DETAILED ACTION
This action is in response to the application filed 05/25/2023. Claims 1-9 are pending and have been examined.
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 channel characteristic extraction module” in claim 5 claiming “A channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data …”
“A time sequence characteristic extraction module” in claim 5 claiming “A time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data …”
“A degradation state classification module” in claim 5 claiming “A degradation state classification module configured for performing degradation state classification operation on the time sequence degradation characteristic data…”
“A data difference adjustment module” in claim 5 claiming “A data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set…”
“A component life prediction module” in claim 5 claiming “A component life prediction module configured for performing component life prediction …”
“A data acquisition module” in claim 6 claiming “ a data acquisition module configured for acquiring original degradation data of a target rotary multi-component system”
“A data marking module” in claim 6 claiming “ a data marking module configured for marking data of a preset proportion in the original degradation data…”
“A data establishment module” in claim 6 claiming “ a data establishment module configured for establishing the preset component degradation data…”
Claims 7 and 8 also recite “A channel characteristic extraction module” and “A time sequence characteristic extraction module” as claimed in independent claim 5, and therefore will also be interpreted under 35 U.S.C 112(f).
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 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 5-8 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.
Regarding claim 5, claim limitations “A channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data …”, “A time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data …”, “A degradation state classification module configured for performing degradation state classification operation on the time sequence degradation characteristic data…”, “A data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set…”, “A component life prediction module configured for performing component life prediction …” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structures refer to computer implemented means-plus function. 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. “A channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data …”, “A time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data …”, “A degradation state classification module configured for performing degradation state classification operation on the time sequence degradation characteristic data…”, “A data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set…”, “A component life prediction module configured for performing component life prediction …” are specialized computer functions that would require an algorithm to be disclosed, in addition to the physical structure that would perform the algorithm. While Figure 2, show the modules, there is no physical structure shown in Figure 2. 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.
Claims 6-8 are rejected for at least the same reasons as claim 5 because they are dependent on claim 5.
Regarding claim 6, claim limitations “ a data acquisition module configured for acquiring original degradation data of a target rotary multi-component system”, “ a data establishment module configured for establishing the preset component degradation data…”, “ a data establishment module configured for establishing the preset component degradation data…” invoke 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. “A data acquisition module configured for acquiring original degradation data of a target rotary multi-component system”, “ a data establishment module configured for establishing the preset component degradation data…”, “ a data establishment module configured for establishing the preset component degradation data…” are specialized computer functions that would require an algorithm to be disclosed, in addition to the physical structure that would perform the algorithm. While Figure 2, show the modules, there is no physical structure shown in Figure 2. 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. 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.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claim 5-8 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 5, “A channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data …”, “A time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data …”, “A degradation state classification module configured for performing degradation state classification operation on the time sequence degradation characteristic data…”, “A data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set…”, “A component life prediction module configured for performing component life prediction …” as described above does not provide adequate structure to perform the claimed function (see 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention.
Claims 6-8 are rejected for at least the same reasons as claim 5 because they are dependent on claim 1.
Regarding claim 6, “ a data acquisition module configured for acquiring original degradation data of a target rotary multi-component system”, “ a data establishment module configured for establishing the preset component degradation data…”, “ a data establishment module configured for establishing the preset component degradation data…” as described above does not provide adequate structure to perform the claimed function (see 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
performing degradation state classification operation on the time sequence degradation characteristic data … to obtain a degradation state data set, wherein the degradation state data set comprises state data with label and state data without label; (This limitation is a mental process as it encompasses a human mentally performing a classification operation.)
performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data; (This limitation is a mental process as it encompasses a human mentally performing difference adjustment.)
performing component life prediction according to the optimized characteristic data to obtain a life prediction curve (This limitation is a mental process as it encompasses a human mentally performing a prediction.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network, wherein the preset component degradation data comprise data with life label and data without life label; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, wherein the preset time sequence attention network comprises a preset time sequence weight; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
by using a preset degradation state classifier (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
by using a preset LSTM prediction model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network, wherein the preset component degradation data comprise data with life label and data without life label; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, wherein the preset time sequence attention network comprises a preset time sequence weight; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
by using a preset degradation state classifier uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by using a preset LSTM prediction model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
marking data of a preset proportion in the original degradation data according to a preset rule to obtain the data with life label; (This limitation is a mental process as it encompasses a human mentally marking data according to a preset rule.)
