DETAILED ACTION
This Office Action is in response to the Amendment filed on 05/12/2026
THIS ACTION IS MADE FINAL
Response to Arguments
Applicant’s arguments, see pages 6-7 of remarks, filed 05/12/2026, with respect to rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. The rejection of claims 1-20 under 35 U.S.C. 101 has been withdrawn in view of the argument and the amendment filed on 05/12/2026.
Applicant’s arguments, see page 8-9 of remarks, filed 05/12/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1).
Applicant's arguments filed 05/12/2026 with respect to rejection of claim 8 under 35 U.S.C. § 102 have been fully considered but they are not persuasive.
Applicant in page 9 argues, “However, Asmus does not teach automatically activating or deactivating chiller systems based on a determination made by an analysis model using neural networks as recited in amended claim 8.”
Examiner respectfully disagrees. As an initial matter, claim 8 does not recite using neural networks. Claim 8 recites, “automatically activating or deactivating one or more of the plurality of chiller systems based on the determined number of chiller systems to utilize in cooling the load.” Asmus in ¶0061 teaches, turning on the devices of the combination with optimum energy consumption (310). Therefore, Asmus teaches the claimed limitation.
Applicant's arguments filed 05/12/2026 with respect to rejection of claim 17 under 35 U.S.C. § 103 have been fully considered but they are not persuasive.
Applicant in page 10 argues, “Rousselet does not teach automatically adjusting chiller system operating parameters in accordance with operating parameter adjustments generated by an analysis model as recited in amended claim 17”
Examiner respectfully disagrees. Rousselet in ¶0085 teaches, adjusting water flow rate by controlling the speed of pump 20. ¶0046 teaches, the pump 20 directs cooled water from the cooling tower 16 along a cool process line 32 to a water cooled condenser 34 of the chiller 18 wherein the water receives heat from the chiller 18. Therefore, Rousselet teaches, automatically adjusting, with the control system, a flow of cooling water of the chiller system in accordance with the operating parameter adjustments
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Asmus (US20130345880A1)
Regarding claim 8,
Asmus teaches, A system, comprising:
a plurality of chiller systems each configured to be selectively activated for cooling a load; (¶0028-¶0029 teaches, Chiller plant subsystems 130 are illustrated to include a plurality of chillers 132)
a control system communicatively coupled to the plurality of chiller systems and including: (¶0028-¶0029 teaches, chiller plant controller 102)
one or more computer memories configured to store software instructions; (¶0032 teaches, Chiller plant controller 102 includes memory 108 is communicably connected to processor 106 via processing circuit 104 and includes computer code for executing (e.g., by processing circuit 104 and/or processor 106) one or more processes)
one or more processors configured to execute the software instructions, (¶0032 teaches, Chiller plant controller 102 to processor 106 configured to execute computer code)
wherein executing the software instructions performs a method comprising:
receiving, at an analysis model of the control system, input parameters associated with the plurality of chiller systems; (¶0050 teaches chiller plant controller 102 receives operating conditions about the chiller plant devices)
processing the input parameters with the analysis model; and (¶0052 teaches, Step 302 may utilize binary optimization to determine one or more feasible combinations of devices that will satisfy the plant load at a time and for an actual or expected set of conditions (e.g., load conditions, weather conditions, etc.)
determining, with the analysis model based on the input parameters, a number of the plurality of chiller systems to utilize in cooling the load. (¶0052 teaches, determine one or more feasible combinations of devices that will satisfy the plant load)
automatically activating or deactivating one or more of the plurality of chiller systems based on the determined number of chiller systems to utilize in cooling the load. (¶0061 teaches, turning on the devices of the combination with optimum energy consumption (310))
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1).
