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 .
Election/Restrictions
Claims 11-25 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 11/18/25.
Claim Rejections - 35 USC § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claim(s) 1-10, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1; “or more processors” appears to present grammatical errors and otherwise deemed to denote “one or more processors”
Regarding Claim 2; “at least one processor to determine a temperature” is unclear; whereas the assertion does not denote any manner by which a processor determines temperature since i.e. associated signals from temperature sensors are not asserted and/or otherwise determination by using i.e. training logic and storage etc. Regarding Claim(s) 2-3, 6-7, and 9-10; “atleast one flow controller to enable flow” or “at least one controller to enable” is unclear; whereas the terms are herein deemed to read on more than one plausible claim construction which read on different incentive structures including mechanical and/or electrical structures or attributes to enable flow and accomplish the assertion. Regarding Clam 7; “to prevent flow” in line 3 and “to enable flow” in line 4 and is unclear; whereas the claim does not denote if i.e. the respective functions occur at different times using the same controller to selectively enable or prevent flow or otherwise if i.e. the functions simultaneously occur at the same time using different controllers and appears to require more than one controller to allow for both functions at the same time. Regarding Clam 9; “to enable flow” in line 3 and “to prevent flow” in line 4 is unclear; whereas the claim does not denote if i.e. the respective functions occur at different times using the same controller to selectively enable or prevent flow or otherwise if i.e. the functions simultaneously occur at the same time using different controllers and appears to require more than one controller to allow for both functions at the same time.
Claim Rejections - 35 USC § 103
4. 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.
5. Claim(s) 1-3, and 5-10, and 26-30, is/are rejected under 35 U.S.C. 103 as being unpatentable over (Shao 20210084797) in view of (Ozonat 2020/0348993).
Regarding Claim 1; Shao discloses a system (a system--as constituted by control logic of a rack management unit (RMU) of a liquid cooled electronic rack utilized to optimize performance and utilizing machine learning to evaluate and indicate computing performance —as set forth by para. 0014; and further wherein the RMU manages operations of a CDU of the rack to determine optimal liquid pump speed—as set forth by para. 0015), comprising: processor (as set forth by para. 0032--whereas the optimal control logic of the RMU may be implemented as a combination of software and hardware including an application specific integrated circuit (ASIC) which constitutes a processor) to use a machine learning model suggested to control a liquid-to-liquid heat exchanger (as already set forth by wherein the RMU utilizes machine learning to manage the CDU which includes a liquid-to-liquid heat exchanger-211-Fig. 2—para. 0024 and control the pump speed of the liquid pump—as set forth by para, 0015) based, at least in part, on sensor data associated with a coolant to cool one integrated circuit (wherein the temperature of the processor can be determined using a third function based on power consumption of the processor and the pump speed and further based on a liquid temperature of a cooling liquid—as set forth by para. 0017; and further set forth by para. 0061--wherein the RMU performs a process, wherein processing logic monitors the temperature the temperature of the processor, the temperature of the cooling liquid, and the speed of the liquid pump using a variety of sensors). Except, Shao does not explicitly disclose the machine learning model comprises a neural network. However, Ozonat discloses a machine learning model comprises a neural network. (as set forth by para.’s 0053-0055—whereas a machine learning algorithm such as a neural network to train and build an optimization function for a data center and a CDU-103 and as further set forth by para.’s 0024-0025, wherein the CDU includes a liquid-to-liquid heat exchanger—as depicted by Fig. 1 and employs control valves, isolation valves and pumps to increase/decrease speed and control coolant flow and flow rate of heated liquid from the heat exchanger, and executing instructions to cause opening and closing of the valves—as set forth by para. 0028), and thus it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the machine learning model as a neural network since it was known in the art that function may be optimized based on neural network(s) providing training knowledge and direct or indirect relationships acquired between varying system sensors with reduced burden, as above-mentioned by para.’s 0053-0055 and employing CDU valves to control coolant flow so as to optimize computing performance, as desired by Shao, as set forth by para. 0014.
Regarding Claim 2; Shao discloses the already modified system of claim 1, wherein the processor are further to: determine, based on sensor data associated with the coolant, a temperature associated with the coolant (as already set forth by para.’s 0017 and 0061), and cause at least one flow controller to adjust flow rate of the coolant through the liquid-to-liquid heat exchanger (as already set forth—whereas the CDU, as modified includes valves to control flow rate).
Regarding Claim 3; Shao discloses the already modified system of claim 1, further comprising: at least one flow controller associated with the liquid-to-liquid heat exchanger, the at least one flow controller to be enabled based in part on a cooling requirement for the coolant (whereas the already modified system, wherein Ozonat—comprises the CDU-103 including valves associated with the heat exchanger therein—as depicted by Fig. 1 and set forth by para.’s 0024-0025 which further discloses the CDU controls the temperature and flow of coolant to achieve desired cooling; based in part on failure or anomalies indicated by the neural network—as further set forth by para.’s 0080-0088).