establishing the preset component degradation data based on unmarked data in the original degradation data and the data with life label. (This limitation is a mental process as it encompasses a human mentally establishing data based on unmarked data.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
acquiring original degradation data of a target rotary multi-component system; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
acquiring original degradation data of a target rotary multi-component system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
performing convolution calculation on the initial degradation characteristic data based on a spatial convolution layer in the preset time sequence attention network to obtain multiple segments of spatial characteristic data; (This limitation is a mental process as it encompasses a human mentally performing a convolution calculation.)
performing weighted average calculation according to the spatial characteristic data based on the preset time sequence weight to obtain multiple segments of channel degradation characteristic data; (This limitation is a mental process as it encompasses a human mentally performing a weighted average.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
splicing the channel degradation characteristic data according to a time sequence to obtain the time sequence degradation characteristic data. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
splicing the channel degradation characteristic data according to a time sequence to obtain the time sequence degradation characteristic data. is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
marking and classifying the state data without label in the degradation state data set … to obtain proposed classification state data; (This limitation is a mental process as it encompasses a human mentally marking and classifying data.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
through a Gaussian mixture model classifier in the domain adversarial network (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
inputting the proposed classification state data and the state data with label in the degradation state data set into a domain adversarial device in the domain adversarial network for data alignment operation to obtain the optimized characteristic data (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
through a Gaussian mixture model classifier in the domain adversarial network uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
inputting the proposed classification state data and the state data with label in the degradation state data set into a domain adversarial device in the domain adversarial network for data alignment operation to obtain the optimized characteristic data is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Claim 5 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
performing degradation state classification operation on the time sequence degradation characteristic data … to obtain a degradation state data set, wherein the degradation state data set comprises state data with label and state data without label; (This limitation is a mental process as it encompasses a human mentally performing a classification operation.)
performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data; (This limitation is a mental process as it encompasses a human mentally performing difference adjustment.)
performing component life prediction according to the optimized characteristic data to obtain a life prediction curve (This limitation is a mental process as it encompasses a human mentally performing a prediction.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
a channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network, wherein the preset component degradation data comprise data with life label and data without life label; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
a time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, wherein the preset time sequence attention network comprises a preset time sequence weight; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
a degradation state classification module configured for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
by using a preset degradation state classifier (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
a data difference adjustment module configured for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
a component life prediction module configured for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
by using a preset LSTM prediction model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network, wherein the preset component degradation data comprise data with life label and data without life label; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, wherein the preset time sequence attention network comprises a preset time sequence weight; is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a degradation state classification module configured for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by using a preset degradation state classifier uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a data difference adjustment module configured for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a component life prediction module configured for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
by using a preset LSTM prediction model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
marking data of a preset proportion in the original degradation data according to a preset rule to obtain the data with life label; (This limitation is a mental process as it encompasses a human mentally marking data according to a preset rule.)
establishing the preset component degradation data based on unmarked data in the original degradation data and the data with life label. (This limitation is a mental process as it encompasses a human mentally establishing data based on unmarked data.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
a data acquisition module configured for acquiring original degradation data of a target rotary multi-component system; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
a data marking module configured for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
a data establishment module configured for (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a data acquisition module acquiring original degradation data of a target rotary multi-component system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a data marking module configured for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a data establishment module configured for uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7, claim 7 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 8, claim 8 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites the same abstract ideas as claim 1. Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
wherein the device comprises a processor and a storage; the storage is configured for storing a program code and transmitting the program code to the processor; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
the processor is configured for executing the life prediction method of the rotary multi-component system according to claim 1 based on an instruction in the program code. (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim # is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the device comprises a processor and a storage; the storage is configured for storing a program code and transmitting the program code to the processor is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
the processor is configured for executing the life prediction method of the rotary multi-component system according to claim 1 based on an instruction in the program code uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 9 is subject-matter ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 is advised of the obligation under 37 CFR 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.
Claim(s) 1-2, 5-6, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2023/0153608 A1) (hereafter referred to as Zhou) in view of Mansouri et al. (US 2024/0303483 A1) (hereafter referred to as Mansouri) and in further view of Wu et al. (“Weighted Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction”) (hereafter referred to as Wu).
