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 INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) ELEMENT IN CLAIM FOR A COMBINATION.—An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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, 4-6, 12, 16-20, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doan Huu (US 2022/0335347) in view of Liao et al. (Liao) (US 2018/0005151).
Regarding claims 1 and 16, Doan Huu discloses a thermal anomaly management system for managing thermal anomalies in an environment, the thermal anomaly management system comprising:
means to receive environment parameter data for a plurality of environment parameters for the environment ([0002], [0023], [0028], a change is temperature is detected);
a computing system having at least one memory and a processor programmed ([0055], [0056], a program stored on a computer readable medium is executed) to provide at least:
a machine learning system (host platform 120) trained from recorded environment parameter data to identify thermal anomalies ([0001], [0014], [0015], [0018], [0023], host platform system 120 uses a time series forecasting machine learning model 121 to detect anomalies via anomaly detector 122);
a Bayesian network trained to identify relationships between environment parameters ([0021], [0024], [0030], a causal graph is determined using the relationship between detected temperature and detected anomalies over time; the examiner notes it is well known in the art for a causal graph builder to be a type of Bayesian network); and
a causal explanation tree developed from the identified relationships (FIG. 4, [0029], [0045], [0046], a causal tree graph is generated); and
using, on identification of a thermal anomaly from received environment parameter data, the causal explanation tree to predict a root cause of the thermal anomaly ([0021], the cause of an anomaly is determined using a causal explanation determined from the causal graph in FIG. 4).
Doan Huu is silent about a deep learning system.
Liao from the same or similar field of endeavor discloses a deep learning system ([0013] temperature is monitored over time using a deep learning structure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Liao into the teachings of Doan Huu for more accurate detection of an anomaly by incorporating a more accurate deep learning system in place of the machine learning system taught by Doan Huu.
Regarding claims 2 and 19, Doan Huu discloses wherein the environmental parameters are of multiple parameter types, including thermal measurements in the environment and at least one more parameter type ([0023] temperature, pressure, and vibration data).
Regarding claims 4 and 20, Doan Huu discloses wherein the at least one more parameter type comprises one or more of cooling equipment status ([0019], time series data for an anomaly in a cooling system).
Regarding claim 5, Doan Huu discloses wherein the environmental parameters comprise one or more parameters with temporal cyclicity ([0019], Time-series analysis (TSA) is dedicated to capturing signal regularity over time as learned from historical analysis of the signal…Anomalies are usually unexpected and are not a result of the dynamic features of the signal such as trend, seasonality/cyclical, residual, etc. aspects of the signal).
Regarding claim 6, Doan Huu discloses wherein said one or more parameters are represented by a time varying sequence of values determined using an autocorrelation function ([0020], the predictive system can identify a correlation between anomalies in a target signal that are the result of anomalies (or other signal change events) in one or more other time-series signals).
Regarding claims 17 and 18, Liao discloses wherein the means to receive environment parameter data comprises a plurality of sensors in the environment ([0002], [0033], sensors are used to capture environmental data), wherein the plurality of sensors comprises temperature sensors ([0013], temperature data is captured over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Liao into the teachings of Doan Huu for incorporating common use temperature sensors for collecting temperature data.
Regarding claims 12 and 24, Doan Huu discloses an alerting system for providing an alert on detection of the thermal anomaly ([0028], an alert is generated based on a co-occurring anomaly).
Claim(s) 10, 22, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doan Huu (US 2022/0335347) in view of Liao et al. (Liao) (US 2018/0005151), and further in view of Watt et al. (Watt) (US 2022/0114437).
Regarding claims 10, 22 and 26, Doan Huu in view of Liao discloses the thermal anomaly management system of claim 16 (see claim 16 above).
Doan Huu in view of Liao is silent about wherein the deep learning system comprises one or more neural networks; and wherein the deep learning system comprises an autoencoder, a variational autoencoder, or a generative adversarial network.
Watt from the same or similar field of endeavor discloses wherein the deep learning system comprises one or more neural networks ([0046], [0059], a deep learning system utilizing an artificial neural network such as an autoencoder is used for detecting anomalies in a data center); and wherein the deep learning system comprises an autoencoder ([0046], [0059], a deep learning system utilizing an artificial neural network such as an autoencoder is used for detecting anomalies in a data center).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Watt into the teachings of Doan Huu in view of Liao for more accurate detection of an anomaly by incorporating a more accurate deep learning system in place of the machine learning system taught by Doan Huu in view of Liao.
Claim(s) 13, 15, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Doan Huu (US 2022/0335347) in view of Liao et al. (Liao) (US 2018/0005151), and further in view of Gefen et al. (Gefen) (US 2019/0227860).
Regarding claims 13 and 25, Doan Huu in view of Liao discloses the thermal anomaly management system of claim 24 (See claim 24 above).
Doan Huu in view of Liao is silent about wherein the alerting system is adapted to provide the alert with the causal explanation tree for the detected thermal anomaly to one or more recipients by a network connection.
Gefen from the same or similar field of endeavor discloses wherein the alerting system is adapted to provide the alert ([0091], system administrators receive an alert) with the causal explanation tree for the detected thermal anomaly to one or more recipients by a network connection ([0047], the causal graph is displayed on a user interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Gefen into the teachings of Doan Huu for allowing a user to more accurately visualize the root cause of the anomaly.
Regarding claim 15, Doan Huu discloses wherein the environmental parameters are of multiple parameter types and the multiple parameter types include thermal measurements in the environment ([0023] temperature, pressure, and vibration data).
Doan Huu in view of Liao is silent about at least one more parameter type; wherein the at least one more parameter type comprises server load and/or server performance.
Gefen from the same or similar field of endeavor discloses at least one more parameter type; wherein the at least one more parameter type comprises server load and/or server performance (claim 18, [0003], [0057], anomalies in data backup operations for a server are monitored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Gefen into the teachings of Doan Huu for detecting and mitigating potential hardware failures in the system.
Allowable Subject Matter
Claims 8 and 21 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chang et al. (Chang) (US 2021/0058424) ([0004], [0063], performance of a data center is monitored and alerts are sent to a user).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFERY A WILLIAMS whose telephone number is (571)270-7579. The examiner can normally be reached M-F 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEFFERY A WILLIAMS/Primary Examiner, Art Unit 2488
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