DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 01/17/2024. Claims 1-9 are pending in the case. Claims 1, 8, and 9 are independent claims.
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: measurement data acquisition unit, intermediate feature output unit, and state classification unit in claim 8.
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.
Claim Rejections - 35 U.S.C. § 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 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed towards the statutory category of a process. Claim 8 is directed towards the statutory category of a machine. Claim 9 is directed towards the statutory category of an article of manufacture.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
A state classification method comprising (mental process); and
classifying a state of the device using information based on the intermediate feature (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
acquiring measurement data of a physical quantity related to vibration measured for a vibrating device (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)); and
outputting, by a deep learning model that includes an encoder and a decoder using a recurrent neural network and performs deep learning for predicting a future value of the measurement data for the device, an intermediate feature of the vibration from the encoder based on the measurement data (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
acquiring measurement data of a physical quantity related to vibration measured for a vibrating device (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer); and
outputting, by a deep learning model that includes an encoder and a decoder using a recurrent neural network and performs deep learning for predicting a future value of the measurement data for the device, an intermediate feature of the vibration from the encoder based on the measurement data (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the recurrent neural network is a long short term memory (LSTM) (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the recurrent neural network is a long short term memory (LSTM) (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the deep learning is learning performed in a direction in which a value of a loss function, the loss function being defined such that orthogonalization proceeds among a plurality of elements included in the intermediate feature, decreases (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the deep learning is learning performed in a direction in which a value of a loss function, the loss function being defined such that orthogonalization proceeds among a plurality of elements included in the intermediate feature, decreases (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the value of the loss function decreases as a value of an autocorrelation of the plurality of elements increases, and the value of the loss function decreases as a value of a cross-correlation of the plurality of elements decreases (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the value of the loss function decreases as a value of an autocorrelation of the plurality of elements increases, and the value of the loss function decreases as a value of a cross-correlation of the plurality of elements decreases (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
the measurement data is measurement data of a plurality of channels (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data in which at least one of a phase and an amplitude of a signal component of a specific frequency of the first time-series data is changed (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data in which at least one of a phase and an amplitude of a signal component of a specific frequency of the first time-series data is changed (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data of the physical quantity measured for the device whose state changes with time after the first time-series data is measured (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data of the physical quantity measured for the device whose state changes with time after the first time-series data is measured (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
The remaining claims 8 and 9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claim 1 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more.
Claim Rejections - 35 U.S.C. § 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.
Claim limitation “measurement data acquisition unit, ” “intermediate feature output unit, ” and “state classification unit” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is no association between the structure and the function that can be found in the specification. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 U.S.C. § 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 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1, 2, and 5-9 are rejected under 35 U.S.C. § 103 as being unpatentable over Freed et al. (U.S. Pat. App. Pub. No. 2021/0133559, hereinafter Freed) in view of Didari et al. (U.S. Pat. App. Pub. No. 2020/0104639, hereinafter Didari).
As to independent claims 1, 8 and 9, Freed teaches:
A state classification method comprising (Title and abstract):
acquiring measurement data of a physical quantity related to vibration measured for a vibrating device (Figure 3, vibration measurement data 314);…
classifying a state of the device using information based on the intermediate feature (Paragraph 12, "The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system").
Freed does not appear to expressly teach outputting, by a deep learning model that includes an encoder and a decoder using a recurrent neural network and performs deep learning for predicting a future value of the measurement data for the device, an intermediate feature of the vibration from the encoder based on the measurement data.
Didari teaches outputting, by a deep learning model that includes an encoder and a decoder using a recurrent neural network and performs deep learning for predicting a future value of the measurement data for the device, an intermediate feature of the vibration from the encoder based on the measurement data (Paragraphs 95-100. See also Freed at paragraph 12, "trains the encoder and decoder using vibration measurement data". Paragraph 59, "encoder 310 may comprise an input layer, a long short-term memory (LSTM) layer, as well as two dense layers that are separated by dropout layers in between").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the detecting faults using vibration measurement data of Freed to include the long short-term memory anomaly detection for multi-sensor equipment monitoring techniques of Didari to use an LSTM model to analyze and determine different combinations of sensors over time (see Didari at paragraph 17).