Regarding claim 1, Zhou teaches:
A life prediction method of a rotary multi-component system, comprising the following steps of: extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network (Zhou, page 13, paragraph 0057, “step 3: CAN training, including adding attention mechanism to a convolutional neural network (CNN) to obtain the CAN model, mining deep degradation features of channel and temporal dimension in the vibration signals, and performing feature extraction on the first feature sequence by the CAN model to obtain the second feature sequence.” Examiner notes that mining features of channel and temporal dimension is extracting initial degradation characteristic data according to present component degradation data based on a preset channel attention network. Examiner notes that the initial degradation characteristic data is the vibration signals, the preset component degradation data is the deep degradation features and the preset channel attention network is the CAN),
extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, (Zhou, page 13, paragraph 0057, “step 3: CAN training, including adding attention mechanism to a convolutional neural network (CNN) to obtain the CAN model, mining deep degradation features of channel and temporal dimension in the vibration signals, and performing feature extraction on the first feature sequence by the CAN model to obtain the second feature sequence.” Examiner notes that extracting features from the first feature sequence is extracting time sequence degradation characteristic data according to the initial degradation characteristic data. Examiner notes that the second feature sequence is the time sequence degradation characteristic data and the CAN is the preset time sequence attention network.);
wherein the preset time sequence attention network comprises a preset time sequence weight (Zhou, page 11, paragraph 0017, “Furthermore the attention mechanism comprises channel attention and spatial attention; a construction process of the attention mechanism comprises extracting feature outputs
z
l
-
1
∈
R
I
x
1
x
j
in second sequence features generated by the CNN model from the attention mechanism, sequentially calculating channel attention weight
α
l
∈
R
1
x
1
x
j
and spatial attention weight
β
∈
R
l
x
1
x
j
, where 1 is a number of convolutional layers and I is a length of feature outputs, J=NxS is a number of the feature outputs, S is a number of channels of input sensor sequence.” Examiner notes that channel weight is the preset time sequence weight)
performing degradation state classification operation on the time sequence degradation characteristic data by using a preset degradation state classifier to obtain a degradation state data set (Zhou, page 11, paragraph 0023, “The method for predicting remaining useful life of railway train bearing based on CAN-LSTM extracts the parameters of the time domain features, the frequency domain features, and the time-frequency domain features from the bearing lifecycle vibration data, and further performs the normalization processing on the parameters of the time domain feature, the frequency domain feature, and the time-frequency domain feature after the normalization processing are taken as inputs of a convolutional attention network (CAN). The deep degradation features in the channel and the temporal dimension are learned by the CAN, thereby improving characterization capability of the deep degradation features. Then the deep degradation features are input into the LSTM, the RUL prediction is performed on the bearing based on degradation features, and meanwhile, a health index is normalized to an interval [0,1] to obtain the same failure threshold.” Examiner notes that the deep degradation features is the degradation state data set and the CAN is the preset degradation state classifier.),
Zhou does not teach
wherein the preset component degradation data comprise data with life label and data without life label
wherein the degradation state data set comprises state data with label and state data without label
performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data
and performing component life prediction according to the optimized characteristic data by using a preset LSTM prediction model to obtain a life prediction curve
Mansouri teaches
wherein the preset component degradation data comprise data with life label and data without life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the preset component degradation data is the database with unlabelled and labelled data.);
wherein the degradation state data set comprises state data with label and state data without label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the degradation state data is the database with labelled and unlabelled data.);
Zhou and Mansouri are considered analogous to the claimed invention because they both use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use the labeled and unlabeled data of Mansouri. Doing so is advantageous because “the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” (Mansouri, page 19, paragraph 0085).
