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 .
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a diagnosis unit;” “an FFT unit;” “an RMS unit;” and “an index storage unit” in claim 6.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
A review of the specification as filed shows that the following appears to be the corresponding structures, materials, or acts described in the specification as filed for the above 35 U.S.C. § 112(f) limitations: “controller,” “a computer program,” or “a microprocessor.”
Claim Objections
Claims 3 and 6-7 objected to because of the following informalities:
Claim 3 line 4 and claim 7 line 3: “receiving the measurement signal in real time” should be corrected to “receiving [[the]]a measurement signal in real time”.
Claim 6 line 6: “according to the signal feature extracted” should be corrected to “according to [[the]]a signal feature extracted”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 1-9 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.
Claims 1-9 recite the limitations “a CNN algorithm,” “an FFT algorithm,” “an RMS value,” and “an LMS algorithm”. The recited limitations are indefinite because they contain acronyms that are not defined in the claims. When possible it is preferable to define acronyms within the claims. For the purposes of examination the recited acronyms shall be interpreted respectively as “a convolutional neural network,” “a fast Fourier transform algorithm,” “a root mean square value,” and “a least mean square algorithm”. This rejection could be overcome by amending the claim language to define each of the recited acronyms.
Claims 2 and 6 recite the limitation “extracting frequency and phase components, which correspond to the signal feature, from the measurement signal, using the index”. It is unclear from the language of the claim if the recited “signal feature” is intended to refer to “the signal feature” or to “the common signal feature”, because the claims assign “the index” to “the common signal feature” and not “the signal feature”. The claims are indefinite because it is unclear how “the index” and “the signal feature” are used together”. For the purposes of examination the recited “the signal feature” shall be interpreted as referring to either the previously defined “a signal feature” or to “a common signal feature”. This rejection could be overcome by amending the claim language to clarify what signal feature is being referenced at any given point in the claims.
Claims 4 and 8 recited the limitation “determining, using the extracted signal feature, whether or not an error occurs in the diagnosis-target apparatus”. It is unclear from the language of the claim if the recited “extracted signal feature” is intended to refer to the previously defined “a signal feature” or to the previously defined “a common signal feature”. The claims are indefinite because it is unclear what is required by the claims. For the purposes of examination the recited “extracted signal feature” shall be interpreted as referring to either “a signal feature” or “a common signal feature”. This rejection could be overcome by amending the claim language to clarify what signal feature is being referenced at any given point in the claims.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), Second paragraph.
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 1-4 and 6-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an Abstract idea without significantly more.
With respect to claim 1 the limitation(s):
A diagnosis method for use by a diagnosis system for diagnosing a state of a diagnosis-target apparatus by receiving and analyzing vibration or noise that is output from the diagnosis-target apparatus, the diagnosis method comprising:
receiving measurement signals from one or more sensors that measure the vibration or the noise;
extracting a signal feature through a CNN algorithm made up of a plurality of convolution layers; and
diagnosing a drive state of the diagnosis-target apparatus according to the signal feature and
outputting diagnosis information of the drive state.
These limitation(s) highlighted in (bold) is/are directed to an abstract idea and would fall within the “Mental Processes” and “Mathematical Concepts” groupings of abstract ideas. The above portion(s) of the claim(s) constitute(s) an abstract idea because:
The limitation(s) regarding “extracting a signal feature through a CNN algorithm made up of a plurality of convolution layers”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than reciting “a diagnosis system,” nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, but for the “a diagnosis system” language, “extracting” in the context of this claim encompasses the user manually extracting signal features from a measurement signal.
Further, the limitation regarding “extracting a signal feature through a CNN algorithm made up of a plurality of convolution layers”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported by the recitation of a mathematical operation acting on one or more variables to determine another. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
The limitation(s) regarding “diagnosing a drive state of the diagnosis-target apparatus according to the signal feature”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than reciting “a diagnosis system,” nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, but for the “a diagnosis system” language, “diagnosing” in the context of this claim encompasses the user manually diagnosing the state of a machine.
