DETAILED ACTION
Non-Final Rejection
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 Objections
Claim 9 is objected to because of the following informalities: the limitation “wherein a step of the method..” should be changed to “wherein the method..”. Appropriate correction is required.
Drawings
The drawings (figs. 1, 2, 10 and 11) are objected to because The items 10-20, s110-s140, s141-s142 and s210-s250 inside the boxes should be changed to storage medium 20, processor 10 and so on.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 9 is rejected under 35 USC § 101 because they are directed to non-statutory subject matter.
The descriptions or expressions of the computer program product (element ) are not physical “things”. They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer, which permit the computer program’s functionality to be realized. In contrast, a claimed a non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. Accordingly, it is important to distinguish claims that define descriptive material per se from claims that define statutory inventions.
Claims 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims1-11 falls within one of the four statutory categories. See MPEP § 2106.03. Each of claims 1-7 fall within category of process; Each of claim8 and 10-11 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863));
Regarding Claims 1-6
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of identifying an abnormality.
The abstract idea is set forth or described by the following italicized limitations:
1. A method for identifying an abnormality in a mechanical apparatus or mechanical component, comprising:
i) acquiring at least two classes of undersampled measurement data collected in or on a mechanical apparatus or mechanical component, all of the at least two classes of undersampled measurement data being different from one another in either one of or both of the following aspects: delay relative to occurrence time of a trigger event, and sampling frequency; and
ii) based on the at least two classes of undersampled measurement data acquired, using an abnormality identification model to identify an abnormality in the mechanical apparatus or mechanical component, the abnormality identification model being based on machine learning and used for identifying an abnormality in the mechanical apparatus or mechanical component..
The italicized limitations above represent a mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “[..]an abnormality identification model to identify an abnormality [..]” is mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea), see 2106.04(a)(2).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ acquiring at least two classes of undersampled measurement data collected in or on a mechanical apparatus or mechanical component, all of the at least two classes of undersampled measurement data being different from one another in either one of or both of the following aspects: delay relative to occurrence time of a trigger event, and sampling frequency;” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 2nd additional first element is “a mechanical apparatus or mechanical component ”. This element amounts to mere use of a generic device, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the two “additional elements” individually do not provide a practical application of the abstract idea.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that are, i.e. a mechanical apparatus or mechanical component”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d))The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Dependent Claims 2-6
Dependent claims 2-6 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-6 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For Examples, claim 2-6: only add insignificant extra-solution activity (e.g., data gathering).
Regarding Claim 7
Exemplary claim 7 is directed to an abstract idea of identifying an abnormality.
The abstract idea is set forth or described by the following italicized limitations:
7. A method for training an abnormality identification model based on machine learning, the abnormality identification model being used to identify an abnormality in a mechanical apparatus or mechanical component, the method comprising:
acquiring at least two classes of undersampled measurement data collected in or on a mechanical apparatus or mechanical component, all of the at least two classes of undersampled measurement data being different from one another in either one of or both of the following aspects: delay relative to occurrence time of a trigger event, and sampling frequency; and
training an abnormality identification model on the basis of the at least two classes of undersampled measurement data acquired.
The italicized limitations above represent a mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “[..]an abnormality identification model to identify an abnormality [..]” is mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea), see 2106.04(a)(2).
Step 2A – Prong 2
Claims 7 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ acquiring at least two classes of undersampled measurement data collected in or on a mechanical apparatus or mechanical component, all of the at least two classes of undersampled measurement data being different from one another in either one of or both of the following aspects: delay relative to occurrence time of a trigger event, and sampling frequency” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
2nd additional first element is “ training an abnormality identification model on the basis of the at least two classes of undersampled measurement data acquired” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 3rd additional first element is “a mechanical apparatus or mechanical component ”. This element amounts to mere use of a generic device with computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea.
.
Step 2B
Claims7 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that are, i.e. a mechanical apparatus or mechanical component”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d))The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
.