Regarding Claim 5; Shao discloses the already modified system of claim 1, wherein, to control the liquid-to-liquid heat exchanger, the processor are further to use the neural network to: determine a change in a coolant state based in part on the sensor data (as constituted via the control of coolant flow and the flow rate--as already set forth).
Regarding Claim 6; Shao discloses the already modified system of claim 5, wherein, to control the liquid-to-liquid heat exchanger, the processor are further to use the neural network to cause, based on the change in coolant state, at least one flow controller to change a flow of the coolant to change an amount of heat to be removed from the one or more integrated circuits (as constituted by the already modified system, wherein Ozonat including the CDU-103 comprising valves associated with the heat exchanger therein—as depicted by Fig. 1 and set forth by para.’s 0024-0025 which further discloses the CDU controls the temperature and flow of coolant to achieve desired cooling; based in part on failure or anomalies indicated by the neural network—as further set forth by para.’s 0080-0088).
Regarding Claim 7; Shao discloses the already modified system of claim 1, wherein, to control the liquid-to-liquid heat exchanger, the processor are further to use the neural network (whereas the processor using the modified neural network is already modified), to cause at least one controller to enable flow of the coolant through the liquid-to-liquid heat exchanger and to prevent flow of the coolant to a secondary cooling loop (wherein Ozonat—para. 0024-0025 already modifies the CDU to employ valves including control valves or isolation valves including rack side valves and control coolant flow associated with the heat exchanger in the CDU, and the CDU executing instructions to cause opening and closing of the valves—as further set forth by para. 0028, wherein open valves on the rack side constitutes enabling coolant to/from the rack and through CDU heat exchanger; and otherwise constitutes closing the valves to prevent coolant between a second cooling loop defined between the rack and the CDU heat exchanger).
Regarding Claim 8; Shao discloses the already modified system of claim 1, further comprising: association of the liquid-to-liquid heat exchanger with a rear door of a rack (as set forth by para. 0023—whereas a rack 200 includes a backdoor at 202 in-part associated by facing the heat exchanger-211—as depicted by Fig. 2), except, explicitly disclosing a latching mechanism to enable the association. However, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the rack with a latching mechanism to enable the association since it was known in the art, as set forth by para. 0023 that the CDU 201 which includes the heat exchanger is arranged at a particular position or may implement other arrangements or configurations, and thus a latching mechanism for a door is commonly defined by a fastening structure including a hinge to securely open and close a door, and thus enabling access to the heat exchanger and/or liquid coupling thereof for easy replacement or repair. Note: a latching mechanism is not asserted as latching any particular structure or latching between any two structures including rack, door or heat exchanger—if so intended.
Regarding Claim 9; Shao discloses the already modified system of claim 1, wherein, to control the liquid-to-liquid heat exchanger, the processor are to further use the more neural network cause at least one flow controller to prevent flow of the coolant through the liquid-to-liquid heat exchanger and to enable flow of the coolant to a secondary cooling loop (whereas the processor using the modified neural network is already set forth, wherein Ozonat—para. 0024-0025 already discloses the CDU comprising valves on the facility side and the rack side, wherein the valves may be regulated by partially or fully closing or opening (wherein Ozonat—para. 0024-0025 already modifies the CDU to employ valves including control valves or isolation valves including rack side valves and control coolant flow associated with the heat exchanger in the CDU, and the CDU executing instructions to cause opening and closing of the valves—as further set forth by para. 0028, wherein open valves on the rack side constitutes enabling coolant to/from the rack and through CDU heat exchanger; and otherwise constitutes closing the valves to prevent coolant between the rack and the CDU heat exchanger).
Regarding Claim 10; Shao discloses the system of claim 1, wherein, to control the liquid-to-liquid heat exchanger, the processor are further to use the neural network to control a flow controller to enable at least one of a first mode to provide cooling from the liquid-to-liquid heat exchanger (wherein Ozonat—para. 0024-0025 already modifies the CDU to employ valves including control valves or isolation valves including rack side valves and control coolant flow associated with the heat exchanger in the CDU, and the CDU executing instructions to cause opening and closing of the valves—as further set forth by para. 0028, wherein open valves on the rack side enables a flow of coolant to/from the rack and through CDU heat exchanger and constitutes a first mode).
Regarding Claims 26-30; the method steps are necessitated by the already modified system of Shao in view of Ozonat.
Allowable Subject Matter
6. Claims 4, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding Claim 4; the system of claim 1, further comprising: a cold plate associated with the one or more integrated circuits and having first ports for a first portion of microchannels to support secondary coolant distinctly from second ports for a second portion of the microchannels to support local coolant.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190373776 A1
GAO; Tianyi
Fig.’s 2, and 4A-4B
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to COURTNEY SMITH whose telephone number is (571)272-9094. The examiner can normally be reached M-F 9-5p.
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/COURTNEY L SMITH/Primary Examiner, Art Unit 2835