Further, referring to the MPEP 2106.04, the claim limitations are analogous to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, recites mathematical relationships, mathematical formulas or equations, and mathematical calculations, then it fall within the “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the non- abstract additional elements of the claims do not impose meaningful limits on practicing the abstract idea(s) recited in the preceding claim(s). In particular, the claims recited the additional elements of:
The limitation(s) regarding “A diagnosis method for use by a diagnosis system for diagnosing a state of a diagnosis-target apparatus” does/do not integrate the abstract idea into a practical application, because it is recited at such a high-level of generality that it is viewed as generally linking the use of the judicial exception to apparatuses. Generally linking the use of the judicial exception to a particular technological environment or field of use, fails to integrate the abstract ideas into a practical application, because the claim does not specify what practical application the claim is directed to.
The limitation(s) regarding “receiving measurement signals from one or more sensors that measure the vibration or the noise” does/do not integrate the abstract idea into a practical application because the claim does not specify what practical application the claim is directed to. Rather the limitation is recited at such a high-level of generality that it amounts to no more than adding insignificant extra- solution activity to the judicial exception, i.e. data gathering. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are regarded as data gathering steps necessary or routine to implement the abstract idea.
The limitation(s) regarding “outputting diagnosis information of the drive state” does/do not integrate the abstract idea into a practical application because the claim does not specify what practical application the claim is directed to. Rather the limitation is recited at such a high-level of generality that it amounts to no more than adding insignificant extra- solution activity to the judicial exception, i.e. insignificant application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are regarded as data outputting steps necessary or routine to implement the abstract idea. Further, referring to the MPEP 2106.05(g), the claim limitations are analogous to a claim to Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.
The limitation(s) regarding “a diagnosis system” does/do not integrate the abstract idea into a practical application because the claim limitation is a generic computer component performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component.
As such Examiner does NOT view that the claims:
-Improve the functioning of a computer, or to any other technology or technical field;
-Apply the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b);
-Effect a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); or
-Apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception – see MPEP 2106.05(e) and Vanda Memo.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements amount to no more than mere instructions to apply the exception using a generic computer component, or are well-understood, routine, and conventional (WURC) data gathering functions.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “a diagnosis-target apparatus” is/are seen as generally linking the use of the judicial exception to a particular technological environment. Linking a judicial exception to a technological environment cannot provide an inventive concept. Similarly, with regards to the additional element(s) of “receiving measurement signals” and "outputting diagnosis information” is/are viewed as insignificant extra-solution activity, such as mere data gathering in a conventional way and, therefore, does not provide an inventive concept. Similarly, with regards to the additional element(s) of “a diagnosis system” is/are view as a generic computer component performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Examiner further notes that such additional elements are viewed to be well- understood, routine, and conventional (WURC) as evidenced by: Walczyk et al. (US 20220244682 A1); Sahara et al. (US 20080033695 A1); Kim et al. (KR 102335013 B1); Kobayashi et al. (US 5638305 A); Li et al. (Li, Shaobo, et al. "An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis." Sensors 17.8 (2017): 1729.); and Qian et al. (Qian, Weiwei, et al. "An intelligent fault diagnosis framework for raw vibration signals: Adaptive overlapping convolutional neural network." Measurement Science and Technology 29.9 (2018): 095009.).
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claims fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitation regarding “a diagnosis-target apparatus,” “receiving measurement signals,” “outputting diagnosis information,” and “a diagnosis system” can be viewed as a field of use, necessary data gathering, and any device and do not impose a meaningful limitation describing what problem is being remedied or solved.
