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 .
DETAILED ACTION
The action is in response to the Applicant’s communication filed on 03/06/2025.
Claims 1-17 are pending, where claims 1 and 14 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/19/2024 has been filed. The submission is in-compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim(s) 7-8 is/are objected to because of the following informalities:
Claims 7-8 recites the terminology "and/or", what is actually being performed by the alternatively claimed language. However, it will be assumed "or” for the purposes of examination.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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 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.
Claims 1-17 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Ross, et al. USPGPub No. 10180261 B1.
As to claim 1, Ross discloses A method for operating a flow producing unit comprising a fan control that is configured to control at least one fan (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - virtual sensors to generate condition signals - based on one or more relationships between the generated condition signals and one or more other measured values” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions - maximum allowable temperature” see Fig.1-10), wherein the method comprises:
providing training data for a model creation device configured for machine learning, wherein the training data comprise at least one room air parameter, at least one fan operating parameter and at least one environmental air parameter, (Ross [col 15-21] “a machine learning service - used to determine optimal control parameters for controlling cooling resources - machine learning service utilize condition information stored in metrics storage 914 to generate optimized control parameters using machine learning techniques - provide control outputs - based on machine learning operations” [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - to determine control parameters - include fan speeds” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, physical sensors measure values multiple environmental air parameters, machine learning service utilize condition information stored to generate optimized control parameters using machine learning techniques obviously provides training data for a model creation device configured for machine learning, - room air parameter, at least one fan operating parameter and at least one environmental air parameter)
determining a control model, defining a correlation between the at least one room air parameter, the at least one fan operating parameter and the at least one environmental air parameter by means of the model creation device, (Ross [col 3-8] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors - virtual sensors to generate condition signals - based on one or more relationships between the generated condition signals and one or more other measured values” [col 9-14] machine learning techniques used to update a model used in a model based cooling control system - machine learning technique used to learn relationships between control outputs, energy consumption, and water usage - to select sets of control outputs match the learned relationships based on room conditions similar to previously observed room conditions” [col 15-21] “a machine learning service - used to determine optimal control parameters for controlling cooling resources - machine learning service utilize condition information stored in metrics storage 914 to generate optimized control parameters using machine learning techniques - provide control outputs - based on machine learning operations” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” see Fig.1-10, multiple environmental air parameters, model based cooling control utilize condition information stored to generate optimized control parameters using machine learning techniques, to learn relationships between control outputs and update machine learning techniques to model based cooling control system obviously provides determining a control model, defining a correlation between the at least one room air parameter, the at least one fan operating parameter and the at least one environmental air parameter by means of the model creation device)
determining a current room air measurement value for each room air parameter respectively that is used as a controlled parameter, determining at least one current environmental air measurement value for the at least one environmental air parameter or for multiple environmental air parameters, control of the at least one fan using the control model, the at least one current room air measurement value and the at least one current environmental air measurement value, so that a room air parameter used as controlled parameter remains in a predefined parameter value range and thereby electrical energy required for operation of the fan is minimum (Ross [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - based on - measured values - performs optimization calculations to select a set of control parameters for controlling air handling systems - to determine control parameters - include fan speeds for fans - fans 110, water injection rates (e.g. valve position) for injection valves, such as water injection valves 120, and fan speeds - include iterating through multiple sets of control parameters to determine corresponding predicted room conditions - determining corresponding predicted energy and water usages - selecting a set of control parameters meets one or more constraints for cooling - include selecting control parameters that result in consuming less energy and/or water” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, physical sensors measure values multiple environmental air parameters, model-based cooling control system performs optimization calculations to select a set of control parameters to determine control parameters include fan speeds based on control parameters meets (predefined parameter) constraints to reduce or optimize energy meeting conditions obviously provides fan using the control model, the at least one current room air measurement value and the at least one current environmental air measurement value, so that a room air parameter used as controlled parameter remains in a predefined parameter value range and thereby electrical energy required for operation of the fan is minimum).
Application and the reference Ross are analogous arts from the same field of endeavor and contain overlapping structural and functional similarities and both contain fan controller for building management system.
It would be therefore obvious to one having ordinary skill in the art at the time of the invention that machine learning techniques utilize multiple environmental parameter stored or measured used to generate optimized control parameters for model-based cooling control system are assumed as providing training data for a model creation device for machine learning.