Regarding claim 1,
Lee teaches, A method, comprising:
receiving, with a control system associated with the chiller system, a measured power consumption of the chiller system; (¶0215 teaches, receiving an actually measured amount of power consumed by the facility through the data collection module 121. ¶0264 teaches facility includes a chiller.)
providing, to an analysis model of the control system, a plurality of operating parameters associated with the chiller system; (¶0303 teaches, The system setting module 122 may supply information of facilities, to the energy use amount simulation module 123. ¶0304 teaches, The information of the facilities may be information necessary for modeling the facilities and may include specifications of the facilities such as configurations, capacities, kinds, and/or the like of the facilities. ¶0264 teaches facility includes a chiller.)
generating, with the analysis model, a predicted power consumption of the chiller system; and (¶0305 teaches, in operation S30 the energy use amount simulation module 123 may model the at least one facility, based on the information of the at least one facility and may simulate the consumption power of the modeled at least one facility that operates according to the control scenario.)
comparing the predicted power consumption to the measured power consumption. (¶0361 teaches, operation S62 of determining whether a difference between the actual measurement consumption amount and the prediction use amount is out of a predetermined range)
Lee doesn’t teach, chilling, with a chiller system, a load associated with a semiconductor fabrication facility; (Seki in ¶0043 teaches a chiller system being used in a semiconductor manufacturing apparatus involving plasma etching as described later, and is configured to perform control so as to maintain, for example, a temperature of a semiconductor wafer)
Seki is an art in the area of interest as it teaches a chiller device 100 is used for, for example, a semiconductor manufacturing apparatus. A combination of Seki with Lee would allow the system to be used in a chiller system associated with a semiconductor fabrication facility. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Seki with Lee. One would have been motivated to do so because doing so would allow the system to maintain a temperature of a semiconductor wafer to a constant temperature in the plurality of steps of fabrication as taught by Seki in ¶0043.
Lee and Seki doesn’t teach, automatically adjusting, with the control system, a flow of a refrigerant fluid of the chiller system based on the comparison of the predicted power consumption to the measured power consumption. (Yoon in ¶0034 teaches, Then, as shown in FIG. 2, the demand control unit 120 estimates an amount of power consumed by the air conditioners using the data received from the power consumption amount detecting unit 110 and monitors whether the estimated power amount exceeds a pre-set target power amount (S1). ¶0038 teaches changing a circulation rate of a refrigerant based on the above determination)
Yoon is an art in the area of interest as it relates to a system and method for controlling multiple air conditioners. A combination of Yoon with Lee and Seki would teach automatically adjusting a flow of a refrigerant fluid of the chiller system based on the comparison of the predicted power consumption to the measured power consumption. Lee and Seki already teaches comparing a predicted power consumption and measured power consumption of a chiller system. However, it doesn’t teach adjusting a flow of refrigerant fluid based on the power consumption comparison. Yoon teaches changing a circulation rate of a refrigerant based on power consumption comparison. One of ordinary skill in the art could modify the teaching of Lee and Seki to include adjusting a refrigerant fluid of the chiller system based on the comparison. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Yoon with Lee and Seki. One would have been motivated to do so because doing so would allow the system to reduce power consumption if the calculated power consumption is above an expected power consumption. (See Yoon ¶0038).