Zhou and Mansouri do not teach, but Wu does teach
performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data (Wu, page 1, 1st column, abstract, “To address the above issue, this article proposes a weighted adversarial loss (WAL) for cross-domain RUL prediction. To be specific, WAL utilizes the ground-truth labels of the source domain and the pseudo-labels of the target domain to calculate weight and then obtain the WAL. This proposed loss forces the adversarial model to align samples with similar RULs from the source and target domains. Therefore, WAL can enhance positive transfer while alleviating negative transfer. Extensive experiments demonstrate that the proposed loss can be effectively plugged into existing adversarial domain adaptation methods and yield state-of-the-art results.” Examiner notes that aligning samples is performing difference adjustment.);
and performing component life prediction according to the optimized characteristic data by using a preset LSTM prediction model to obtain a life prediction curve (Wu, page 6, 4th paragraph, “As mentioned before, we use the BiLSTM network as the feature extractor. To ensure the fairness of the comparison, we use the same number of neurons as CADA, which is 32. As for the RUL predictor and domain classifier, we also adopt the same structure as in CADA, namely a network with three fully connected layers. The numbers of neurons in the RUL predictor are 32, 16, and 1, respectively, while the numbers in discriminators are 64, 32, and 1, respectively. A ReLU activation function is added after each layer” and Wu, page 9, Figure 7
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Examiner notes that the BiLSTM is the preset LSTM prediction model that is used to obtain a life prediction curve as shown in Figure 7.).
Zhou in view of Mansouri and Wu are considered analogous to the claimed invention because they use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou in view of Mansouri to use the difference adjustment like in Wu. Doing so is advantageous because its “able to generate domain-invariant features across data from different conditions. In particular, the proposed loss can enhance positive transfer while alleviating negative transfer through a weighted strategy” (Wu, page 3, 2nd column, 1st paragraph).
Regarding claim 2, Zhou in view of Mansouri and Wu teach the life prediction method of the rotary multi-component system according to claim 1. Zhou in view of Mansouri and Wu further teach
wherein before the step of extracting the plurality of initial degradation characteristic data according to the preset component degradation data based on the preset channel attention network acquiring original degradation data of a target rotary multi-component system (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the original degradation data is the measured signals.);
marking data of a preset proportion in the original degradation data according to a preset rule to obtain the data with life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” where “a set of p time series V={X1, X2,…, Xp} is obtained, corresponding to the p parameters of the shape vector making it possible to monitor the state of the system”(Mansouri, page 19, paragraph 0088). Examiner notes that the labelling of the data according to the processing is marking data according to a preset rule to obtain data with life label. Examiner further notes that the preset proportion is the p time-series that corresponds to the p parameters of the shape vector.);
and establishing the preset component degradation data based on unmarked data in the original degradation data and the data with life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the preset component degradation data is the database with unlabelled and labelled data.).
Zhou and Mansouri are considered analogous to the claimed invention because they both use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use the labeled and unlabeled data of Mansouri. Doing so is advantageous because “the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” (Mansouri, page 19, paragraph 0085).
Regarding claim 5, Zhou teaches:
A life prediction apparatus of a rotary multi-component system, comprising: a channel characteristic extraction module configured for extracting a plurality of initial degradation characteristic data according to preset component degradation data based on a preset channel attention network (Zhou, page 13, paragraph 0057, “step 3: CAN training, including adding attention mechanism to a convolutional neural network (CNN) to obtain the CAN model, mining deep degradation features of channel and temporal dimension in the vibration signals, and performing feature extraction on the first feature sequence by the CAN model to obtain the second feature sequence.” Examiner notes that mining features of channel and temporal dimension is extracting initial degradation characteristic data according to present component degradation data based on a preset channel attention network. Examiner notes that the initial degradation characteristic data is the vibration signals, the preset component degradation data is the deep degradation features and the preset channel attention network is the CAN. Examiner further notes that the CAN is the channel characteristic extraction module.),
A time sequence characteristic extraction module configured for extracting time sequence degradation characteristic data according to the initial degradation characteristic data based on a preset time sequence attention network, (Zhou, page 13, paragraph 0057, “step 3: CAN training, including adding attention mechanism to a convolutional neural network (CNN) to obtain the CAN model, mining deep degradation features of channel and temporal dimension in the vibration signals, and performing feature extraction on the first feature sequence by the CAN model to obtain the second feature sequence.” Examiner notes that extracting features from the first feature sequence is extracting time sequence degradation characteristic data according to the initial degradation characteristic data. Examiner notes that the second feature sequence is the time sequence degradation characteristic data and the CAN is the preset time sequence attention network. Examiner notes that the CAN is the time sequence characteristic extraction module.);
