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
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. claims 80-101, drawn to a system and method for cooling a heat source by submerging the heat source in a liquid, classified in H05K 7/*;
II. 102-108, drawn to a method of predicting an overheating event based on a trained model, classified in G06F 1/*.
The inventions are independent or distinct, each from the other because:
Predicting using trained model in invention II is not necessary needed for invention I.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
Applicant’s election without traverse of Group II in the reply filed on 5/1/2026 is acknowledged.
Status of Claims
Claims 1-101 and 106 are canceled. Claims 109-121 are newly added. Claims 102-105 and 107-121 are pending.
Claim Objections
In claim 105, the phrase “comprises” should be changed to ““comprise[[s]]”, to correct the grammatical error.
In claim 121, the recited limitation should be changed to “the group consisting of increasing a liquid flow rate, decreasing a liquid temperature, and enabling a two-phase cooling mode”, to correct the grammatical errors.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 103, 104, 110 and 112 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 pre-AIA the applicant regards as the invention.
Claims 103 and 104 recite a limitation “said overheat event” which lacks sufficient antecedent basis. For continuing examination purpose, the limitation has been construed as “said overheating event”.
Claims 110 and 112 recite a limitation “said one or more parameters” which lacks sufficient antecedent basis. For continuing examination purpose, the limitation has been construed as “said plurality of parameters”.
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 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.
(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.
Claim 102, 104, 105, 109, 110, 113, 114, 117, 118, 120 and 121 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over ZHAO (WO 2021/042339 A1, hereinafter as “ZHAO”).
Regarding claim 102, ZHAO teaches:
A method for predicting an overheating event to aid in cooling a heat source ([0005]), comprising:
(a) receiving a plurality of parameters associated with a plurality of electrical components of an electrical network from a plurality of sensors, wherein one of said plurality of sensors is a temperature sensor ([0077]: a plurality of sensors including temperature sensor measure “actual power information” of devices and temperatures of the external atmosphere and coolant operating temperature, wherein these measured data are input into “overheating risk prediction model” to predict a possible overheating event); and
(b) computer processing said plurality of parameters with a predictive model to generate an output indicative of said overheating event ([0077]), wherein said predictive model is trained on a training dataset comprising a plurality of historical data of said plurality of parameters across different time points ([0069]), and wherein said plurality of historical data is labeled as originating or not originating from an electrical component that has undergone an overheating event ([0070]: historical sample data is marked/labeled as passing or not passing overheating temperature threshold, i.e., originating or not originating from a component that has an overheating event).
ZHAO teaches specifically (underlines are added by Examiner for emphasis):
[0005] This application provides a heat dissipation control method, comprising: whenever a
heat dissipation control condition is triggered, acquiring the actual power information of
at least one device to be cooled within a designated space; inputting the actual power
information of the at least one device to be cooled into an overheating risk prediction
model to obtain the probability of overheating risk occurring in the designated space
under at least one candidate cooling parameter; determining a target cooling parameter
based on the probability of overheating risk occurring in the designated space under at
least one candidate cooling parameter; and controlling a cooling system to dissipate
heat from the at least one device to be cooled within the designated space based on the
target cooling parameter.
[0069] Optionally, multiple sets of labeled sample data can be generated by combining sample
generation methods based on real data and CFD simulation, including: generating at
least one set of labeled historical sample data based on the historical power information
of at least one device to be cooled and the historical cooling parameters of the cooling
system; and generating at least one set of labeled simulated sample data by using a
CFD model to perform simulation calculations between power information and cooling
parameters.
[0070] Furthermore, the process of generating at least one set of labeled historical sample data
includes: acquiring at least one set of unlabeled historical sample data, each set of
unlabeled historical sample data including historical power information of at least one
device to be cooled and historical cooling parameters of the cooling system within the
same historical moment or historical period; for each set of unlabeled historical sample
data, marking whether the computer room has an overheating risk based on the
temperature of the internal components of at least one device to be cooled within the
corresponding historical moment or historical period and the overheating temperature
threshold corresponding to the internal components, thereby obtaining at least one set
of labeled historical sample data.
