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 .
Status of Claims
Claim 1-15 are currently pending and have been examined.
Claims 1-15 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for parent Application No. KR10-2020-0189558 on 06/23/2023.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 12/31/2020 claiming benefit to KR10-2020-0189558.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-15 are drawn to a method, system or manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method, independent claim 14 recites a system, and independent claim 15 recites an arithmetic processor of a system for diagnosing a defect in a rotating machine.
These independent claims recite the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general:
determining a defect level on the basis of data obtained by diagnosing a state of the rotating machine, the data, obtained by diagnosing the state of the rotating machine, including at least one from among a feature vector related to a vibration signal of the rotating machine, a frequency linked to the defect in the rotating machine, and a total vibration value of the rotating machine
applying a weight to the defect level on the basis of information related to a defect in state history data of the rotating machine and/or whether an alarm related to operation information about the rotating machine has occurred; and
determining a defect severity of the rotating machine on the basis of the defect level to which the weight is applied
Under the broadest reasonable interpretation of the limitations, the limitations require (1) determining a defect level of a rotating machine from observing and logging in a feature vector vibration signal, some frequency value, or a total vibration value; (2) weighting the defect level based on the machine’s operational history, specifically how often the machine has had an alarm; and (3) determining a defect severity value for the machine from the weighted defect level. These limitations are best categorized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2).
Dependent claim 2 recites, in part, wherein the state history data of the rotating machine includes a maintenance history of the rotating machine and information related to facilities of the same type, and the defect in the state history data of the rotating machine is a defect with the highest frequency in the facilities of the same type.
Dependent claim 3 recites, in part, , wherein the alarm occurs on the basis of a monitoring item related to operation information of the rotating machine exceeding a preset reference value.
Dependent claim 4 recites, in part, wherein the operation information of the rotating machine includes at least one of a flow rate of a pump related to the rotating machine, front and rear end pressures related to the rotating machine, or a fluid temperature related to the rotating machine.
Dependent claim 5 recites, in part, wherein the applying of the weight to the defect level includes adding the weight to the defect level on the basis of matching between the defect with the highest frequency in the facilities of the same type related to the rotating machine and a defect state of the rotating machine related to the defect level.
Dependent claim 6 recites, in part, wherein the applying of the weight to the defect level includes adding the weight to the defect level on the basis of an occurrence of the alarm related to operation information of the rotating machine.
Dependent claim 7 recites, in part, wherein it is determined whether the alarm related to the operation information of the rotating machine occurs on the basis of a discrepancy between the defect with the highest frequency in the facilities of the same type related to the rotating machine and the defect state of the rotating machine related to the defect level.
Dependent claim 8 recites, in part, wherein the determining of the defect severity includes diagnosing a first defect value for the rotating machine through [a model] on the basis of the feature vector related to a vibration signal of the rotating machine, diagnosing a second defect value on the basis of the frequency linked to the defect of the rotating machine and the first defect value, diagnosing a third defect value on the basis of the total vibration value of the rotating machine and the second defect value, and determining the defect level of the rotating machine on the basis of at least one of the first defect value, the second defect value, and the third defect value.
Dependent claim 9 recites, in part, wherein the diagnosing of the first defect value includes determining whether the rotating machine has a defect through the [model].
Dependent claim 10 recites, in part, wherein on the basis of the existence of the defect in the rotating machine, the first defect value is determined on the basis of all samples related to the rotating machine and defect samples related to the rotating machine, and on the basis of the frequency linked to the defect of the rotating machine being within a preset range, the second defect value is determined as a preset first value.
Dependent claim 11 recites, in part, wherein on the basis of the total vibration value of the rotating machine being smaller than a first threshold value, the third defect value is determined as the second defect value, and the defect level is determined as the second defect value.
Dependent claim 12 recites, in part, wherein on the basis of the total vibration value of the rotating machine being greater than a first threshold value, the third defect value is determined as a preset second value, and the defect level is determined as the preset second value.
Dependent claim 13 recites, in part, wherein on the basis of the total vibration value of the rotating machine being greater than a second threshold value, the third defect value is determined as a preset third value, and the defect level is determined as the preset third value.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claim 1, and hence are nonetheless directed towards fundamentally the same mental process abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 8 and 9 recite machine learning. The specification provides that the machine learning may be a classification model embodiment (see the instant specification in ¶ 45), but provides no required structure only the intended inputs and outputs for said model. The use of machine learning, in this case to determining of the defect severity, only recites the machine learning as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 15 recites a processor. The specification defines the arithmetic processor as a computer with no specific hardware requirements (see the instant specification in ¶ 39). The use of a processor, in this case to performs prediction and diagnosis on the basis of the data acquired from a rotating machine, only recites the processor as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 8 and 9 recite machine learning. Claim 15 recites a processor.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-15 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections-35 USC § 112
The following is a quotation of 35 U.S.C. 112(a)-(b):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
(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 2, 5, and 7 are rejected under 35 U.S.C. 112(b) 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 2 and 7 recites the limitation "the defect in the state history data of the rotating machine is a defect with the highest frequency in the facilities of the same type". There is insufficient antecedent basis for this limitation in the claim. Independent claim 1 recites “a frequency linked to the defect in the rotating machine” without further recitation what constitutes the frequency, if there are more than a single frequency, and the basis for the “highest” comparison. Therefore, the metes and bounds of what is included or excluded in the highest frequency is unclear. The examiner will interpret the “highest frequency” to include any comparison between the functioning of the rotating device with historical data. Claim 5 depends on claim 2 and does not remedy the written description requirement issues of claim 2. As dependent claims inherit the deficiencies of the claims they depend on, claim 5 is also rejected.