Regarding claim 2,
Lee, Seki and Yoon teaches, The method of claim 1, comprising outputting, with the control system, an alert if the predicted power consumption is different from the measured power consumption by more than a threshold difference. (Lee in ¶0361 teaches, operation S62 of determining whether a difference between the actual measurement consumption amount and the prediction use amount is out of a predetermined range and operation S63 of displaying alarm on the screen when the difference between the actual measurement consumption amount and the prediction use amount is out of the predetermined range)
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1) and further in view of Subbloie (US20210173358A1)
Regarding claim 3,
Lee, Seki and Yoon doesn’t teach, The method of claim 2, wherein the alert indicates that maintenance should be performed on one or more components of the chiller system. (Subbloie in ¶0123 teaches, The measured actual power usage is then compared to the expected power usage signature 408 and a determination is made if the measured usage exceeds the threshold deviation value 410. ¶0124 teaches, If, however, it is determined that the measured power usage does exceed the threshold deviation, the system will move to generate and alert 412. ¶0125 teaches, the alert could comprise a text or email message to maintenance personnel that the system needs to be checked)
Subbloie is an art in the area of interest as it relates to monitoring power consumption. Lee already teaches, displaying alarm on the screen when the difference between the actual measurement consumption amount and the prediction use amount is out of the predetermined range (see Lee ¶0361). However, it doesn’t teach, the alarm to indicate a maintenance should be performed. Subbloie teaches an alert which comprises a message that the system needs to be checked, when the measured power usage exceeds expected power usage. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Subbloie with Lee, Seki and Yoon to include an indication in the alert that the maintenance should be performed . One would have been motivated to do so because doing so would allow the system to notify a service personnel of potential problems with the equipment that needs to be checked and serviced, as taught by Subbloie in ¶0125.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1) and further in view of Pedder (US20230341827A1)
Regarding claim 4,
Lee, Seki and Yoon doesn’t teach, The method of claim 2, wherein the alert indicates that maintenance should be performed on a sensor associated with the chiller system. (Pedder in ¶0079 teaches, A deviation condition may be detected based on excessive deviation from the expected (or reference) behavior. ¶0121 teaches the control unit may determine which of the sensors generated the sensors on which the deviating monitored values and/or variations may be based, and may flag these sensors. Furthermore, the control unit may estimate a cause of the deviation condition based at least in part on sensor measurements generated by these particular sensors. If the estimated cause may be a malfunctioning sensor, then the control unit may flag that sensor for repair or replacement, notify the operator, substitute the sensor, or the like.)
Pedder is an art in the area of interest as it teaches, determining sensor malfunction (¶0121). Lee already teaches, displaying alarm on the screen when the difference between the actual measurement consumption amount and the prediction use amount is out of the predetermined range (see Lee ¶0361). However, it doesn’t teach, the alarm to indicate a maintenance should be performed on a sensor. Pedder in ¶0079 and ¶0121 teaches determining deviation is caused by sensor fault and notifying the operator. A combination of Pedder with Lee, Seki and Yoon would allow the system to determine sensor as cause of the difference between actual measurement consumption amount and the prediction use amount and issuing an alert regarding sensor maintenance. One would have been motivated to combine the teaching of Pedder with Lee, Seki and Yoon because doing so would allow flagging the sensor for replacement, as taught by Pedder in ¶0141.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1) and further in view of Wallace (US20170089625A1)
Regarding claim 5,
Lee, Seki and Yoon doesn’t teach, The method of claim 1, further comprising automatically adjusting, based on the comparison, a refrigeration ton of the chiller system; a refrigerant fluid evaporation pressure, a refrigerant fluid condensing pressure; and a power consumption of a compressor of the chiller system. (Wallace in ¶0150 teaches, comparing actual versus predicted parameters (e.g., power consumption) and optimizing power consumption of the refrigeration system 10 by coordinating power consumption of the compressor rack 14. ¶0106-¶0107 teaches, optimizing total refrigeration system energy consumption by modifying operation of at least one of the compressor rack to reduce power consumption)
Wallace is an art in the area of interest as it relates to refrigeration systems (see ¶0002). One of ordinary skill in the art could combine the teachings of Wallace with Lee, Seki and Yoon to include in the method the capability of adjusting a power consumption of a compressor based on comparing predicted power consumption to the measured power consumption. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Wallace with Lee, Seki and Yoon. One would have been motivated to do so because doing so would allow the system to optimize power consumption of the refrigeration system, as taught by Wallace in ¶0150 and ¶0106-¶0107.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US20160103475A1) in view of Seki (US20200132344A1) and further in view of Yoon (US20080178615A1) and further in view of Madhusudanan (A MACHINE LEARNING FRAMEWORK FOR ENERGY CONSUMPTION PREDICTION)
Regarding claim 6,
Lee, Seki and Yoon doesn’t teach, The method of claim 1, wherein the analysis model includes a polynomial regression model. (Madhusudanan in Page 25 section 3. METHODS teaches, statistical regression modelling for energy consumption forecasting. Page 48 section 4.6. MODULES USED teaches, using Polynomial Regression)
Madhusudanan is an art in the area of interest as it teaches energy consumption forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Madhusudanan with Lee, Seki and Yoon in order to use polynomial regression model to predict power consumption. One would have been motivated to do so because it was found that second degree polynomial had the least deviation (as taught by Madhusudanan in Page 49 section 4.6. MODULES USED and polynomial regression performs better in determining expected energy consumption as taught by Madhusudanan in Page iii section ABSTRACT.