Independent claim 6 is also held to be patent ineligible under 35 U.S.C. 101 because the additionally recited limitations fail to establish that the claims are not directed to an Abstract idea. Claim 6 recites the additional elements of:
The limitation regarding “an FFT unit configured to convert a signal computed by the plurality of convolution layers into a frequency-band signal by applying an FFT algorithm to the signal”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported by the recitation of a mathematical operation acting on one or more variables to determine another. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
The limitation regarding “an RMS unit configured to compute an RMS value of the frequency-band signal and to set the computed RMS value as a criterion”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported by the recitation of a mathematical operation acting on one or more variables to determine another. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
The limitation(s) regarding “an index storage unit configured to extract a plurality of peak values as a plurality of signal features according to the criterion and to store an index for a common signal feature, among the plurality of signal features”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than reciting “a diagnosis system,” nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, but for the “a diagnosis system” language, “extract” in the context of this claim encompasses the user manually extracting peaks from a signal.
The limitation(s) regarding “a frequency and phase extraction unit configured to extract frequency and phase components, which correspond to the signal feature, from the measurement signal, using the index”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than reciting “a diagnosis system,” nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, but for the “a diagnosis system” language, “extract” in the context of this claim encompasses the user manually extracting components indicated by an index.
Dependent claims 2-4 and 7-8 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additionally recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below: there are no additional element(s) in the dependent claims that adds a meaningful limitation to the abstract idea to make the claims significantly more than the judicial exception (abstract idea).
Claims 3 and 7 recite limitations regarding data gathering steps and insignificant application necessary or routine to implement the abstract idea and thus are not significantly more than the abstract idea and viewed to be well known routine and conventional as evidenced by the prior art shown above.
Claims 2-4 and 7-8 further limit the abstract idea with an abstract idea, such as an “Mental Processes” and “Mathematical Concepts”, and thus the claims are still directed to an abstract idea without significantly more.
Claims 5 and 9 are seen as applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As such, claims 5 and 9 are not rejected under 35 USC 101.
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.
Claim(s) 1 and 5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Walczyk et al. (US 20220244682 A1).
Regarding Claim 1. Walczyk teaches:
A diagnosis method for use by a diagnosis system for diagnosing a state of a diagnosis-target apparatus by receiving and analyzing vibration or noise that is output from the diagnosis-target apparatus, the diagnosis method comprising:
receiving measurement signals from one or more sensors that measure the vibration or the noise (See Fig. 9, Fig. 10, and para[0004]: The system includes a plurality of vibration sensors configured to be coupled to a unit of building equipment.);
extracting a signal feature through a CNN algorithm made up of a plurality of convolution layers (See Fig. 9, Fig. 10, para[0123] – para[0124]: As such, CNN models can be utilized to identify abnormal vibration signals can reliably automate a portion of vibration analysis. A convolutional layer can include a number of filters that can learn different features from an input. Convolutional layers may result in parameter sharing as peaks and spectral patterns may repeat throughout an FFT spectrum sample.); and
diagnosing a drive state of the diagnosis-target apparatus according to the signal feature and outputting diagnosis information of the drive state (See Fig. 9, Fig. 10, and para[0135] – para[0136]: As a result of passing an FFT spectra for a vibration data set through ML models 714, a set of abnormality probabilities for the FFT spectra can be calculated and provided to an abnormality identifier 716. For a given FFT spectrum, a specific ML model associated with a frequency range (or other aspect) of the FFT spectrum can analyze the FFT spectrum to determine a probability that the FFT spectrum is abnormal.).
Regarding Claim 5. Walczyk teaches:
The diagnosis method of claim 1,
after the diagnosing of the drive state of the diagnosis-target apparatus according to the signal feature and outputting the diagnosis information of the drive state, further comprising:
inputting the diagnosis information into a controller installed in the diagnosis-target apparatus (See Fig. 4, Fig. 7, para[0063] – para[0065] and para[0106]- para[0107]: Data set abnormality controller 700 can be configured to analyze vibration data sets (or other types of data sets) to determine if the data sets include abnormalities that may be indicative of problems with building equipment.); and
compensating for a change in the state of the diagnosis-target apparatus in response to the diagnosis information (See Fig. 9, Fig. 10, and para[0142]: if the analyst indicates the vibration data set is correctly classified as abnormal by abnormality identifier 716, various corrective actions may be taken to address the abnormality.).