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)(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 1-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (US Patent App No 2021/0148791)[hereinafter Huang].
Claim 1 is rejected because Huang teaches on all elements of the claim:
a method of diagnosing a defect in a rotating machine, the method comprising is taught in the Detailed Description in ¶ 0043, ¶ 0121-122, ¶ 0080, in the Figures at fig. 3, and fig. 10 (teaching on a machine learning program on a computer with a processor and corresponding hardware for analyzing historical vibration fault data to determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) for a rotating machine )
determining a defect level on the basis of data obtained by diagnosing a state of the rotating machine is taught in the Detailed Description in ¶ 0080, ¶ 0097-99, and ¶ 0121-122 (teaching on determining a representing a diagnostic state, Sc,t, of a rotating machine from a vectorized vibration sensor data input set)
the data, obtained by diagnosing the state of the rotating machine, including at least one from among a feature vector related to a vibration signal of the rotating machine, a frequency linked to the defect in the rotating machine, and a total vibration value of the rotating machine is taught in the Detailed Description in ¶ 0058-59, ¶ 0085, and ¶ 0097-99 (teaching on the vibration data including (i) collected vibration signal parameter, Vi with length N (treated as synonymous to a vibration signal of the rotating machine) and (ii) a time parameter, t, over a measurement period N (a frequency linked to the defect in the rotating machine))
applying a weight to the defect level on the basis of information related to a defect in state history data of the rotating machine and/or whether an alarm related to operation information about the rotating machine has occurred; and is taught in the Detailed Description in ¶ 0053 and ¶ 0076-79 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data))
determining a defect severity of the rotating machine on the basis of the defect level to which the weight is applied is taught in the Detailed Description in ¶ 0080 and ¶ 0097-99 (determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) from a trained regression model of the Sc,t values determined utilizing the vibration data applied to the time-frequency decomposition coefficients)
Independent claims 14 and 15 are rejected under the same rational.
As per claim 2, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein the state history data of the rotating machine includes a maintenance history of the rotating machine and information related to facilities of the same type, and the defect in the state history data of the rotating machine is a defect with the highest frequency in the facilities of the same type is taught in the Detailed Description in ¶ 0050-53, ¶ 0076-79, and ¶ 0100 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) from facility/factory failure cases (treated as synonymous to a maintenance history) to find the closest similar (treated as synonymous to highest) pattern for clustering)
As per claim 3, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein the alarm occurs on the basis of a monitoring item related to operation information of the rotating machine exceeding a preset reference value is taught in the Detailed Description in ¶ 0053, ¶ 0076-79, and ¶ 0087-89 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) - Examiner notes that the broadest reasonable interpretation of the independent claim does not require the determination of an alarm condition but Huang does teach on an embodiment of determining an alert/fault event based on a threshold value for supervised labeling of failure events)
As per claim 4, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein the operation information of the rotating machine includes at least one of a flow rate of a pump related to the rotating machine, front and rear end pressures related to the rotating machine, or a fluid temperature related to the rotating machine is taught in the Detailed Description in ¶ 0053, ¶ 0076-79, and ¶ 0087-89 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) - Examiner notes that the broadest reasonable interpretation of the independent claim does not require the determination of an alarm condition from operation information)
As per claim 5, Huang discloses all of the limitations of claim 2. Huang also discloses the following:
the method of claim 2, wherein the applying of the weight to the defect level includes adding the weight to the defect level on the basis of matching between the defect with the highest frequency in the facilities of the same type related to the rotating machine and a defect state of the rotating machine related to the defect level is taught in the Detailed Description in ¶ 0050-53 and ¶ 0076-79 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) from facility failure cases (treated as synonymous to a maintenance history) to find the closest similar (treated as synonymous to highest) pattern for clustering)
As per claim 6, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein the applying of the weight to the defect level includes adding the weight to the defect level on the basis of an occurrence of the alarm related to operation information of the rotating machine is taught in the Detailed Description in ¶ 0053, ¶ 0076-79, and ¶ 0087-89 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) wherein the clusting occurs based on determining an alert/fault event based on a threshold value for supervised labeling of failure events)
As per claim 7, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein it is determined whether the alarm related to the operation information of the rotating machine occurs on the basis of a discrepancy between the defect with the highest frequency in the facilities of the same type related to the rotating machine and the defect state of the rotating machine related to the defect level is taught in the Detailed Description in ¶ 0053, ¶ 0076-79, and ¶ 0087-89 (teaching on determining a coefficient for time-frequency decomposition of the vibration signals based on a clustering approach comparing current frequences to historical labeled frequencies (treated as synonymous to state history data) - Examiner notes that the broadest reasonable interpretation of the independent claim does not require the determination of an alarm condition from operation information)