[0077] In one embodiment, the structure of an overheating risk prediction model is assumed to
be as shown in Figure 2b. The structure of the thermal risk prediction model shown in Figure 2b is merely illustrative and is not intended to limit the scope of the model. As shown in Figure 2b, the input data supported by this model includes: actual power information of devices 1 to n, external atmospheric temperature, and a candidate cooling parameter (such as operating temperature). The output is the probability of overheating risk in the computer room under the candidate cooling parameter. Where n is a positive integer. With multiple candidate cooling parameters, the probability of overheating risk in the computer room under the candidate cooling parameters can be obtained through the thermal risk prediction model shown in Figure 2b. ….
Regarding claim 104, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said predictive model is a multi-class predictive model, and wherein said output comprises a probability distribution over a plurality of levels ([0077]: “…, after the thermal risk prediction model shown in Figure 2b, four probability values are obtained, which are 0.76, 0.83, 0.89 and 0.92 respectively”) or imminency of said overheating event.
Regarding claim 105, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said plurality of sensors comprise electrical characteristic sensors ([0077]: “As shown in Figure 2b, the input data supported by this model includes: actual power information of devices 1 to n,…”. This teaches the plurality of sensors comprise electrical sensors measuring electrical power of devices).
Regarding claim 109, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said training dataset comprises a plurality of historical data on thermal measurements received from said temperature sensor and electrical characteristic measurements from said electrical characteristic sensors ([0070]: “the process of generating at least one set of labeled historical sample data includes: acquiring at least one set of unlabeled historical sample data, each set of unlabeled historical sample data including historical power information of at least one device to be cooled and historical cooling parameters of the cooling system within the same historical moment or historical period; for each set of unlabeled historical sample data, marking whether the computer room has an overheating risk based on the temperature of the internal components of at least one device to be cooled within the corresponding historical moment or historical period and the overheating temperature threshold corresponding to the internal components, thereby obtaining at least one set of labeled historical sample data”).
Regarding claim 110, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said one or more plurality of parameters comprise voltage, electric current, electrical resistance, electrical reactance, electrical charge, partial discharge, electrical power ([0077]: “the input data supported by this model includes: actual power information of devices 1 to n”), magnetic flux, or magnetic field.
Regarding claim 113, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said computer processing is in real-time ([0039]: “the heat dissipation control device 103 can monitor the total power change of at least one device 101 to be cooled in the computer room in real time, and use the total power change of at least one device to be cooled as the heat dissipation control condition”).
Regarding claim 114, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said predictive model is a machine learning model ([0008]: “train a deep neural network model using the multiple sets of labeled sample data to obtain an overheating risk prediction model”).
Regarding claim 117, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said plurality of electrical components comprises a central processing unit, a graphics processing unit, a circuit board, a chipset, a memory driver, or a battery ([0030]: “at least one device 101 to be cooled may include, but is not limited to, at least one of the following device forms: rack equipment, server equipment, computer equipment, printer, hub, power supply equipment, storage equipment, and network switching equipment, etc. Server equipment can include, but is not limited to: conventional servers, server arrays, or cloud servers. Power supply equipment can be storage battery equipment, dry cell battery equipment, or uninterruptible power supply (UPS), etc.”).
Regarding claim 118, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said predictive model comprises a pre-trained predictive model ([0014]: “an overheating risk prediction model is pretrained”).
Regarding claim 120, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
using a machine learning model to generate a set of commands that provide corrective actions in response to said output indicative of said overheating event ([0009]: “determine a target cooling parameter based on the probability of the data center system experiencing overheating risk under at least one candidate cooling parameter; and control the cooling system to dissipate heat from the at least one device to be cooled based on the target cooling parameter”; and [0039]: “whenever the total power change of at least one device 101 to be cooled exceeds the first amplitude threshold, the heat dissipation control device 103 obtains the actual power information of at least one device 101 to be cooled; combined with the overheating risk prediction model, it continuously adjusts the cooling parameters of the cooling system 102 to dynamically dissipate heat from at least one device 101 to be cooled in the computer room by controlling the cooling system 102”).
Regarding claim 121, ZHAO teach(es) all the limitations of its base claim from which the claim depends.