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(s) 6-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walczyk et al. (US 20220244682 A1) in view of Sahara et al. (US 20080033695 A1) and Kim et al. (KR 102335013 B1).
Regarding Claim 6. Walczyk teaches:
A diagnosis system for diagnosing a state of a diagnosis-target apparatus by analyzing vibration or noise that is output from the diagnosis-target apparatus, the system comprising:
a diagnosis unit configured to receive measurement signals from one or more sensors that measure the vibration or the noise (See Fig. 9, Fig. 10, and para[0004]: The system includes a plurality of vibration sensors configured to be coupled to a unit of building equipment.),
to diagnose a drive state of the diagnosis-target apparatus according to the signal feature extracted by a CNN algorithm made up of a plurality of convolution layers (See Fig. 9, Fig. 10, para[0123] – para[0124]: As such, CNN models can be utilized to identify abnormal vibration signals can reliably automate a portion of vibration analysis. A convolutional layer can include a number of filters that can learn different features from an input. Convolutional layers may result in parameter sharing as peaks and spectral patterns may repeat throughout an FFT spectrum sample.), and
to output diagnosis information of the drive state (See Fig. 9, Fig. 10, and para[0135] – para[0136]: As a result of passing an FFT spectra for a vibration data set through ML models 714, a set of abnormality probabilities for the FFT spectra can be calculated and provided to an abnormality identifier 716. For a given FFT spectrum, a specific ML model associated with a frequency range (or other aspect) of the FFT spectrum can analyze the FFT spectrum to determine a probability that the FFT spectrum is abnormal.);
an FFT unit configured to convert a signal computed by the plurality of convolution layers into a frequency-band signal by applying an FFT algorithm to the signal (See Fig. 9, Fig. 10, para[0116] – para[0117], and para[0123] – para[0124]: The FFTs can represent the timewaves in a frequency domain such that the vibration data sets can be more easily processed by ML models 714.);
to store an index for a common signal feature, among the plurality of signal features (See para[0123] – para[0128]: With specific regard to ML models 714, the filters may learn to recognize, for example, FFT peaks and peak patterns, regardless of whether they appear in input. Convolutional layers may result in parameter sharing as peaks and spectral patterns may repeat throughout an FFT spectrum sample. Spectrum CNN models can be trained on labeled historical data that is available (e.g., stored in database 726) so that the spectrum CNN models output a probability that a given spectrum is abnormal (i.e., is indicative of a machine fault).); and
a frequency and phase extraction unit configured to extract frequency components, which correspond to the signal feature, from the measurement signal, using the index (See para[0080], para[0123] – para[0124]: With specific regard to ML models 714, the filters may learn to recognize, for example, FFT peaks and peak patterns. Spectrum CNN models can be trained on labeled historical data that is available (e.g., stored in database 726) so that the spectrum CNN models output a probability that a given spectrum is abnormal (i.e., is indicative of a machine fault). AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model.).
Walczyk is silent as to the language of:
an RMS unit configured to compute an RMS value of the frequency-band signal and to set the computed RMS value as a criterion;
an index storage unit configured to extract a plurality of peak values as a plurality of signal features according to the criterion and
a frequency and phase extraction unit configured to extract phase components, which correspond to the signal feature, from the measurement signal, using the index.
