DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined
under the first inventor to file provisions of the AIA .
Claim Objection
2. Claim 4 is objected to because of the following informalities:
In claim 4, line 3, after “based on”, change “the the” into -- the --.
Appropriate correction is required.
Claim Interpretation
3. 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.
4. 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: “an orbit data set obtaining module”, “an orbit data set processing module”, “an orbit identification modeling module” and “an orbit analysizing module” in claims 13 and 14.
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 § 101
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
6. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1-16 are directed to an abstract idea of diagnosing faults of a rotating machinery using a machine/deep learning model.
Specifically, representative claim 1 recites:
A method of establishing a fault diagnosis model for a sliding bearing in rotating machinery, the method comprising:
(S1) obtaining a signal data set of displacement signals of shaft vibration of the sliding bearing in the rotating machinery;
(S2) classifying and archiving the displacement signals of the shaft vibration in the signal data set according to fault types;
(S3) synthesizing the displacement signals in the archived signal data set to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery, so as to obtain a plot data set of the shaft center orbit; and
(S4) training the fault diagnosis model based on the plot data set of the shaft center orbit.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
Method
2A - Prong 1: Judicial Exception Recited?
Yes.
See the bolded portion listed above.
Under its broadest reasonable interpretation (BRI), each of the limitations S2 and S3 encompasses mental processes, i.e., data analysis, evaluation, judgement and/or concepts that can be performed in the human mind with the aid of pen and paper. In particular, a general method of classifying data is considered a mental process, as it is similar to how a human would categorize or filter information. The recited attributes of signals and the derived plot data set are merely characterization which can be viewed as to generally link the use of the mental processes to the relevant technological environment or field of use.
The limitation S4 is recited at a high level of generality. Under its BRI and in light of the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Example 47, claim 2), a generic recitation of training an AI model, by a computer, based on input of existing training data involve optimizing the AI model using a series of mathematical calculations to iteratively adjust the algorithms and/or parameter values of the AI model, therefore encompasses mathematical concepts (see Applicant’s Spec. para. [0061]-[0064]).
Nothing in the bolded portion precludes the limitations S2, S3 and S4 from practically being performed in the mind and/or using a pen and paper. As such, the bolded portion of instant claim 1 falls within a combination of the “Mental Process” and “Mathematical Concepts” groupings of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
Under the BRI, the limitation S1 encompasses an insignificant pre-solution activity (i.e., necessary data gathering). According to MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
The limitation of “a sliding bearing in rotating machinery” is considered merely data characterization that generally links the use of the judicial exception to the relevant technological environment or field of use.
In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications.
2B: Claim provides an Inventive Concept?
No.
See analysis given in 2A - Prong 2 above.
Focusing on what the inventors have invented exactly, it is considered that the “heart” of pending claim 1 is directed to an algorithm of processing observation data values to extract plot data set for training an AI model.
The claim does not recite any additional element that is qualified for “significantly more” or reflects an “inventive concept” (See discussion of prior art as set forth in sections 7-8 below; see also MPEP 2106.05).
The claim is therefore ineligible under 35 USC 101.
The dependent claims 2-8 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above.
Claim 2 recites: wherein the signal data set includes at least one of a set of simulated data and a set of condition monitoring data, the set of simulated data includes data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other, and the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other. Under the BRI, each of the limitations of “a set of simulated data” which “includes data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other” and “the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other” reads on an insignificant pre-solution activity (i.e., necessary data gathering). Further, the limitation of “virtual measurements” covers concepts that can be performed in the human mind, including observation, evaluation, judgment and opinion; while the lack of particular type of sensor and/or deployment of the sensors in particular locations of the rotating machinery would monopolize the judicial exception across a wide range of applications.
Claims 5-8 recite additional elements of a general-purpose computer adapted to perform computing activities via basic function of the computer for practicing an abstract idea. According to MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself. As such, none of the limitations recited in claims 5-8 amounts to be “significantly more” than the abstract idea itself.