wherein the preset time sequence attention network comprises a preset time sequence weight (Zhou, page 11, paragraph 0017, “Furthermore the attention mechanism comprises channel attention and spatial attention; a construction process of the attention mechanism comprises extracting feature outputs
z
l
-
1
∈
R
I
x
1
x
j
in second sequence features generated by the CNN model from the attention mechanism, sequentially calculating channel attention weight
α
l
∈
R
1
x
1
x
j
and spatial attention weight
β
∈
R
l
x
1
x
j
, where 1 is a number of convolutional layers and I is a length of feature outputs, J=NxS is a number of the feature outputs, S is a number of channels of input sensor sequence.” Examiner notes that channel weight is the preset time sequence weight)
A degradation state classification module configured for performing degradation state classification operation on the time sequence degradation characteristic data by using a preset degradation state classifier to obtain a degradation state data set (Zhou, page 11, paragraph 0023, “The method for predicting remaining useful life of railway train bearing based on CAN-LSTM extracts the parameters of the time domain features, the frequency domain features, and the time-frequency domain features from the bearing lifecycle vibration data, and further performs the normalization processing on the parameters of the time domain feature, the frequency domain feature, and the time-frequency domain feature after the normalization processing are taken as inputs of a convolutional attention network (CAN). The deep degradation features in the channel and the temporal dimension are learned by the CAN, thereby improving characterization capability of the deep degradation features. Then the deep degradation features are input into the LSTM, the RUL prediction is performed on the bearing based on degradation features, and meanwhile, a health index is normalized to an interval [0,1] to obtain the same failure threshold.” Examiner notes that the deep degradation features is the degradation state data set and the CAN is the preset degradation state classifier. Examiner further notes that the CAN-LSTM is the degradation state classification module.),
Zhou does not teach
wherein the preset component degradation data comprise data with life label and data without life label
wherein the degradation state data set comprises state data with label and state data without label
a data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data
and a component life prediction module configured for performing component life prediction according to the optimized characteristic data by using a preset LSTM prediction model to obtain a life prediction curve
Mansouri teaches
wherein the preset component degradation data comprise data with life label and data without life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the preset component degradation data is the database with unlabelled and labelled data.);
wherein the degradation state data set comprises state data with label and state data without label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the degradation state data is the database with labelled and unlabelled data.);
Zhou and Mansouri are considered analogous to the claimed invention because they both use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use the labeled and unlabeled data of Mansouri. Doing so is advantageous because “the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” (Mansouri, page 19, paragraph 0085).
Zhou and Mansouri do not teach, but Wu does teach
a data difference adjustment module configured for performing difference adjustment on characteristic distribution of the degradation state data set based on a domain adversarial network to obtain optimized characteristic data (Wu, page 1, 1st column, abstract, “To address the above issue, this article proposes a weighted adversarial loss (WAL) for cross-domain RUL prediction. To be specific, WAL utilizes the ground-truth labels of the source domain and the pseudo-labels of the target domain to calculate weight and then obtain the WAL. This proposed loss forces the adversarial model to align samples with similar RULs from the source and target domains. Therefore, WAL can enhance positive transfer while alleviating negative transfer. Extensive experiments demonstrate that the proposed loss can be effectively plugged into existing adversarial domain adaptation methods and yield state-of-the-art results.” Examiner notes that aligning samples is performing difference adjustment. Examiner further notes that the adversarial model is the data difference adjustment module.);
and a component life prediction module configured for performing component life prediction according to the optimized characteristic data by using a preset LSTM prediction model to obtain a life prediction curve (Wu, page 6, 4th paragraph, “As mentioned before, we use the BiLSTM network as the feature extractor. To ensure the fairness of the comparison, we use the same number of neurons as CADA, which is 32. As for the RUL predictor and domain classifier, we also adopt the same structure as in CADA, namely a network with three fully connected layers. The numbers of neurons in the RUL predictor are 32, 16, and 1, respectively, while the numbers in discriminators are 64, 32, and 1, respectively. A ReLU activation function is added after each layer” and Wu, page 9, Figure 7
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Examiner notes that the BiLSTM is the preset LSTM prediction model that is used to obtain a life prediction curve as shown in Figure 7. Examiner further notes that the BiLSTM is the component life prediction module.).
Zhou in view of Mansouri and Wu are considered analogous to the claimed invention because they use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou in view of Mansouri to use the difference adjustment like in Wu. Doing so is advantageous because its “able to generate domain-invariant features across data from different conditions. In particular, the proposed loss can enhance positive transfer while alleviating negative transfer through a weighted strategy” (Wu, page 3, 2nd column, 1st paragraph).