As to claim 2, Ross further discloses The method according to claim 1, wherein the fan control controls the at least one fan based on the control model independent from the model creation device (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - virtual sensors to generate condition signals - based on one or more relationships between the generated condition signals and one or more other measured values” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” see Fig.1-10, model-based cooling control system includes air handling systems and air moving devices fans obviously provides fan control controls the at least one fan based on the control model independent from the model creation device).
As to claim 3, Ross further discloses The method according to claim 1, wherein the model creation device is realized separately from the fan control (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - virtual sensors to generate condition signals - based on one or more relationships between the generated condition signals and one or more other measured values” [abstract] see Fig.1-10, model-based cooling control for air moving devices fans as virtual control obviously provides model creation device is realized separately from the fan control).
As to claim 4, Ross further discloses The method according to claim 1, wherein the model creation device and the fan control are communicatively connected (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - virtual sensors to generate condition signals - based on one or more relationships between the generated condition signals and one or more other measured values” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions - maximum allowable temperature” see Fig.1-10, model-based cooling control, air moving devices fans, sensors are coupled obviously provides model creation device and the fan control are communicatively connected).
As to claim 5, Ross further discloses The method according to claim 1, wherein the at least one room air parameter comprises one or more of the following parameters: a room air temperature, a room air humidity, a room air pressure, a room air component, which describes a fraction or an amount of a gas component of a room air (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc.” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” see Fig.1-10, physical sensors provides the limitations).
As to claim 6, Ross further discloses The method according to claim 1, wherein the at least one environmental air parameter comprises one or more of the following parameters: an environmental air temperature, an environmental air humidity, an environmental air pressure, an environmental air component, which describes a fraction or an amount of a gas component of an environmental air (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc.” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” see Fig.1-10, physical sensors provides the limitations).
As to claim 7, Ross further discloses The method according to claim 1, wherein the at least one fan operating parameter comprises one or more of the following parameters: a fan rotational speed, a fan torque, a motor voltage of an electric motor of the fan, a motor current of an electric motor of the fan, a mechanical and/or electrical power of the fan (Ross [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - based on - measured values - performs optimization calculations to select a set of control parameters for controlling air handling systems - to determine control parameters - include fan speeds for fans - fans 110, water injection rates (e.g. valve position) for injection valves, such as water injection valves 120, and fan speeds - include iterating through multiple sets of control parameters to determine corresponding predicted room conditions - determining corresponding predicted energy and water usages - selecting a set of control parameters meets one or more constraints for cooling - include selecting control parameters that result in consuming less energy and/or water” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, model-based cooling control, selecting control parameters meets constraints for cooling includes operating parameter for fan speed obviously provides fan operating parameter - a fan rotational speed).
As to claim 8, Ross further discloses The method according to claim 1, wherein the training data additionally comprise at least one condition parameter and/or at least one geographic parameter (Ross [col 15-21] “a machine learning service - used to determine optimal control parameters for controlling cooling resources - machine learning service utilize condition information stored in metrics storage 914 to generate optimized control parameters using machine learning techniques - provide control outputs - based on machine learning operations” [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - to determine control parameters - include fan speeds” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, physical sensors measure values multiple environmental air parameters includes room temperature, machine learning service utilize condition information stored to generate optimized control parameters (condition parameter) obviously provides training data additionally comprise at least one condition parameter and/or at least one geographic parameter).
As to claim 9, Ross further discloses The method according to claim 1, wherein the training data comprise in addition at least one fan operating parameter, wherein the model creation device creates the control model by learning the training data (Ross [col 15-21] “a machine learning service - used to determine optimal control parameters for controlling cooling resources - machine learning service utilize condition information stored in metrics storage 914 to generate optimized control parameters using machine learning techniques - provide control outputs - based on machine learning operations” [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - to determine control parameters - include fan speeds” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, physical sensors measure values multiple environmental air parameters includes room temperature and fan speed (operating parameter), machine learning service utilize condition information stored to generate optimized control parameters (condition parameter) obviously provides fan operating parameter, wherein the model creation device creates the control model by learning the training data).