Regarding Claims 8-9
Claims 8-9 contains language similar to claims 1-7 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 8-9 are also rejected under 35 U.S.C. § 101(abstract idea). Furthermore, claim contain the additional elements “a computer apparatus, processor, storage medium ”. This element amounts to mere use of a generic device with computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
Regarding Claims 10-11
Claims 10-11 contains language similar to claims 1-9 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 10-11 are also rejected under 35 U.S.C. § 101(abstract idea). Furthermore, claim contain the additional elements “A detection apparatus, configured to collect measurement data at an undersampling frequency, the measurement data indicating an operating status of a mechanical apparatus or mechanical component, and the detection apparatus in particular being communicatively connected to the computer apparatus according to Claim 8, characterized in that wherein: the detection apparatus comprises a single sensor, the sensor being configured to begin data collection at a variable delay (At) in response to a trigger event, and/or configured to have a variable undersampling frequency; or the detection apparatus comprises at least two sensors, a first sensor of the at least two sensors being configured to begin data collection at a first delay in response to a trigger event, and a second sensor being configured to begin data collection at a second delay) different from the first delayin response to a trigger event; and/or the first sensor being configured to have a first undersampling frequency (fs1), and the second sensor being configured to have a second undersampling frequency different from the first undersampling frequency.”. This element amounts to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
Examiner Notes
Regarding claims 1-11, There is no prior art rejection over claims, however there is 101 rejections, specifically claim 1 (“based on the at least two classes of undersampled measurement data acquired, using an abnormality identification model to identify an abnormality in the mechanical apparatus or mechanical component, the abnormality identification model being based on machine learning and used for identifying an abnormality in the mechanical apparatus or mechanical component.”),.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Hay et al. (US 10,977,800) disclose o set the frame rate for capturing the video images. For a user who may lack complete knowledge of signal processing or the current operational state of the machine, this may result in under-sampling, as well as lost efficiency through laborious trial and error as the user attempts to determine an appropriate frame rate on the camera. the reconstructed video of a windmill with multiple damaged blades. Again, this illustrates a condition which can be detected through the practice of present embodiments. Even though the presence of some damage could be seen when the windmill was turning at slow speeds, the full extent of the damage could not be assessed during operation to the extent provided through the practice of the present embodiments. In like manner, conditions associated with the wind turbines in FIGS. 7A-7B and the ice accumulation on the blade in FIG. 8 can be evaluated and diagnosed prior to failure.
b) Swisher et al. (US 2020/0405269) disclose Deep generative networks (DGNs), a family of neural networks, have be shown to simulate high quality data that cannot be distinguished from real data by human viewers. DGNs may be capable of generating images in real or near real time. However, real appearing data created with DGNs alone could produce incorrect medical diagnoses due to the creation of fake data, such as a fake lesion (e.g., false positive) or normalization of a malignant lesion (e.g., false negative). Known approaches for implementing a deep generative network include Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). VAEs generally aim at maximizing the lower bound of the data log-likelihood while GANs aim at achieving an equilibrium between a generator and a discriminator. In some embodiments herein, a GAN modeling framework may be used to implement the neural network applied to undersampled data for producing a better-quality image than the undersampled data would otherwise permit. A GAN suitable for the current application may be implemented as described, for example by Goodfellow, Ian, Jean Pouget-Abadie, et al., in “Generative Adversarial Nets” published in Advances in Neural Information Processing Systems 27 (NIPS 2014), pages 2672-2680, which publication are incorporated herein by reference in its entirety for any purpose.
c) Zang et al. (US 2019/0347772) disclose parallel imaging and compressed sensing, have been employed for accelerated MR image acquisition, however the practical acceleration capability is still limited. For example, when the scan time is significantly shortened, parallel imaging suffers from aliasing artifact along with dramatically amplified noise. In another example, compressed sensing suffers from image blurring. Conventional methods may achieve accelerated data acquisition by: (1) reducing number of repetitions, (2) undersampling beyond the Nyquist sampling rate, or (3) reducing image resolution. Such methods may result in images with various artifacts such as low SNR, aliasing or blurring.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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/MOHAMMAD K ISLAM/ Primary Examiner, Art Unit 2857