As per claim 8, Huang discloses all of the limitations of claim 1. Huang also discloses the following:
the method of claim 1, wherein the determining of the defect severity includes diagnosing a first defect value for the rotating machine through machine learning on the basis of the feature vector related to a vibration signal of the rotating machine, diagnosing a second defect value on the basis of the frequency linked to the defect of the rotating machine and the first defect value, diagnosing a third defect value on the basis of the total vibration value of the rotating machine and the second defect value, and determining the defect level of the rotating machine on the basis of at least one of the first defect value, the second defect value, and the third defect value is taught in the Detailed Description in ¶ 0080 and ¶ 0097-99 (determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) from a trained regression model of the Sc,t values determined utilizing the vibration data applied to the time-frequency decomposition coefficients determined from a clustering technique comparing unlabeled vibrations to historical, labeled vibration patterns)
As per claim 9, Huang discloses all of the limitations of claim 8. Huang also discloses the following:
the method of claim 8, wherein the diagnosing of the first defect value includes determining whether the rotating machine has a defect through the machine learning is taught in the Detailed Description in ¶ 0086-89 and ¶ 0080 (teaching on fault detection utilizing a machine learning model)
As per claim 10, Huang discloses all of the limitations of claim 9. Huang also discloses the following:
the method of claim 9, wherein on the basis of the existence of the defect in the rotating machine, the first defect value is determined on the basis of all samples related to the rotating machine and defect samples related to the rotating machine, and on the basis of the frequency linked to the defect of the rotating machine being within a preset range, the second defect value is determined as a preset first value is taught in the Detailed Description in ¶ 0058-59, ¶ 0085-88, and ¶ 0097-99 (teaching on the vibration data including (i) collected vibration signal parameter, Vi with length N (treated as synonymous to a vibration signal of the rotating machine) and (ii) a time parameter, t, over a measurement period N (a frequency linked to the defect in the rotating machine))
As per claim 11, Huang discloses all of the limitations of claim 10. Huang also discloses the following:
the method of claim 10, wherein on the basis of the total vibration value of the rotating machine being smaller than a first threshold value, the third defect value is determined as the second defect value, and the defect level is determined as the second defect value is taught in the Detailed Description in ¶ 0080, ¶ 0097-99, ¶ 0066-70, and in the Figures at fig. 7 (determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) from a trained regression model of the Sc,t values determined utilizing the vibration data applied to the time-frequency binary decomposition coefficients wherein each preceding level corresponds to higher frequency scale (treated as synonymous to smaller than a threshold value) and doubles the length of the succeeding level - this is repeated at least three times)
As per claim 12, Huang discloses all of the limitations of claim 10. Huang also discloses the following:
the method of claim 10, wherein on the basis of the total vibration value of the rotating machine being greater than a first threshold value, the third defect value is determined as a preset second value, and the defect level is determined as the preset second value is taught in the Detailed Description in ¶ 0080, ¶ 0097-99, ¶ 0066-70, and in the Figures at fig. 7 (determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) from a trained regression model of the Sc,t values determined utilizing the vibration data applied to the time-frequency binary decomposition coefficients wherein each preceding level corresponds to higher frequency scale (treated as synonymous to smaller than a threshold value) and doubles the length of the succeeding level - this is repeated at least three times)
As per claim 13, Huang discloses all of the limitations of claim 10. Huang also discloses the following:
the method of claim 10, wherein on the basis of the total vibration value of the rotating machine being greater than a second threshold value, the third defect value is determined as a preset third value, and the defect level is determined as the preset third value is taught in the Detailed Description in ¶ 0080, ¶ 0097-99, ¶ 0066-70, and in the Figures at fig. 7 (determine a remaining useful life (RUL) estimation (treated as synonymous to a defect severity) from a trained regression model of the Sc,t values determined utilizing the vibration data applied to the time-frequency binary decomposition coefficients wherein each preceding level corresponds to higher frequency scale (treated as synonymous to smaller than a threshold value) and doubles the length of the succeeding level - this is repeated at least three times)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Amruthnath et al., A Research Study on Unsupervised Machine Learning Algorithms for Early Fault Detection in Predictive Maintenance, 2018 IEEE 20th Conference on Business Informatics (CBI) (Sept. 02, 2018) teaching on a machine learning algorithm for monitoring a rotating machine utilizing vibration pattern data clusting for maintenance planning in the § I. INTRODUCTION on p. 1 and § V. RESULTS on p. 6.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez, can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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