Nevertheless Sahara teaches:
an RMS unit configured to compute an RMS value of the frequency-band signal and to set the computed RMS value as a criterion (See Fig. 1, Fig. 2, para[0133] – para[0134], para[0230], and para[0312] : Extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected. As the threshold value, a relative value is employed that is determined in accordance with the power average value of peaks extracted by the smooth differential peak extraction unit 105b, or in accordance with the root mean square of an overall signal.); and
an index storage unit configured to extract a plurality of peak values as a plurality of signal features according to the criterion (See Fig. 1, Fig. 2, para[0133] – para[0134], para[0230], and para[0312] : Extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected. As the threshold value, a relative value is employed that is determined in accordance with the power average value of peaks extracted by the smooth differential peak extraction unit 105b, or in accordance with the root mean square of an overall signal.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Walczyk by an RMS unit configured to compute an RMS value of the frequency-band signal and to set the computed RMS value as a criterion; an index storage unit configured to extract a plurality of peak values as a plurality of signal features according to the criterion such as that of Sahara. Sahara teaches, “the extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected” (See para[0133]). One of ordinary skill would have been motivated to modify Walczyk, because extracting peaks using a RMS value would have helped to accurately detect peaks covered by noise, as recognized by Sahara.
Sahara is silent as to the language of:
extracting phase components, which correspond to the signal feature, from the measurement signal, using the index.
Nevertheless Kim teaches:
extracting phase components, which correspond to the signal feature, from the measurement signal, using the index (See para[0045] and para[0062]: the first learning unit 120 may train a noise removal model based on a convolutional neural network (cnn) learned to remove noise from the input time series data. the data preprocessor 140 may extract a characteristic variable for at least one of a shape, a size, and a phase from the target vibration data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Walczyk extracting phase components, which correspond to the signal feature, from the measurement signal, using the index such as that of Kim. Kim teaches, “the vibration signal includes various information on the facility state, but there is a possibility that the prediction result is distorted due to interference of a signal generated in the surrounding environment when analyzing the vibration signal” (See para[0002]). One of ordinary skill would have been motivated to modify Walczyk, because determining a phase component of a measurement signal would have helped to determine the state of a device, as recognized by Kim.
Regarding Claim 7. Walczyk teaches:
The diagnosis system of claim 6,
wherein the diagnosis unit comprises:
a filter layer configured to receive the measurement signal in real time (See para[0188]: The sensor controller 1206 is configured to receive measurements (e.g., in the form of analog signals) from the sensors 1201-1204 and process the measurements in real-time in order to provides a stream of vibration data to the gateway 1208. ) and
to filter the received measurement signal to obtain a frequency-band signal that is necessary for analysis (See para[0116]: Data set preparation module 712 performs fast Fourier transforms (FFTs) for each timewave associated with a vibration data set. The FFTs can represent the timewaves in a frequency domain such that the vibration data sets can be more easily processed by ML models 714.);
a plurality of convolution layers configured to sequentially receive signals filtered by the filter layer and to perform convolution on the received filtered signals (See para[0123] – para[0128]: Activation layers of CNNs can apply an activation function to their inputs.); and
a diagnosis layer configured to determine the state of the diagnosis-target apparatus in response to an output signal that undergoes a convolution process (See Fig. 9, Fig. 10, para[0123] – para[0128]: the spectrum CNN models further predict a specific type of machine fault that is present based on the FFT spectra.).
Regarding Claim 9. Walczyk teaches:
The diagnosis system of claim 7,
wherein the diagnosis information is input into a controller installed in the diagnosis-target apparatus (See Fig. 4, Fig. 7, para[0063] – para[0065] and para[0106]- para[0107]: Data set abnormality controller 700 can be configured to analyze vibration data sets (or other types of data sets) to determine if the data sets include abnormalities that may be indicative of problems with building equipment.), and
wherein the controller compensates for a change in the state of the diagnosis-target apparatus in response to the diagnosis information (See Fig. 9, Fig. 10, and para[0142]: if the analyst indicates the vibration data set is correctly classified as abnormal by abnormality identifier 716, various corrective actions may be taken to address the abnormality.).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walczyk et al. (US 20220244682 A1) as applied to claim 1 above, and further in view of Sahara et al. (US 20080033695 A1) and Kim et al. (KR 102335013 B1).