Claims 9-16 are rejected for the same reasons as set forth above for claims 1-8. Particular consideration is given to the limitation of “utilizing a fault diagnosis model to diagnose the fault type of the sliding bearing in the rotating machinery to be diagnosed, based on the plot of the shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed”. In view of the USPTO’s July 17, 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), a prediction using a machine learning model is considered an abstract idea if the claim focuses solely on the concept of making predictions and/or classification utilizing the machine learning model, but without any specific technical improvements or applications that go beyond the basic idea of using a computer to analyze data and generate predictions/classifications; essentially, if the claim is too high-level and does not describe a concrete, inventive implementation of the machine learning process. In the instant case, the recited “utilizing a fault diagnosis model to diagnose the fault type of …” generally applies the abstract idea without placing any limits on how the “fault diagnosis model”, which comprises “a machine learning model, a deep learning model or a fusion model thereof”, functions in a way that reflects an inventive concept. Rather, the claims only recite the outcome of the AI model but does not include any details about how the “diagnose” is accomplished. See MPEP 2106.05(f).
Hence instant claims 1-16 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
7. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention; or
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claims 1-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jeong et al. (Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images, Procedia Manufacturing, Volume 5, 2016, Pages 1107–1118).
Regarding claims 1 and 13, Jeong discloses a method of establishing a fault diagnosis model (Fig. 2) for a sliding bearing in rotating machinery and a device (e.g., a general-purpose computer performing a generic computer function of data processing) for implementing the method (Abstract; Fig. 7; Section 4.1: “The testbed consists of a shaft with length of 470 mm coupled with a flexible coupling to reduce the effect of the high frequency vibration, two discs and three bearing housings”), the method comprising: obtaining a signal data set of displacement signals of shaft vibration of the sliding bearing in the rotating machinery (Section 1: “ … monitoring vibration signals collected by accelerometer or proximity sensors in various locations”; Section 2.5: “ … are the vibration signal”); classifying and archiving the displacement signals of the shaft vibration in the signal data set according to fault types (Section 4.1: “We apply the proposed orbit image pattern recognition algorithm to the orbit images collected from the rotor kit, shown in Figure 7. There are pre-defined five classes of orbits: circle (C), ellipse (E), eight (8), heart (H), and tornado (T) shapes according to the rotor status. Different orbit shapes are produced, depending on the rotor status such as normal, unbalance, misalignment, rubbing, etc.”); synthesizing the displacement signals in the archived signal data set to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery, so as to obtain a plot data set of the shaft center orbit (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1”; see also Table 2 and related text); and training the fault diagnosis model (Section 2.3) based on the plot data set of the shaft center orbit (Section 4.1: “These data sets are used to train weight parameters for CNN. Total 3 layers structure (convolution and sub-sampling layer, and fully connected to 5 output neurons) is used for CNN in our experiment. Table 3 and Figure 8 show the detailed training constraints and information”).
Regarding claim 2, Jeong discloses: wherein the signal data set includes at least one of a set of simulated data (Section 3: “ … we will normalize an orbit image with respect to location, rotation, and size in the pre-processing step. … such as offset shifting, re-orienting, and size normalizing are necessary”; see also Figs. 3-5 and related text) and a set of condition monitoring data (e.g., the orbit images collected from the rotor kit, shown in Figure 7), the set of simulated data includes data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other (see Figs. 3-5 and related text), and the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other (Section 4.1: “Two accelerometers are mounted at bearing housing along x and y directions”).
