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 .
Claim Objections
As per claims 1, 8 and 15, change first introduction of “signal” to “a signal”.
As per claims 3 and 9, the determining step recites, “…higher than the optimal …”, but then the responsive step states “…is lower than the optimal…”. This is inconsistent and confusingly worded.
As per claim 13, change “a sensor” to “the sensor”.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zugibe et al., US 2013/0269376 A1, in view of Chambers, US 3,557,342 A.
As per claim 1, Zugibe et al. discloses a computer-implemented method for controlling temperature of a refrigeration equipment (e.g., See Zugibe et al.; [0150] – [0151]; “The invention provides a method for controlling refrigeration system or chillers… the compressor 100 produces hot dense refrigerant vapor … the vapor 103 is subjected to measurements of refrigerant temperature and pressure by temperature gage 155 and pressure gage 156”), comprising:
receiving signal generated by sensors mounted in the refrigeration equipment (e.g., See Zugibe et al.; [0150] – [0151]; “The compressor 100 produces hot dense refrigerant vapor in the line 106… The evaporator 03 is subjected to measurements of refrigerant temperature and pressure by temperature gage 155 and pressure gage 156… An oil sensor 159 provides a continuous measurement of oil concentration in the evaporator 103…);
determining an optimal temperature for the refrigeration equipment, the optimal temperature determined based on a plurality of factors including the signal and at least one or more external factors (e.g., See Zugibe et al.; [0153] – [0156], which discloses “The variables are fed into the adaptive control 200 employing a nonlinear model of the system, based on neural network 203 technology… The variables include refrigerant charge level, optionally system power consumption, as well as thermodynamic parameters, including condenser and evaporator water temperature in and out, condenser and evaporator water flow rates and pressure, in and out, compressor RPM, suction and discharge pressure and temperature, and ambient pressure and temperature.”); and
sending a modified signal to a control module of the refrigeration equipment, wherein the control module controls the refrigeration equipment to change a current temperature of the refrigeration equipment towards the optimal temperature (e.g. See Zugibe et al.; [0153] – [0157]; “The neural network 203 evaluates the input data set… produces an output control signal… to control refrigerant charge level, compressor speed, and refrigerant oil concentration in evaporator… in order to optimize energy efficiency.”).
Zugibe et al. does not explicitly disclose:
(1) the sensor(s) being a thermistor, per se;
(2) modifying the signal received from the thermistor to generate a modified signal for achieving the optimal temperature; or
(3) that the control module receives a modified thermistor signal as its input.
Chambers discloses these features by disclosing:
(1) “The leg 38 includes a temperature sensitive variable impedance 54, preferably a thermistor having a negative temperature coefficient of impedance variation.”; (See C4 L50-55); and
(2) “The bridge circuit 14 is temperature sensitive and may be adjusted to balanced conditions at preselected environmental temperatures. Changes in environmental temperature unbalance the bridge causing the rectifier to conduct and thereby effect operation of a furnace or air conditioner.”; (See C3 L10-20); this is interpreted to correspond to the thermistor signal being altered by the bridge circuit into a modified control signal which is then used by the rectifier to operate the heating/cooling system; and
(3) “The firing of the rectifier 12 causes the voltage across the portion 40 of the secondary winding 42 to able applied directly across the controlled heat source 16… thereby energizing the controlled heat source to either heat or cool the environment to return the environment to the predetermined temperature.”; (See C3 L30-40).
It would have obvious to one of ordinary skill in the art at the time the invention was made to have incorporated the teachings of Chambers into Zugibe et al. for the purpose of improving the accuracy and stability of the temperature regulation control loop while simultaneously helping to avoid overshoot and lag in order to aid in maintaining the desired performance with less waste energy and reduced risk of system faults.
As per claim 2, Zugibe et al.’s combined system further discloses that the control module controls the refrigeration equipment by determining by the control module that the current temperature is lower than the optimal temperature, and responsive to determining by the control module that the current temperature is lower than the optimal temperature, increasing the temperature of the refrigeration equipment (e.g., See Zugibe et al.; [0153] – [0157]; “The neural network 203 evaluates the input data set… produces an output control signal… to control refrigerant charge level, compressor speed… in order to optimize energy efficiency”; interpreted to implicitly include increasing the temperature when the current is less than optima, such as when adjusting the compressor/condenser to warm).
As per claim 3, Zugibe et al.’s combined system further discloses that the control module controls the refrigeration equipment by determining, by the control module, that the current temperature is higher than the optimal temperature, and responsive to determining that the current temperature is lower than the optimal temperature, reducing the temperature of the refrigeration equipment (e.g., See Zugibe et al.; [0153] – [0157]; “wherein the control module… determining… current temperature is higher… and… reducing the temperature…”).
As per claim 4, Zugibe et al.’s combined system further discloses that the optimal temperature is determined to minimize a power consumption of the refrigeration equipment (e.g., See Zugibe et al.; [0150] – [0154]; “Power meter 101 … produces output representative of efficiency … an optimum operating regime may thereafter be defined … Proper maintenance, to achieve a high optimum efficiency, may be quite cost effective.”).