Regarding Claim 2. Walczyk teaches:
The diagnosis method of claim 1,
wherein the extracting of the signal feature through the CNN algorithm made up of the plurality of convolution layers comprises:
converting a signal computed by the plurality of convolution layers into a frequency-band signal by applying an FFT algorithm to the signal (See Fig. 9, Fig. 10, para[0116] – para[0117], and para[0123] – para[0124]: The FFTs can represent the timewaves in a frequency domain such that the vibration data sets can be more easily processed by ML models 714.);
storing an index for a common signal feature, among the plurality of signal features (See para[0123] – para[0128]: With specific regard to ML models 714, the filters may learn to recognize, for example, FFT peaks and peak patterns, regardless of whether they appear in input. Convolutional layers may result in parameter sharing as peaks and spectral patterns may repeat throughout an FFT spectrum sample. Spectrum CNN models can be trained on labeled historical data that is available (e.g., stored in database 726) so that the spectrum CNN models output a probability that a given spectrum is abnormal (i.e., is indicative of a machine fault).); and
extracting frequency, which correspond to the signal feature, from the measurement signal, using the index (See para[0080], para[0123] – para[0124]: With specific regard to ML models 714, the filters may learn to recognize, for example, FFT peaks and peak patterns. Spectrum CNN models can be trained on labeled historical data that is available (e.g., stored in database 726) so that the spectrum CNN models output a probability that a given spectrum is abnormal (i.e., is indicative of a machine fault). AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model.).
Walczyk is silent as to the language of:
computing an RMS value of the frequency-band signal and setting the computed RMS value as a criterion;
extracting a plurality of peak values as a plurality of signal features according to the criterion and
extracting phase components, which correspond to the signal feature, from the measurement signal, using the index.
Nevertheless Sahara teaches:
computing an RMS value of the frequency-band signal and setting the computed RMS value as a criterion (See Fig. 1, Fig. 2, para[0133] – para[0134], para[0230], and para[0312] : Extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected. As the threshold value, a relative value is employed that is determined in accordance with the power average value of peaks extracted by the smooth differential peak extraction unit 105b, or in accordance with the root mean square of an overall signal.); and
extracting a plurality of peak values as a plurality of signal features according to the criterion (See Fig. 1, Fig. 2, para[0133] – para[0134], para[0230], and para[0312] : Extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected. As the threshold value, a relative value is employed that is determined in accordance with the power average value of peaks extracted by the smooth differential peak extraction unit 105b, or in accordance with the root mean square of an overall signal.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Walczyk by computing an RMS value of the frequency-band signal and setting the computed RMS value as a criterion; extracting a plurality of peak values as a plurality of signal features according to the criterion such as that of Sahara. Sahara teaches, “the extracted peaks, peaks, the amplitude level of which is equal to or greater than the threshold value, are selected. Therefore, the peaks of the frequency spectrum that are covered by peak noise can be more accurately detected” (See para[0133]). One of ordinary skill would have been motivated to modify Walczyk, because extracting peaks using a RMS value would have helped to accurately detect peaks covered by noise, as recognized by Sahara.
Sahara is silent as to the language of:
extracting phase components, which correspond to the signal feature, from the measurement signal, using the index.
Nevertheless Kim teaches:
extracting phase components, which correspond to the signal feature, from the measurement signal, using the index (See para[0045] and para[0062]: the first learning unit 120 may train a noise removal model based on a convolutional neural network (cnn) learned to remove noise from the input time series data. the data preprocessor 140 may extract a characteristic variable for at least one of a shape, a size, and a phase from the target vibration data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Walczyk extracting phase components, which correspond to the signal feature, from the measurement signal, using the index such as that of Kim. Kim teaches, “the vibration signal includes various information on the facility state, but there is a possibility that the prediction result is distorted due to interference of a signal generated in the surrounding environment when analyzing the vibration signal” (See para[0002]). One of ordinary skill would have been motivated to modify Walczyk, because determining a phase component of a measurement signal would have helped to determine the state of a device, as recognized by Kim.