Regarding claim 6, Zhou in view of Mansouri and Wu teach the life prediction apparatus of the rotary multi-component system according to claim 5. Zhou in view of Mansouri and Wu further teach
a data acquisition module configured for acquiring original degradation data of a target rotary multi-component system (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the original degradation data is the measured signals. Examiner further notes that the data acquisition module is the processing unit 14.);
a data marking module configured for marking data of a preset proportion in the original degradation data according to a preset rule to obtain the data with life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” where “a set of p time series V={X1, X2,…, Xp} is obtained, corresponding to the p parameters of the shape vector making it possible to monitor the state of the system”(Mansouri, page 19, paragraph 0088). Examiner notes that the labelling of the data according to the processing is marking data according to a preset rule to obtain data with life label. Examiner further notes that the preset proportion is the p time-series that corresponds to the p parameters of the shape vector. Examiner notes that the data marking module is the processing unit 14.);
and a data establishment module configured for establishing the preset component degradation data based on unmarked data in the original degradation data and the data with life label (Mansouri, page 19, paragraph 0085, “The processing unit 14 acquires the measured signals which are stored in the database 17, implements processings on these measured signals, and stores the result of these processings in the memory 16. Initially, the database 17 is composed of unlabelled data coming from measurements taken on test equipment (the jacks 5). This processing is therefore not implemented directly on equipment in operation (commissioned), but the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it.” Examiner notes that the preset component degradation data is the database with unlabelled and labelled data. Examiner further notes that the data establishment module is the processing unit 14.).
Zhou and Mansouri are considered analogous to the claimed invention because they both use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou to use the labeled and unlabeled data of Mansouri. Doing so is advantageous because “the results of the learning will make it possible to estimate the level of severity of defects and the remaining useful life of equipment in operation. The data obtained in operation will then be labelled and introduced into the database 17 to enhance it” (Mansouri, page 19, paragraph 0085).
Regarding claim 9, Zhou in view of Mansouri teaches
A life prediction device of a rotary multi-component system, wherein the device comprises a processor and a storage; the storage is configured for storing a program code and transmitting the program code to the processor; and the processor is configured for executing the life prediction method of the rotary multi-component system according to claim 1 based on an instruction in the program code (Mansouri, page 18, paragraph 0083, “All the operations described are, in this case, implemented in an electronic processing unit 14, which can be seen in FIG. 3, which comprises at least one processing component 15 adapted to execute instructions of a program to implement the partitioning method according to the invention. The program is stored in at least one memory 16 of the processing unit 14.”).
Zhou and Mansouri are analogous to the claimed invention because they both use machine learning to predict the remaining useful life of equipment. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to have implemented Zhou’s method of extracting and classifying data on Mansouri’s device including a processor and a storage. Thus, this would be applying a known technique (extracting and classifying data) to a known device (processor and storage) ready for improvement to yield predictable results (extracted and classified data) (MPEP 2143 I. (C) Use of known technique to improve similar devices (methods, or products) in the same way).
Claim(s) 4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Mansouri and in further view of Wu and Gardner et al. (“Domain-adapted Gaussian mixture models for population-based structural health monitoring”) (hereafter referred to as Gardner).
Regarding claim 4, Zhou in view of Mansouri and Wu teach the life prediction method of the rotary multi-component system according to claim 1. Zhou in view of Mansouri and Wu further teach
wherein the step of performing the difference adjustment on the characteristic distribution of the degradation state data set based on the domain adversarial network to obtain the optimized characteristic data, comprises: marking and classifying the state data without label in the degradation state data set through a … model classifier in the domain adversarial network to obtain proposed classification state data (Wu, page 1, 1st column, abstract, “To address the above issue, this article proposes a weighted adversarial loss (WAL) for cross-domain RUL prediction. To be specific, WAL utilizes the ground-truth labels of the source domain and the pseudo-labels of the target domain to calculate weight and then obtain the WAL. This proposed loss forces the adversarial model to align samples with similar RULs from the source and target domains. Therefore, WAL can enhance positive transfer while alleviating negative transfer. Extensive experiments demonstrate that the proposed loss can be effectively plugged into existing adversarial domain adaptation methods and yield state-of-the-art results.” Examiner notes that aligning samples is marking and classifying the state data, the proposed classification is the labels of the source domain.);
and inputting the proposed classification state data and the state data with label in the degradation state data set into a domain adversarial device in the domain adversarial network for data alignment operation to obtain the optimized characteristic data (Wu, page 5, Fig. 4,
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and Wu, page 3, Table 1
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Examiner notes that fs is the proposed classification state data and ft is the state data with label. D is the domain adversarial device.).
