system under control…”:
([0024, lines 5-14] “receive a set of input signals from one or more systems associated with an industrial plant; extract a first set of attributes from the set of input signals received, the first set of attributes pertaining to one or more finite constant parameters associated with the one or more systems. The UE may further extract a second set of attributes from the set of input signals received, the second set of attributes pertaining to one or more control parameters associated with the one or more systems. A causality learning engine associated with the processor may be configured to train the set of inputs received based on the first and the second set of attributes and a predefined dataset obtained from a knowledgebase associated with a centralized server operatively coupled to the industrial plant.” Here, it is shown that KUMAR discloses signals of attributes and parameters being received from a plurality of connected systems that are used to train the learning engine to learn from data associated with multiple systems.
Further, KUMAR teaches “wherein the system is a digital twin of a building system having different model parameters, and wherein the controller is further configured… to calibrate the digital twin to find optimal model parameters…”:
([0040, lines 31-33 and line 1 of the following page] “The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating creation of a digital twin of a process unit which can perform constrained optimization of control parameters to minimize or maximize an objective function.”)
Further, KUMAR teaches “to use a meta-learning algorithm” to do the calibration to find the optimal parameters:
([0051, lines 4-7] “Thus, the system (110) is configured to create an end-to-end differentiable digital twin model that includes the causality learning engine (214) and the ML engine (216) of the industrial plant (120).”)
It would be obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the base reference of HUANG with the teachings of KUMAR because KUMAR also teaches of methods for optimizing devices that control industrial systems, like and is analogous to the disclosures of HUANG.
One of ordinary skill in the art would be motivated to do so because HUANG teaches algorithms and methods for controlling a system while KUMAR teaches the actual physical controller which controls the system. Without the controller, control methods may not be implemented. In addition, doing so allows for more control and monitoring of the entire system and interconnected systems, potentially increasing optimization, and reducing costs for the entire system and gaining valuable insights from runtime for further ease of use.
Further, HUANG in view of KUMAR still fails to explicitly teach “corresponding costs of operation of the system”, “wherein the probabilistic distribution is… to define at least first two order moments of the probabilistic distribution”, “…according to an acquisition function of the first two order moments of the probabilistic distribution”, “…using Bayesian optimization” and “…by warm-starting the performance function.”. However, analogous art explaining point-estimate schemes for solving probabilistic power flow problems, MORALES does teach these limitations:
([Introduction, lines 4-7] “Computational methods which tackle uncertainty allow engineers to propose solutions less sensitive to environmental influences, while achieving simultaneously cost reduction or reliability improvement.”) and ([Section I. paragraphs 7-10] “In this paper, the point estimate method approach is used to solve the probabilistic power flow problem. The main advantages follow.
As Monte Carlo simulation, point estimate methods use deterministic routines for solving probabilistic problems; however, they require a much lower computational burden.
Furthermore, point estimate methods overcome the difficulties associated with the lack of perfect knowledge of the probability functions of stochastic variables, since these functions are approximated using only their first few statistical moments (i.e., mean, variance, skewness, and kurtosis). Therefore, a smaller level of data information is needed.”)
It would be obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the base reference of HUANG & KUMAR with the teachings of MORALES because it teaches that the probability of various functions can be estimated using only the first moments in a probabilistic distribution and is analogous to HUANG in the sense that both references disclose methods for improving optimization of control parameters in relation to controlling industrial systems.
One of ordinary skill in the art would be motivated to do so, because as MORALES teaches in paragraph 8 of Section I, these methods “require a much lower computational burden” and “overcome the difficulties associated with the lack of perfect knowledge of the probability functions of stochastic variables, since these functions are approximated using only their first few statistical moments (i.e., mean, variance, skewness, and kurtosis). Therefore, a smaller level of data information is needed, solutions become less sensitive to environmental influences, cost is reduced, and reliability improved.”
HUANG in view of KUMAR, and MORALES fails to explicitly teach “…using Bayesian optimization” and “…by warm-starting the performance function.” However, analogous art explaining the benefits of Bayesian parameter optimization for machine learning, KOEHRSON, does teach “…using Bayesian optimization”:
([paragraph 1] “Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency:
Manual
Grid search
Random search
Bayesian model-based optimization”
It can be seen here that Bayesian optimization of hyperparameters is one of the most common methods, and that it is more efficient than random search, as HUANG used within its performance function.)
It would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to combine the base reference of HUANG in view of KUMAR, & MORALES, with KOEHRSON because machine learning algorithms and methods for optimization are used in the previously cited prior art, and KOEHRSON teaches and explains optimized machine learning methods.