Regarding claim 3, Jeong discloses: wherein training fault diagnosis model based on the plot data set of the shaft center orbit comprises: extracting a set of signal features based on the archived signal data set (see Section 3: Pre-processing), and extracting a set of image features (e.g., Section 4.1: “apply the proposed orbit image pattern recognition algorithm to the orbit images collected from the rotor kit, shown in Figure 7. … Different orbit shapes are produced, depending on the rotor status such as normal, unbalance, misalignment, rubbing, etc.”) based on the plot data set of the shaft center orbit; and training the fault diagnosis model based on the extracted set of the signal features and the extracted set of the image features (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1). The orbit images are changed x and y axial symmetry images to maximize the effect of training. These data sets are used to train weight parameters for CNN”).
Regarding claim 4, Jeong discloses: wherein training the fault diagnosis model based on the extracted set of the signal features and the extracted set of the image features comprises: performing machine learning modeling and deep learning modeling based on the extracted set of the signal features and the extracted set of the image features (Abstract; Section 4.1; see also Fig. 2 and related text); fusing a machine learning model and a deep learning mode to obtain the fault diagnosis model (Section 2.3: Jeong’s Convolutional Neural Network (CNN) model represents a hybrid approach which leverages traditional ML for structured data (e.g., tabular data) and deep learning for unstructured data (e.g., images), this hybrid approach integrates model architectures of traditional machine learning and deep learning using the CNN for feature extraction and classification).
Regarding claims 5-8, Jeong discloses: a device (e.g., a general-purpose computer performing a generic computer function of data processing) for fault diagnosis of a sliding bearing in rotating machinery (Fig. 7), the device comprising: a non-transitory computer-readable storage medium having computer instructions stored thereon, and a processor (inherent to a general-purpose computer), wherein the instructions, when executed by the processor, cause the processor to perform a method of claim 1 (Abstract; Section 2.2: “deep learning is a computational model which is composed of multiple processing layers that perform non-linear input-output mappings to learn representations of data with multiple levels of abstraction. Then, deep learning can find complicated hidden patterns in large data sets by using the backpropagation algorithm to calculate its internal parameters that are used to compute the representation in each layer from the representation in the previous layer”; see also discussion for claim 1 above).
Regarding claims 9 and 14, Jeong discloses a method for fault diagnosis of a sliding bearing in rotating machinery and a device (e.g., a general-purpose computer performing a generic computer function of data processing) for implementing the method (Abstract; Fig. 7; Section 4.1: “The testbed consists of a shaft with length of 470 mm coupled with a flexible coupling to reduce the effect of the high frequency vibration, two discs and three bearing housings”), the method comprising: obtaining displacement signals of shaft vibration of the sliding bearing in the rotating machinery to be diagnosed (Section 1: “ … monitoring vibration signals collected by accelerometer or proximity sensors in various locations”; Section 2.5: “ … are the vibration signal”); synthesizing the displacement signals of the shaft vibration to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1”; see also Table 2 and related text); utilizing a fault diagnosis model (Section 2.3: the CNN model) to diagnose the fault type (classification) of the sliding bearing in the rotating machinery to be diagnosed, based on the plot of the shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed (Section 4.2).
Regarding claim 10, Jeong discloses: wherein in case the fault diagnosis model is a machine learning model, a deep learning model or a fusion model thereof (see discussion of claim 4 above), the method further includes: extracting signal features based on the displacement signals of the shaft vibration, and extracting image features based on the plot of the shaft center orbit (Section 2.3: “Many techniques are performed to solve image pattern recognition problem as extracting features or developing matching algorithm. We will briefly describe how CNN works since it is used as a key algorithm for the orbit image pattern recognition in this paper. … This hierarchical organization is able to extract proper features in image classification tasks”); and inputting the extracted signal features and image features into the fault diagnosis model to diagnose the fault type of the sliding bearing in the rotating machinery to be diagnosed (Section 4.2; see also Section 4.3: “Because deep learning can autonomously extract abstract features which, in general, cannot be seen in a training data set, the deep learning algorithm with CNN can provide robust classification results even with subtle difference of shape, orientation and position”).
Regarding claims 11-12 and 15-16, Jeong discloses the claimed invention (see discussion for claims 5-8 above).
Contact Information
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857