Regarding Claims 3. Walczyk teaches:
The diagnosis method of claim 2,
wherein the extracting of the signal feature through the CNN algorithm made up of the plurality of convolution layers comprises:
receiving the measurement signal in real time (See para[0188]: The sensor controller 1206 is configured to receive measurements (e.g., in the form of analog signals) from the sensors 1201-1204 and process the measurements in real-time in order to provides a stream of vibration data to the gateway 1208. ) and
filtering the received measurement signal to obtain a frequency-band signal that is necessary for analysis (See para[0116]: Data set preparation module 712 performs fast Fourier transforms (FFTs) for each timewave associated with a vibration data set. The FFTs can represent the timewaves in a frequency domain such that the vibration data sets can be more easily processed by ML models 714.);
receiving filtered signals sequentially and performing convolution on the received filtered signals (See para[0123] – para[0128]: Activation layers of CNNs can apply an activation function to their inputs.); and
determining the state of the diagnosis-target apparatus in response to an output signal that undergoes a convolution process (See Fig. 9, Fig. 10, para[0123] – para[0128]: the spectrum CNN models further predict a specific type of machine fault that is present based on the FFT spectra.).
Claim(s) 4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walczyk et al. (US 20220244682 A1) in view of Sahara et al. (US 20080033695 A1) and Kim et al. (KR 102335013 B1) as applied to claims 2 and 7 above, and further in view of Kobayashi et al. (US 5638305 A).
Regarding Claims 4 and 8. Walczyk teaches:
The diagnosis method of claim 2, or the diagnosis system of claim 7,
after the extracting of the frequency and phase components, which correspond to the signal feature, from the measurement signal, using the index, further comprising:
determining, using the extracted signal feature, whether or not an error occurs in the diagnosis-target apparatus (See para[0124] – para[0128]: the fully connected layers can output abnormality probabilities based on the FFT spectra received from data set preparation module 712.).
Walczyk is silent as to the language of:
inputting the extracted frequency and phase components as a signal for feedback on an LMS algorithm.
Nevertheless Kobayashi teaches:
inputting the extracted frequency and phase components as a signal for feedback on an LMS algorithm (See Col. 9 line 65 – Col. 10 line 5 and Col. 11 line 40 – Col. 12 line 5: A control LMS (least mean square) processor 29 which operates on an adaptive control algorithm for executing arithmetic operation for updating the filter coefficient of the W filter 26. Phase/amplitude information (transfer characteristic) of the C table 27 in FIG. 3 is updated based on an output from the transfer characteristic-updating block 42. The identifying filter 39 and the identifying LMS processor 41 cooperate to form transfer characteristic-identifying means.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Walczyk by inputting the extracted frequency and phase components as a signal for feedback on an LMS algorithm such as that of Kobayashi. Kobayashi teaches, “vibration/active noise control systems have intensively been developed in various fields of the industry, which are adapted to damp vibrations and noises produced from vibration/noise sources, by the use of an adaptive digital filter (hereinafter referred to as "ADF"), to thereby reduce the vibrations and noises” (See Col. 1 lines 12 – 17). One of ordinary skill would have been motivated to modify Walczyk, because inputting frequency and phase into a LMS algorithm would have helped to damp vibrations and noises, as recognized by Kobayashi.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (Li, Shaobo, et al. "An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis." Sensors 17.8 (2017): 1729.) discloses a convolutional neural network that takes root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs (Abstract).
Qian et al. (Qian, Weiwei, et al. "An intelligent fault diagnosis framework for raw vibration signals: Adaptive overlapping convolutional neural network." Measurement Science and Technology 29.9 (2018): 095009.) discloses diagnosing a mechanical device using vibration data and a convolutional neural network with a root-mean-square (RMS) pooling layer (See Abstract).
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/CARTER W FERRELL/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863