One of ordinary skill in the art would be motivated to do so because KOEHRSON teaches that using Bayesian parameter optimization as opposed to methods such as random search is more efficient.
HUANG in view of KUMAR, MORALES, & KOEHRSON still fails to explicitly teach “by warm-starting the performance function.” However, analogous art of AWS documentation explaining the benefits of warm-starting, AWS, does teach this:
([paragraph 1, sentence 1-3] “Use warm start to start a hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. Hyperparameter tuning uses either Bayesian or random search to choose combinations of hyperparameter values from ranges that you specify.”).
It would be obvious to one of ordinary skill in the art to combine the base reference of HUANG in view of KUMAR, MORALES, & KOEHRSON, with the teachings of AWS because AWS teaches optimal ways of adjusting parameters, which HUANG is designed to control.
One of ordinary skill in the art would be motivated to do so because, as AWS points out in paragraph 1, sentence 5, “Using information from previous hyperparameter tuning jobs can help increase the performance of the new hyperparameter tuning job by making the search for the best combination of hyperparameters more efficient.”
Regarding claim 2, HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS teaches the limitations of claim 1. Further, KOEHRSON teaches “wherein the probabilistic distribution of the performance function is learned and updated using a meta Bayesian optimization”.:
([paragraph 1] “Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency:
Manual
Grid search
Random search
Bayesian model-based optimization”
It can be seen here that Bayesian optimization of hyperparameters is one of the most common methods, and that it is more efficient than random search, as HUANG used within its performance function.)
Regarding claim 3, HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS teaches the limitations of claim 1. Further, HUANG teaches “wherein the probabilistic distribution is updated until a termination condition is met, such that upon reaching the termination condition, the controller is configured to: select… between different combinations of control parameters for controlling the system…; select an optimal combination of control parameters optimizing… system”:
(Figure 2)
It can be seen in Figure 2 above that a terminating condition (i=Generation) is used to cause it to loop through the probabilistic distributions until it is reached, causing the previously cited selection and controlling to thereby ensue. Once this termination condition has been reached, the “Elitism, Replace the Worst in P’ with the best in P” and “Find the Best String of the Generations So Far, Set P’ as P” step will have ensured that the optimal combination of parameters of all combinations is selected after all combinations were examined via the prior steps, thus selecting various combinations of parameters and finally settling on the optimal combination.
Further, HUANG teaches “control the system using the optimal combination of control parameters”:
([5. Conclusions, lines 9-16] “Using the genetic algorithm tuning program, the optimal controller parameter values for the HVAC system were determined successfully, with which the system yielded a satisfactory performance. From the output curve, we can see that the overshoot is equal to 0.38 I°C and the settling time 14.5 s, while using Ziegler-Nichols method, the overshoot and the settling time are equal to 0.922°C and 358 s respectively.”) Here, we see the system being controlled using the selected combination of parameters, and the current state of the system changing from it.
Further, the claim cites the various combinations of parameters having “corresponding costs of operation” and “optimizing the cost of the operation of the system” with the selected optimal combination, and “a deterministic relationship” between the parameters and the cost, which is taught by MORALES:
([Introduction, lines 4-7] “Computational methods which tackle uncertainty allow engineers to propose solutions less sensitive to environmental influences, while achieving simultaneously cost reduction or reliability improvement.”) Here, it is shown that more optimal solutions simultaneously achieve cost reduction and reliability improvement, meaning also that the relationship between optimization and cost is a deterministic relationship.
Regarding claim 8, it comprises similar limitations to claim 1 and is rejected under the same rationale.
Regarding claim 9, HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS teaches the limitations of claim 8. Further, claim 9, comprises similar additional limitations to claim 2 and is rejected under the same rationale.
Regarding claim 10, it comprises similar limitations to claim 3 and is rejected under the same rationale.
Regarding claim 15, it cites “a non-transitory computer-readable storage medium embodied thereon a program executable by a processor”, which is taught by KUMAR:
([0057, lines 31-34] “Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions (a program) stored in a memory (204) of the system (110). The memory (204) may be configured to store one or more computer-readable instructions or routines (a program) in a non-transitory computer readable storage medium, which may be fetched and executed to…”). Further, claim 15 comprises similar limitations to claim 1 and is rejected under the same rationale.
Claims 4, 7, 11, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS as applied above, and further in view of Jones, et al. Patent No.: US 12,253,274 B2 (hereafter, JONES)).