Regarding claim 7,
Lee, Seki and Yoon and Madhusudanan teaches, The method of claim 6, wherein the polynomial regression model has a degree of two or higher. (Madhusudanan in Page 25 section 3. METHODS teaches, statistical regression modelling for energy consumption forecasting. Page 48-49 section 4.6. MODULES USED teaches, using second degree polynomial Regression)
Claim(s) 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmus (US20130345880A1) in view of Moon (Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron)
Regarding claim 9,
Asmus doesn’t teach, The system of claim 8, wherein the analysis model includes a neural network trained with a machine learning process to generate a predicted power consumption for each number of chiller systems. (Moon in Abstract teaches a hybrid short-term load forecast model to predict power consumption. Page 7-8 section Building a Hybrid Forecasting Model teaches using an artificial neural network to build the hybrid model.)
Moon is an art in the area of interest as it teaches a model to predict power consumption (Abstract). A combination of Moon with Asmus would allow the system to use neural network trained with a machine learning process to generate a predicted power consumption. Asmus in ¶0060 already teaches, estimating power consumption of chillers. However it doesn’t teach using a neural network to generate the estimated power consumption. Moon teaches using a neural network to generate the estimated power consumption. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Moon with Asmus, and use a neural network to estimate power consumption. One would have been motivated to do so because a hybrid model with neural network is able to provide good performance in predictions regardless of diverse external factors, as taught by Moon in Page 15 section 5.3.ComparisonofForecastingTechniques)
Regarding claim 10,
Asmus and Moon teaches, The system of claim 9, wherein determining the number of the plurality of chiller systems to utilize includes selecting the number of chiller systems that results in the lowest predicted power consumption. (Asmus in ¶0061 teaches, turning on the devices of the combination with optimum energy consumption (310). According to an exemplary embodiment, the optimum energy consumption is the lowest energy consumption for devices that will meet the plant load and satisfy constraints on the system)
Regarding claim 11,
Asmus and Moon teaches, The system of claim 9, wherein the analysis model includes a decision tree model coupled to the neural network model. (Moon in Abstract teaches a hybrid short-term load forecast model to predict power consumption. Page 7-8 section Building a Hybrid Forecasting Model teaches using an artificial neural network and a decision tree model to build the hybrid model.)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmus (US20130345880A1) in view of Estelle (US20220373614A1)
Regarding claim 12,
Asmus doesn’t teach, The system of claim 8, wherein the analysis model is configured to generate a predicted average load current for each number of chiller systems. (Estelle in ¶0078 teaches determining a predicted current or voltage measurement)
Estelle is an art in the area of interest as it teaches, predicting current measurement. A combination of Estelle with Asmus would teach generating a predicted average load current for each number of chiller systems. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Estelle with Asmus. One would have been motivated to do so because doing so would allow the system to identify fault conditions relating to excessive, insufficient, or absent current.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmus (US20130345880A1) in view of Estelle (US20220373614A1) and further in view of Wallace (US20200408447A1)
Regarding claim 13,
Asmus and Estelle doesn’t teach, The system of claim 12, wherein determining the number of the plurality of chiller systems to utilize includes selecting the number of chiller systems that results in the lowest predicted average load current. (Wallace in ¶0124 teaches, power consumption formula which shows that power consumption has a linear relationship with current. Asmus in ¶0061 already teaches, selecting a chiller with lowest energy consumption. A combination of Asmus’s teaching of selecting chiller with lowest energy consumption and Wallace’s teaching regarding the linear relationship between power consumption and current, would teach selecting chiller with lowest load current.)