Zhou in view of Mansouri and Wu are considered analogous to the claimed invention because they use machine learning to predict the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou in view of Mansouri to use the difference adjustment like in Wu. Doing so is advantageous because its “able to generate domain-invariant features across data from different conditions. In particular, the proposed loss can enhance positive transfer while alleviating negative transfer through a weighted strategy” (Wu, page 3, 2nd column, 1st paragraph).
Zhou in view of Mansouri and Wu do not teach, but Gardner does teach
marking and classifying the state data without label in the degradation state data set through a Gaussian mixture model classifier(Gardner, page 2, 1st column, 2nd – 3rd paragraph, “Transfer learning, and more specifically domain adaptation, is a branch of machine learning that aims to achieve this goal of identifying a mapping between different domains (i.e. different feature spaces) [5- 7]. By inferring a mapping that harmonises these domains based on some criteria (typically using statistical distances [8--14] and/or manifold assumptions [12, 15]), a classifier can be inferred for a source domain where labelled information is known, and be applied to unlabelled target domains….The method outlined in this paper- named the domain-adapted Gaussian mixture model (DA-GMM)-is an extension of work proposed by PaaBen et al. [18], that inferred a linear mapping between a fully-labelled target dataset and a source Gaussian Mixture Model. The novelty in this paper is that the method has been extended to the scenario where the target is unlabelled (and even the scenario where the source is unlabelled as well).” Examiner notes that the mapping between different domains is marking and classifying the state data.);
Zhou, Mansouri, Wu, and Gardner are considered analogous to the claimed invention because they use machine learning to perform domain adaption on the remaining useful life of equipment. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Zhou in view of Mansouri and Wu to use the Gaussian mixture model of Gardner. Doing so is advantageous because “The inferred mapping aligned the two datasets allowing class information to be shared between the two bridges. The inferred feature space also retained physical meaning, something not possible with many of the existing domain adaptation technologies.” (Gardner, page 10, 2nd column, 1st paragraph).
Regarding claim 8, claim 8 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Allowable Subject Matter
Claims 3 and 7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Examiner further notes that the 101 rejections must be overcome as well.
Specifically, regarding claim 3, “performing weighted average calculation according to the spatial characteristic data based on the preset time sequence weight to obtain multiple segments of channel degradation characteristic data; and splicing the channel degradation characteristic data according to a time sequence to obtain the time sequence degradation characteristic data” in conjunction with the other limitations of the claims are not taught by the prior art of record.
The closest prior art is Zhou, Mansouri, and Wu.
Zhou, Mansouri, and Wu disclose “The life prediction method of the rotary multi-component system according to claim 1” (as shown above in the 103 rejection). Zhou further discloses wherein extracting time sequence degradation characteristic data comprises performing convolution calculation on the initial degradation characteristic data to obtain multiple segments of spatial characteristic data (Zhou, page 2, Figure 1). Zhou fails to disclose performing weighted average calculation according to the spatial characteristic data based on the preset time sequence weight to obtain multiple segments of channel degradation characteristic data and splicing the channel degradation characteristic data according to a time sequence to obtain the time sequence degradation characteristic data.
Neither Mansouri nor Wu disclose performing weighted average calculation according to the spatial characteristic data based on the preset time sequence weight to obtain multiple segments of channel degradation characteristic data and splicing the channel degradation characteristic data according to a time sequence to obtain the time sequence degradation characteristic data either.
Therefore, the prior art of record, individually, or in combination, does not disclose claim 3 as a whole.
Claim 7 recites substantially similar limitations as claim 3 and is therefore allowable under the same rationale if the 101 rejections are overcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang et al. (“Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery”) discloses a CAN that is used for remaining useful life which has channel and temporal attention.
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/K.R.H./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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