ZHAO further teaches:
said corrective actions are selected from the group consisting of increasing a liquid flow rate, decreasing a liquid temperature, and enabling a two-phase cooling mode ([0051]: “in a water-cooling system, liquid coolant can be supplied to the computer room system 100 through pipes or other liquid carriers. The liquid coolant can be cold water or liquid sodium metal, etc. The liquid coolant flows within the computer room system 100 or flows around the equipment to be cooled within the computer room system 100, thereby removing the heat inside the computer room system 100 and achieving the purpose of heat dissipation. Optionally, the water cooling system of this embodiment includes, but is not limited to, the following operating parameters: outlet water temperature, return water temperature, water flow rate, and water flow volume, which affect the heat dissipation performance of the water cooling system. The operating parameters of the water cooling system can be used as the target cooling parameters in this embodiment. For the water cooling system, the target cooling parameters may include, but are not limited to, at least one of the following: water outlet temperature, water return temperature, water flow rate, and water flow volume”).
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, 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.
Claim 103 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of MASAKAWA (US 20180181114 A1, hereinafter as “MASAKAWA”).
Regarding claim 103, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach said predictive model is a binary predictive model, and wherein said output is a binary output that indicates whether one of said plurality of electrical components will or will not have said overheating event.
However, MASAKAWA teaches in an analogous art:
predictive model is a binary predictive model, and wherein said output is a binary output that indicates whether one of said plurality of components will or will not have said overheating event ([0014]: “An overheat prediction device (for example, an overheat prediction device 30 to be described later) according to the present invention includes: overheat prediction means (for example, an overheat prediction unit 31 to be described later) for predicting whether the spindle motor overheats or not from the cutting processing conditions and the present temperature of the spindle motor on the basis of the learning model constructed by the learning model construction device according to any one of (1) to (3)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of MASAKAWA, to make the method wherein said predictive model is a binary predictive model, and wherein said output is a binary output that indicates whether one of said plurality of electrical components will or will not have said overheating event. One of ordinary skill in the art would have been motivated to do this modification since it can help prevent overheating.
Claim 107 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Sakai (US 20190222112 A1, hereinafter as “Sakai”).
Regarding claim 107, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach said training dataset comprises topological relationships between said plurality of electrical components.
However, Sakai teaches in an analogous art:
overheating depends on topological relationships between said plurality of electrical components ([0043]: “Distances between the thermistor 126 and positions where the SBD 107 for rectification as a heat generation source and the resistor 141 for the snubber circuit are mounted on the circuit board 157 have effects on a threshold temperature for the overheat protection circuit 136. Therefore, the threshold temperature for the overheat protection circuit 136 to detect the overheating state is determined taking into account the dispersion of detection temperature of the thermistor 126 in the product model A described below”).
Since topology of components affect the overheating detection, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Sakai, to make the method wherein said training dataset comprises topological relationships between said plurality of electrical components. One of ordinary skill in the art would have been motivated to do this modification since it can help avoid “false detection of the overheating state”, as Sakai suggests in [0004].
Claim 108 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Mohn (US 20120161405 A1, hereinafter as “Mohn”).
Regarding claim 108, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach said temperature sensor is an infrared thermometer.
However, Mohn teaches in an analogous art:
temperature sensor is an infrared thermometer ([0221]: “An alternative to using one or more chuck-mounted RTDs is to use a sensor capable of remote measurement, such as a Lumasense infrared thermometer”).
Since topology of components affect the overheating detection, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Mohn, to make the method wherein said temperature sensor is an infrared thermometer. One of ordinary skill in the art would have been motivated to do this modification since it is “capable of remote measurement”, as Mohn suggests in [0221].
Claims 111 and 112 are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Youngs (US 20150380782 A1, hereinafter as “Youngs”).
Regarding claim 111, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach an electrical component of said plurality of electrical components is contained within a sealed container.
However, Youngs teaches in an analogous art:
an electrical component is contained within a sealed container (FIG. 1 and [0020]: battery 114 is contained within sealed container 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Youngs, to make the method wherein an electrical component of said plurality of electrical components is contained within a sealed container. One of ordinary skill in the art would have been motivated to do this modification since it can help make “any heat energy generated within the container [] can be rapidly transferred from the inside” to outside, as Youngs suggests in [0017].