As per claim 5, Zugibe et al.’s combined system further discloses that the optimal temperature is determined to minimize a power consumption of a plurality of equipment of a facility including the refrigeration equipment (e.g., See Zugibe et al.; [0153] – [0156]; “These variables include… system power consumption (kWatt-hours) … thermodynamic parameters… ambient pressure and temperature”; also see [0134] – [0135]; discuss optimization in the context of a facility-scale chiller system).
As per claim 6, Zugibe et al.’s combined system further discloses that the optimal temperature is determined by executing a machine learning model trained to output a score indicating energy demand of a facility including the refrigeration equipment (e.g. See Zugibe et al.; [0153]; “The variables are fed into the adaptive control 200 employing a nonlinear model ... based on neural network 203 technology … The neural network evaluates the input data set … produces an output control signal.”).
As per claim 7, Zugibe et al.’s combined system further discloses that the machine learning model is configured to receive as input, feature comprising environmental attributes associated with the refrigeration equipment (e.g., See [0156]; “ambient pressure and temperature”).
As per claim 8, this claim is substantially the same in scope as claim 1, except claim 8 is drafted in a computer readable format; accordingly, the rational as already set forth above with respect to the rejection of claim 1, is applied herein, and claim 8 is unpatentable for at least the same reasons that claim 1 was determined to be unpatentable.
As per claim 9, the rational as set forth above with respect to the rejection of claim 2, is applied herein. It is noted that claim 9 discusses “attribute” while claim 2 discusses “temperature”.
As per claim 10, the rational as set forth above with respect to the rejection of claim 3, is applied herein. It is noted that claim 10 discusses “attribute” and “decreases attribute value” while claim 3 discusses “temperature” and “reduces temperature”.
As per claim 11, the rational as set forth above with respect to the rejection of claim 5, is applied herein.
As per claim 12, Zugibe et al.’s combined system further discloses that modifying the signal is performed by changing a value of a resistor associated with the signal (e.g., See Chambers; C3 L65 – C4 L10; “The bridge circuit 14 is temperature sensitive … The firing of the rectifier 12 causes the voltage … applied directly across the controlled heat source 16”; also see C5 L40-55, which describes the placement of impedance elements (e.g., resistors, thermistor) that modifies the control signal.”).
As per claim 13, Zugibe et al.’s combined system adequately discloses calibrating the sensor based on the signal generated by a sensor to determine the optimal target attribute value of the signal for the sensor (e.g., See Zugibe et al.; [0126] – [0133]; “Reoptimization … prior measurements may be used to redefine the desired operating regime”; interpreted to correspond to calibration/re-optimization of sensor-derived signals, per se).
As per claim 14, Zugibe et al.’s combined system adequately discloses that the equipment is one of a refrigeration equipment, a heating equipment, or an air-conditioning equipment (e.g., See Zugibe et al.; [0134]; The refrigeration systems or chillers may be large industrial devices, for example 3500 ton devices which draw 4160V at 500 A max (2MW).”; also see Chambers; Abstract; “A temperature control system … and cause the rectifier to conduct and thereby effect operation of a furnace or an air conditioner.”).
As per claim 15, this claim is substantially the same in scope as claim 1, except that claim 15 is drafted in a system form with processors and memory recited. Accordingly, the same combination of Zugibe et al. and Chambers, that was applied with respect to claim 1, from above, is also applied herein and therefore claim 15 is rendered unpatentable for at least the same reasons as already outlined above with respect to the rejection of claim 1.
As per claim 16, the rational as applied with respect to the rejection of claim 2, from above, is applied herein.
As per claim 17, the rational as applied with respect to the rejection of claim 3, from above, is applied herein.
As per claim 18, the rational as applied with respect to the rejection of claim 4, from above, is applied herein.
As per claim 19, the rational as applied with respect to the rejection of claim 5, from above, is applied herein.
As per claim 20, the rational as applied with respect to the rejection of claims 6 and 7, from above, are applied herein.
References Considered but Not Relied Upon
(1) US 3,872,685 A, which discloses thermistor and bridge circuit to control evaporator temperature via signal amplification and adjustable set point shifting;
(2) US 4,750,454 A, which discloses dual control of humidity and temperature in AC systems for rigid environmental control of laboratory air quality;
(3) US 7,895,851 B2, which discloses the utilization of temperature and humidity sensors, and algorithm for controlling and maintaining relative humidity inside a domestic refrigerator;
(4) US 2002/0157407 A1, which discloses a sensor base controller that controls refrigerant and hot gas valves based on sensed temperature in order to accurately control refrigeration flow; and
(5) US 5519644 A, which discloses a multi loop controller with continuous calibration using resistant temperature detector (RTD) sensors.
Conclusion
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/RONALD D HARTMAN JR/Primary Patent Examiner, Art Unit 2119 September 19, 2025
/RDH/