As to dependent claim 2, Freed further teaches the recurrent neural network is a long short term memory (LSTM) (Claim 6, "the encoder comprises a long short-term memory (LSTM) layer").
As to dependent claim 5, Freed further teaches the measurement data is measurement data of a plurality of channels (Figure 3, vibration sensors 302).
As to dependent claim 6, Didari further teaches the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data in which at least one of a phase and an amplitude of a signal component of a specific frequency of the first time-series data is changed (Paragraph 80, "the anomaly identified by the processing logic is based on one or more of a change in amplitude, a change in frequency, a phase shift, a vertical shift, etc.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the detecting faults using vibration measurement data of Freed to include the long short-term memory anomaly detection for multi-sensor equipment monitoring techniques of Didari to use an LSTM model to analyze and determine different combinations of sensors over time (see Didari at paragraph 17).
As to dependent claim 7, Didari further teaches the deep learning is learning performed using first time-series data of the physical quantity measured for the device and second time-series data of the physical quantity measured for the device whose state changes with time after the first time-series data is measured (Paragraph 24).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the detecting faults using vibration measurement data of Freed to include the long short-term memory anomaly detection for multi-sensor equipment monitoring techniques of Didari to use an LSTM model to analyze and determine different combinations of sensors over time (see Didari at paragraph 17).
Claim 3 is rejected under 35 U.S.C. § 103 as being unpatentable over Freed in view of Didari and Cacciarelli et al. (Cacciarelli, Davide, and Murat Kulahci. "A novel fault detection and diagnosis approach based on orthogonal autoencoders." Computers & Chemical Engineering 163 (2022): 107853, hereinafter Cacciarelli).
As to dependent claim 3, the rejection of claim 1 is incorporated.
Freed does not appear to expressly teach the deep learning is learning performed in a direction in which a value of a loss function, the loss function being defined such that orthogonalization proceeds among a plurality of elements included in the intermediate feature, decreases.
Cacciarelli teaches the deep learning is learning performed in a direction in which a value of a loss function, the loss function being defined such that orthogonalization proceeds among a plurality of elements included in the intermediate feature, decreases (Title and abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the detecting faults using vibration measurement data of Freed to include the fault detection and diagnosis approach based on orthogonal autoencoders techniques of Cacciarelli to ensure no correlation among the features of the latent variables (see Cacciarelli at abstract).
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over Freed in view of Didari, Cacciarelli, and Zbontar et al. (Zbontar, Jure, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. "Barlow twins: Self-supervised learning via redundancy reduction." In International conference on machine learning, pp. 12310-12320. PMLR, 2021, hereinafter Zbontar).
As to dependent claim 4, the rejection of claim 3 is incorporated.
Freed does not appear to expressly teach the value of the loss function decreases as a value of an autocorrelation of the plurality of elements increases, and the value of the loss function decreases as a value of a cross-correlation of the plurality of elements decreases.
Zbontar teaches the value of the loss function decreases as a value of an autocorrelation of the plurality of elements increases, and the value of the loss function decreases as a value of a cross-correlation of the plurality of elements decreases (Figure 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the detecting faults using vibration measurement data of Freed to include the cross-correlation techniques of Zbontar to avoid objective function collapse (see Zbontar at abstract).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wong (U.S. Pat. App. Pub. No. 2020/0371491) teaches a method of detecting an operating state of a process, system or machine based on sensor signals from a plurality of sensors is disclosed. The method comprises receiving sensor data, the sensor data based on sensor signals from the plurality of sensors and providing the sensor data as input to a neural network. The neural network comprises an encoder sub-network arranged to receive the sensor data as input and to generate a context vector based on the sensor data; and a decoder sub-network arranged to receive the context vector as input and to regenerate sensor data corresponding to at least a subset of the sensors based on the context vector. The method comprises comparing the context vector to at least one context vector classification; detecting an operating state in dependence on the comparison; and outputting a notification indicating the detected operating state.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Casey R. Garner/Primary Examiner, Art Unit 2123
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