Wallace is an art in the area of interest as it teaches a relationship between power consumption and current (see ¶0124). A combination of Asmus and Estelle’s teaching of selecting chiller with lowest energy consumption and Wallace’s teaching regarding the linear relationship between power consumption and current, would teach selecting chiller with lowest load current. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Wallace with Asmus and Estelle since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 14,
Asmus, Estelle and Wallace teaches, The system of claim 13, wherein the analysis model includes a linear regression model configured to generate the predicted average load current. (Estelle in ¶0062 teaches predictive analyses include linear regression)
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmus (US20130345880A1) in view of Moon (Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron) and further in view of Estelle (US20220373614A1)
Regarding claim 15,
Asmus doesn’t teach, The system of claim 8, wherein the analysis model includes: a neural network trained with a machine learning process to generate a predicted power consumption for each number of chiller systems; and (Moon in Abstract teaches a hybrid short-term load forecast model to predict power consumption. Page 7-8 section Building a Hybrid Forecasting Model teaches using an artificial neural network to build the hybrid model.)
Moon is an art in the area of interest as it teaches a model to predict power consumption (Abstract). A combination of Moon with Asmus would allow the system to use neural network trained with a machine learning process to generate a predicted power consumption. Asmus in ¶0060 already teaches, estimating power consumption of chillers. However, it doesn’t teach using a neural network to generate the estimated power consumption. Moon teaches using a neural network to generate the estimated power consumption. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Moon with Asmus, and use a neural network to estimate power consumption. One would have been motivated to do so because a hybrid model with neural network is able to provide good performance in predictions regardless of diverse external factors, as taught by Moon in Page 15 section 5.3.ComparisonofForecastingTechniques)
However Asmus and Moon as combined doesn’t teach, a linear regression model configured to generate a predicted average load current for each number of chiller systems. (Estelle in ¶0078 teaches determining a predicted current or voltage measurement. ¶0062 teaches predictive analyses include linear regression)
Estelle is an art in the area of interest as it teaches, predicting current measurement. A combination of Estelle with Asmus and Moon would teach generating a predicted average load current for each number of chiller systems. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Estelle with Asmus and Moon. One would have been motivated to do so because doing so would allow the system to identify fault conditions relating to excessive, insufficient, or absent current.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmus (US20130345880A1) in view of Moon (Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron) and further in view of Estelle (US20220373614A1) and further in view of Wallace (US20200408447A1)
Regarding claim 16,
Asmus, Moon and Estelle teaches, The system of claim 15, wherein the method includes determining the number of the plurality of chiller systems to utilize in cooling the load based on the predicted power consumption for each number of chiller systems (Asmus in ¶0052 teaches, determine one or more feasible combinations of devices that will satisfy the plant load at a time and for an actual or expected set of conditions (e.g., load conditions, weather conditions, etc.). ¶0061 teaches, turning on the devices of the combination with optimum energy consumption (310). According to an exemplary embodiment, the optimum energy consumption is the lowest energy consumption for devices that will meet the plant load and satisfy constraints on the system)
Asmus, Moon and Estelle doesn’t teach, and the predicted average load current each number of chiller systems. (Wallace in ¶0124 teaches, power consumption formula which shows that power consumption has a linear relationship with current. Asmus in ¶0061 already teaches, selecting a chiller with lowest energy consumption. A combination of Asmus’s teaching of selecting chiller with lowest energy consumption and Wallace’s teaching regarding the linear relationship between power consumption and current, would teach selecting chiller with lowest load current.)