Regarding claim 4, HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS teaches the limitations of claim 1. These references fail to explicitly teach “wherein the control parameters are values of states of actuators of the system, such that the controller submits the control parameters to the system to cause the actuators of the system to change their states according to corresponding control parameters.” However, analogous art of a patented HVAC controller, JONES, does teach this:
([Detailed Description, lines 23 – 35] “HVAC system 10 includes HVAC component(s) 16, a supply air duct 20, a return air duct 22 (collectively, "ducts 20, 22"), dampers 24, and air filters 26. 25 Additionally, HVAC system 10 includes an HVAC controller 30 configured to control HVAC component(s) 16 to regulate one or more parameters within building 12. For example, HVAC controller 30 may be configured to control the comfort level ( e.g., temperature and/or humidity) in building 12 by activating and deactivating HVAC component(s) 16 in a controlled manner.”)
Here, a device designed to control a system has control parameters which are explicitly shown to actuate (activate and deactivate) components of the system, causing the system to change states according to the parameters.)
It would be obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the base reference of HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS with the teachings of JONES because JONES is a patented HVAC system controller that has similar analogous objectives.
One of ordinary skill in the art would be motivated to do so because without actuators in the system to correspond to the parameters of the controller, it would not be able to actually control the system as desired.
Regarding claim 11, it comprises similar limitations to claim 4 and is rejected under the same rationale.
Regarding claim 7, HUANG in view of KUMAR, MORALES, KOEHRSON, & AWS teaches the limitations of claim 1. These references fail to explicitly teach “wherein the selected control parameters are used by the controller to determine control commands specifying values of states of actuators of the system” However, JONES does teach this:
([Detailed Description, lines 23 – 35] “HVAC system 10 includes HVAC component(s) 16, a supply air duct 20, a return air duct 22 (collectively, "ducts 20, 22"), dampers 24, and air filters 26. 25 Additionally, HVAC system 10 includes an HVAC controller 30 configured to control HVAC component(s) 16 to regulate one or more parameters within building 12. For example, HVAC controller 30 may be configured to control the comfort level ( e.g., temperature and/or humidity) in building 12 by activating and deactivating HVAC component(s) 16 in a controlled manner.”)
Regarding claim 14, it comprises similar limitations to claim 7 and is rejected under the same rationale.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over HUANG in view of KUMAR, MORALES, KOEHRSON, AWS and JONES as applied above, and further in view of Jaber, et al. Patent No.: US 12,253,272 B1 (hereafter, JABER)).
Regarding claim 5, HUANG in view of KUMAR, MORALES, KOEHRSON, AWS and JONES teach the limitations of claim 4. Further, HUANG and JONES teach “wherein the system is a vapor compression system (VCS)”:
Huang ([3. Optimization, sentence 1] “In this study, a GA program based on Goldberg's "Simple Genetic Algorithm" [8] was developed in order to provide a genetics-based method for obtaining the optimal parameter values of a PI controller in an HVAC system.”)
JONES ([Col. 1, lines 40-41] “In general, this disclosure describes a heating, ventilation, and air conditioning (HVAC) controller…”)
(Both references are in relation to controlling an HVAC system, which is a type of vapor compression system.)
Further, JONES teaches the system “having different actuators including one or more of: a compressor, a valve, and a fan…”:
([Col. 4, lines 14-19] “…HVAC component(s) 16 may include any one or combination of a fan, a blower, a furnace, a heat pump, an electric heat pump, a geothermal heat pump, an electric heating unit, an AC unit, a humidifier, a dehumidifier, an air exchanger, an air cleaner, a damper, a valve, and a fan…”)
HUANG in view of KUMAR, MORALES, KOEHRSON, AWS, and JONES, fails to explicitly teach one or more of the above actuators “…such that control parameters specify a speed of the compressor, an opening of the valve, and a speed of the fan respectively.” However, analogous art teaching methods for controlling an HVAC system, JABER, does teach this:
([Col. 14, lines 39-46] “In addition, in some embodiments, method 200 may employ a PI control loop, function, or scheme at 216, 218 to provide the desired speed changes for the indoor fan and compressor. Specifically, in some embodiments the indoor coil temperature error from block 210 is used as a feedback error within a PI control loop for controlling the indoor fan speed at block 216, and within a PI control loop for controlling the compressor speed at block 218.”)
It would be obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the base reference of HUANG in view of KUMAR, MORALES, KOEHRSON, AWS, & JONES with the teachings of JABER because JABER addresses methods for controlling HVAC systems effectively and thus has similar analogous objectives.
One of ordinary skill in the art would be motivated to do so because JABER illustrates that controlling these things (fan and compressor speed) is vital to controlling humidity and may prevent “property damage and/or occupant discomfort” ([Background] “lines 23-24”)
Regarding claim 12, it comprises similar limitations to claim 5 and is rejected under the same rationale.
Conclusion
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/Matthew Lee Lewis/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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