Regarding claim 112, ZHAO-Youngs teach(es) all the limitations of its base claim from which the claim depends.
Youngs further teaches:
measure a pressure within said sealed container (FIG. 1 and [0027]: “Located within the container 102 are one or more sensors 138. Although FIG. 1 depicts a single sensor 138 for illustration purposes, in practice the energy storage system 100 could include any number of sensors 138 positioned in a variety of configurations within the container 102. The sensors 138 can be connected to the battery cell arrays 114 or otherwise placed within the container 102. The sensors 138 can be any of a number of sensors for measuring physical parameters within the container 102 such as for example the temperature of battery cells, the temperature of the walls 104, or the temperature of the fluid. Additionally, the sensors 138 could be pressure sensors, liquid level sensors, and battery cell voltage sensors”. This teaches to use sensor 138 to measure a pressure within sealed container 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified ZHAO based on the teaching of Youngs, to make the method wherein an electrical component of said plurality of electrical components is contained within a sealed container. One of ordinary skill in the art would have been motivated to do this modification since it can help make that “In the case of an increase of internal pressure beyond a target threshold, the pressure relief valve allows gas or fluid to be released from within the container and prevents cracking or other failure of the container 102”, as Youngs suggests in [0017].
Claim 115 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Kodihalli (US 11393024 B1, hereinafter as “Kodihalli”).
Regarding claim 115, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach said plurality of parameters are stored in a graph database.
However, Kodihalli teaches in an analogous art:
plurality of parameters are stored in a graph database ([Col. 16 Lines10-15]: “The information may preferably relate to parameters, details, and other types of data stored in the knowledge graph database 312”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Kodihalli, to make the method wherein said plurality of parameters are stored in a graph database. One of ordinary skill in the art would have been motivated to do this modification since it can help store parameters efficiently.
Claim 116 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Karri (US 20220100185 A1, hereinafter as “Karri”).
Regarding claim 116, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach said plurality of parameters comprises a digital twin of said electrical components, and wherein said method further comprises querying said digital twin to generate said output indicative of said overheating event.
However, Karri teaches in an analogous art:
plurality of parameters comprises a digital twin of industrial equipment, and querying said digital twin to generate output indicative of predicted event ([0039]: “the computing system (e.g., server computer 200 of FIG. 1 or the like) can predict one or more events related to areas within the industrial location based, at least in part, on the digital replica (e.g., digital twin) model simulations of the equipment/surroundings”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Karri, to make the method wherein said plurality of parameters comprises a digital twin of said electrical components, and wherein said method further comprises querying said digital twin to generate said output indicative of said overheating event. One of ordinary skill in the art would have been motivated to do this modification since it can help “enable simulation, testing, modeling, analysis, and/or monitoring based on data generated by and/or collected from the digital twin”, as Karri suggests in [0002].
Claim 119 is rejected under 35 U.S.C. 103 as being unpatentable over ZHAO in view of Greening (US 20160006272 A1, hereinafter as “Greening”).
Regarding claim 119, ZHAO teach(es) all the limitations of its base claim from which the claim depends, but does not teach a sensor of said plurality of sensors is embedded within said plurality of electrical components.
However, Greening teaches in an analogous art:
a sensor is embedded within an electrical component (FIG. 2 and [0035]: “one or more battery voltage sensor 230 may be used to measure one or more voltages within battery 208. For example, each battery voltage sensor 230 may be integrated in battery 208 to directly measure voltage of one or more battery cell 210”, And [0036]: “a battery current sensor 232 is integrated in battery 208 to directly measure current within battery 208”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified ZHAO based on the teaching of Greening, to make the method wherein a sensor of said plurality of sensors is embedded within said plurality of electrical components. One of ordinary skill in the art would have been motivated to do this modification since it can help measure status of the electrical components.
Conclusion
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure.
Ahmed (US 20100076607 A1): teaches a method to provide cooling for data center while optimizing power usage efficiency.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES CAI whose telephone number is (571)272-7192. The examiner can normally be reached on M-F 8-5 EST.
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, Kamini Shah can be reached on 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES CAI/Primary Patent Examiner, Art Unit 2115
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