DETAILED ACTION
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.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
U.S. Patent Application Publication No. 2018/0204116 (Evans).
Claim 1:
The cited prior art describes a heat flow control method, for a data center cooling system, the heat flow control method comprising: (Evans: “This specification describes technologies for data center optimization. These technologies generally involve methods and systems for applying machine learning algorithms to improve data center efficiency.” Paragraph 0003; “In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.” Paragraph 0004; “Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit.” Paragraph 0070; “The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output.” Paragraph 0069)
(a) determining a plurality of features corresponding to a current scene of the data center cooling system at a first time point; and (Evans: see the state data 140 from the data center 104 as illustrated in figure 1; “The efficiency management system (100) can take in, as input, state data (140) representing the current state of the data center (104). This state data (140) can come from sensor readings of sensors in the data center (104) and operating scenarios within the data center (104). The state data may include data such as temperatures, power, pump speeds, and set points.” Paragraph 0020)
(b) determining a plurality of cooling parameters at a second time point according to the plurality of features; (Evans: see the generate an efficiency score 220 and the select new values 230 as illustrated in figure 2; “For each data center setting slate in the set of data center setting slates, the system processes the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models to generate an efficiency score for each machine learning model (220).” Paragraph 0048; “The system selects, based on the efficiency scores for the data center setting slates in the set of data center setting slates, new values for the data center settings (230).” Paragraph 0057)
wherein the data center cooling system utilizes the plurality of cooling parameters to control heat flow at the second time point; (Evans: see the data center settings 120 to the control system 102 as illustrated in figure 1; “Once the efficiency management system (100) determines the data center settings (120) that will make the data center (104) more efficient, the efficiency management system (100) provides the updated data center settings (120) to the control system (102). The control system (102) uses the updated data center settings (120) to set the data center (104) values. For example, if the efficiency management system (100) determines that an additional cooling tower should be turned on in the data center (104), the efficiency management system (100) can either provide the updated data center settings (120) to a user who updates the settings or to the control system (102), which automatically adopts the settings without user interaction. The control system (102) can send the signal to the data center to increase the number of cooling towers that are powered on and functioning in the data center (104).” Paragraph 0022)
wherein the second time point lags the first time point. (Evans: “Each constraint model (112A-112N) is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting.” Paragraph 0032; “Long-term power usage efficiency may be for time durations of thirty minutes, one hour, or longer from the time the data center was in the input state whereas short term power usage efficiency focuses on a short time after the data center was in the input state, e.g., immediately after or five seconds after, the data center was in the input state.” Paragraph 0042)
Claim 2:
The cited prior art describes the heat flow control method of claim 1, wherein the plurality of features comprise a cold air temperature, a cold air velocity, a server inlet temperature, a server outlet temperature, a server load power, a plurality of primary component temperatures, a server fan speed or a server amount. (Evans: “The state data may include data such as temperatures, power, pump speeds, and set points.” Paragraph 0020; “Example data includes: power usage across various parts of a data center such as the server floor, cooling system, networking room, and individual fans; temperature sensors across the data center such as in the water cooling system, on the server floor, and in the chiller; water and/or air speed in various parts of the data center such as the differential pressure of the server floor air and the differential pressure in the water cooling system; fan and/or pump speeds such as the cooling tower fan speeds and the process water pump speed; weather, i.e., the outside air temperature, humidity, and/or air pressure, and forecasts of future weather; and equipment status, i.e., whether the chiller is running, how many cooling towers are on and how many pumps are running.” Paragraph 0059)
Claim 3:
The cited prior art describes the heat flow control method of claim 1, wherein the step (b) further comprises: utilizing a deep learning method to perform a decision-fuse procedure for the plurality of features to generate the plurality of cooling parameters. (Evans: see the generate an efficiency score 220 and the select new values 230 as illustrated in figure 2; “For each data center setting slate in the set of data center setting slates, the system processes the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models to generate an efficiency score for each machine learning model (220).” Paragraph 0048; “The system selects, based on the efficiency scores for the data center setting slates in the set of data center setting slates, new values for the data center settings (230).” Paragraph 0057; “In some implementations, each machine learning model (132A-132N) is a neural network, e.g., a deep neural network, that the efficiency management system (100) can train to produce an efficiency score.” Paragraph 0027; “Each constraint model (112A-112N) is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting.” Paragraph 0032)
Claim 4:
The cited prior art describes the heat flow control method of claim 3, wherein the deep learning method adopts at least one of a deep neural network (DNN), a deep belief network (DBN), a convolutional neural network (CNN) and a convolutional deep belief network (CDBN). (Evans: “In some implementations, each machine learning model (132A-132N) is a neural network, e.g., a deep neural network, that the efficiency management system (100) can train to produce an efficiency score.” Paragraph 0027; “Each constraint model (112A-112N) is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting.” Paragraph 0032)
Claim 5:
The cited prior art describes the heat flow control method of claim 1, wherein the plurality of cooling parameters comprise a predicted server inlet temperature and a predicted server fan speed. (Evans: “Other examples of slate settings that impact efficiency of the data center (104) include: potential power usage across various parts of the data center; certain temperature settings across the data center; a given water pressure; specific fan or pump speeds; and a number and type of the running data center equipment such as cooling towers and water pumps.” Paragraph 0037)
Claim 6:
Claim 6 is substantially similar to claim 1 and is rejected based on the same reasons and rationale as described herein.
6. A heat flow control system, for a data center cooling system, the heat flow control system comprising:
a processor; and
a memory, coupled to the processor, stores a programing code to indicate the processor to perform a transmission parameter decision method, wherein the transmission parameter decision method comprises:
(a) determining a plurality of features corresponding to a current scene of the data center cooling system at a first time point; and
(b) determining a plurality of cooling parameters at a second time point according to the plurality of features;
wherein the data center cooling system utilizes the plurality of cooling parameters to control heat flow at the second time point;
wherein the second time point lags the first time point.
Claim 7:
Claim 7 is substantially similar to claim 2 and is rejected based on the same reasons and rationale as described herein.
7. The heat flow control system of claim 6, wherein the plurality of features comprise a cold air temperature, a cold air velocity, a server inlet temperature, a server outlet temperature, a server load power, a plurality of primary component temperatures, a server fan speed or a server amount.
Claim 8:
Claim 8 is substantially similar to claim 3 and is rejected based on the same reasons and rationale as described herein.
8. The heat flow control system of claim 6, wherein the step (b) further comprises: utilizing a deep learning method to perform a decision-fuse procedure for the plurality of features to generate the plurality of cooling parameters.
Claim 9:
Claim 9 is substantially similar to claim 4 and is rejected based on the same reasons and rationale as described herein.
9. The heat flow control system of claim 8, wherein the deep learning method adopts at least one of a deep neural network (DNN), a deep belief network (DBN), a convolutional neural network (CNN) and a convolutional deep belief network (CDBN).
Claim 10:
Claim 10 is substantially similar to claim 5 and is rejected based on the same reasons and rationale as described herein.
10. The heat flow control system of claim 6, wherein the plurality of cooling parameters comprise a predicted server inlet temperature and a predicted server fan speed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2025/0180408 describes managing thermal anomalies using a Bayesian network.
U.S. Patent Application Publication No. 2024/0129380 describes data center control using machine learning.
U.S. Patent Application Publication No. 2012/0197828 describes energy saving control for a data center.
U.S. Patent Application Publication No. 2019/0104642 describes controlling data center cooling using a machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E EVERETT whose telephone number is (571)272-2851. The examiner can normally be reached Monday-Friday 8:00 am to 5:00 pm (Pacific).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Christopher E. Everett/Primary Examiner, Art Unit 2117
Prosecution Insights