Wallace is an art in the area of interest as it teaches a relationship between power consumption and current (see ¶0124). A combination of Asmus, Moon and Estelle’s teaching of selecting chiller with lowest energy consumption and Wallace’s teaching regarding the linear relationship between power consumption and current, would teach selecting chiller with lowest load current. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Wallace with Asmus, Moon and Estelle since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rousselet (US20210180891A1) in view of Seki (US20200132344A1)
Regarding claim 17,
Rousselet teaches, A method, comprising:
providing, to an analysis model of a control system, a plurality of operating parameters associated with the chiller system; and (¶0056-¶0057 and Fig. 2 and 3A teaches providing collected sensor data and set points data to a providing 84 which includes machine learning models 94. ¶0025 teaches, a chiller)
generating, with the analysis model, operating parameter adjustments for reducing power consumption of the chiller system. (¶0075 and Fig. 3B teaches searching for optimal operating mode and set points for minimizing energy consumption)
automatically adjusting, with the control system, a flow of cooling water of the chiller system in accordance with the operating parameter adjustments. (¶0084 teaches, determining one or more optimal parameters which include the optimal operating mode. ¶0085 teaches, adjusting water flow rate by controlling the speed of pump 20. ¶0046 teaches, the pump 20 directs cooled water from the cooling tower 16 along a cool process line 32 to a water cooled condenser 34 of the chiller 18 wherein the water receives heat from the chiller 18)
Rousselet doesn’t teach, chilling, with a water chiller system, a load associated with a semiconductor fabrication facility; (Seki in ¶0043 teaches a chiller system being used in a semiconductor manufacturing apparatus involving plasma etching as described later, and is configured to perform control so as to maintain, for example, a temperature of a semiconductor wafer)
Seki is an art in the area of interest as it teaches a chiller device 100 is used for, for example, a semiconductor manufacturing apparatus. A combination of Seki with Rousselet would allow the system to be used in a chiller system associated with a semiconductor fabrication facility. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Seki with Rousselet. One would have been motivated to do so because doing so would allow the system to maintain a temperature of a semiconductor wafer to a constant temperature in the plurality of steps of fabrication as taught by Seki in ¶0043.
Regarding claim 18,
Rousselet and Seki teaches, The method of claim 17, comprising training the analysis model with a machine learning process. (Rousselet in ¶0094-¶0095 teaches training the model using machine learning process)
Regarding claim 19,
Rousselet and Seki teaches, The method of claim 17, comprising: generating, with the analysis model, a first predicted power consumption of the chiller system based on the input parameters; (Rousselet in ¶0070 teaches energy consumption for input parameters)
adjusting, with the analysis model, values of the input parameters; (Rousselet in ¶0070 teaches, cycling 154 through potential parameters including operating modes (wet, dry, hybrid or adiabatic) of the cooling tower 16, values for the leaving process fluid temperature (LPFT) and/or pressure, and the process fluid flow rate)
generating, with the analysis model, a second predicted power consumption of the chiller system based on the adjust values; (Rousselet in ¶0070 teaches, cycling 154 through potential parameters including operating modes (wet, dry, hybrid or adiabatic) of the cooling tower 16, values for the leaving process fluid temperature (LPFT) and/or pressure, and the process fluid flow rate to calculate 160 system energy, water consumption, and operating cost for possible combinations of potential parameters such as every possible combination of potential parameters)
and generating the operating parameter adjustments based on the second predicted power consumption. (Rousselet in ¶0084 and Fig. 3B teaches, providing or returning 172 one or more optimal parameters of the cooling subsystem 14 to achieve the target optimizing criterion, e.g., minimized energy consumption, minimized water consumption, or minimized operating cost.)
Regarding claim 20,
Rousselet and Seki teaches, The method of claim 17, wherein the operating parameter adjustments include a pressure difference adjustment for a chilled water pipe of the chiller system. (Rousselet in ¶0029 teaches optimal operating parameter includes an optimal pressure of the process fluid leaving the heat rejection apparatus)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISTIAQUE AHMED whose telephone number is (571)272-7087. The examiner can normally be reached Monday to Thursday 10AM -6PM and alternate Fridays.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISTIAQUE AHMED/Examiner, Art Unit 2116
/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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