As to claim 10, Ross further discloses The method according to claim 1, wherein the model creation device, whereby the model creation device carries out a simulation at different parameter values of the at least one fan operating parameter based on the training data in order to create the control model (Ross [col 15-21] “a machine learning service - used to determine optimal control parameters for controlling cooling resources - machine learning service utilize condition information stored in metrics storage 914 to generate optimized control parameters using machine learning techniques - provide control outputs - based on machine learning operations” [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - to determine control parameters - include fan speeds” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, model based cooling control system, physical sensors measure values multiple environmental air parameters includes room temperature and fan speed (operating parameter), machine learning service utilize condition information to generate optimized control parameters obviously provides model creation device carries out a simulation at different parameter values of the at least one fan operating parameter based on the training data in order to create the control model).
As to claim 11, Ross further discloses The method according to claim 1 whereby the at least one fan is controlled so that a rotational speed of the fan is as low as possible in order to maintain the at least one room air parameter in the predefined parameter value range (Ross [col 3-8] “model-based cooling control system - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc. - generate condition signals - based on - measured values - performs optimization calculations to select a set of control parameters for controlling air handling systems - to determine control parameters - include fan speeds for fans - fans 110, water injection rates (e.g. valve position) for injection valves, such as water injection valves 120, and fan speeds - include iterating through multiple sets of control parameters to determine corresponding predicted room conditions - determining corresponding predicted energy and water usages - selecting a set of control parameters meets one or more constraints for cooling - include selecting control parameters that result in consuming less energy and/or water” [abstract] “model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions” [col 9-14] see Fig.1-10, model-based cooling control, selecting control parameters meets (predefined parameter) constraints for cooling includes operating parameter for fan speed and selecting control parameters in consuming less energy obviously provides controls rotational speed of the fan - to maintain room air parameter in the predefined parameter value range).
As to claim 12, Ross further discloses The method according to claim 1, wherein the controlled parameter is a room air temperature (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc.” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions - maximum allowable temperature” see Fig.1-10).
As to claim 13, Ross further discloses The method according to claim 1, wherein the control model depends on a fan type (Ross [col 3-6] “model-based cooling control system - includes air handling systems 110, 112, and 114 - include air moving devices (e.g. fans) 116 - physical sensors such as thermocouples, resistance temperature detectors, air pressure sensors, air flow sensors, etc.” [abstract] “multiple air handling systems for cooling - model based cooling control system - determine a set of control parameters - to reduce or optimize an amount of energy - satisfying one or more conditions - maximum allowable temperature” see Fig.1-10, model-based cooling control obviously provides control model depends on a fan type).
As to the independent claim 14, the claims recite similar limitations as the independent claim 1 and rejected using same rational as stated above.
As to claims 15-17, the claims recite similar limitations as claims 4-6 and rejected using same rational as stated above.
Citation of Pertinent Prior Art
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2141.02 VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, i.e., as a whole and 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record:
Grover, USPGPub No. 2025/0008704 A1 discloses a method directed toward adaptive control systems in cooling systems using historical operating data to establish current operating parameters and sensor readings to update operating conditions exceed historical operating conditions.
Obinelo, USPGPub No. 2015/0134123 A1 discloses a CFD for modeling environmental characteristics for controlling instrumentation in an environment to optimize efficiency of air-moving systems serving an environment.
Fan, et al. USPGPub No. 2025/0257895 A1 discloses a method for controlling an air conditioner obtaining plurality of predicted control parameter and historical control parameter for determining target control parameter and controlling air conditioner to operate according to target control parameter.
Murakami, et al. USP No. 11,662117 B2 discloses a fan control apparatus acquires measured value of selected conditions at a boundary to control rotational speed that measured values of first and second sensors are same.
Fan, et al. USPGPub No. 2019/0187635 A1 discloses a machine learning used to control building environmental data collected by sensors and predicts attribute affecting control of environment and machine learning model predict load on environmental system, resource consumption by the environmental system, or cost of operating the environmental system.
Risbeck, et al. USPGPub No. 2023/0250988 A1 discloses a building management system and controller associated with plurality of environment species to estimate unknown parameters based on inputting plurality of data into optimization model to predict values of a control objective as control decision.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Md Azad whose telephone @(571)272-0553 or email: md.azad@uspto.gov. The examiner can normally be reached on Mon-Thu 9AM-5PM.
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/Md Azad/
Primary Examiner, Art Unit 2119
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