DETAILED ACTION
Non-Final Rejection
Response to Amendment
Applicant’s amendments, filed 04/10/2025 to claims are accepted. In this amendment, claims 1-20 have been amended.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/2025 has been entered.
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.
Claim limitation “calibration module(claim 12), interface module(claim 16)” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description 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 based on fig.4.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described in claims 1, 9 and 17, e.g. “refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold; a second difference between the first value and the second value, wherein the second alert is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold”, the disclosure does not provide adequate structure to perform the claimed functions. The specification only describes “To illustrate, the autoencoder 202 may be trained until an average residual magnitude over the training data is less than a threshold amount.” And Claim 6(“the threshold corresponds to a rate of change threshold”). The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(I): "Similarly, amended claims may lack written description when the claims define the invention in functional language (e.g. refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold; a second difference between the first value and the second value, wherein the second alert is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold; the threshold corresponds to a rate of change threshold.) specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For method, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold; a second difference between the first value and the second value, wherein the second alert is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV."
Remaining claims are also rejected due to dependency of the base independent claims.
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 1–16 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 claims 1–20 falls within one of the four statutory categories. See MPEP § 2106.03. For example, and each of claims 1-8 falls within category of process; each of claims 9-16 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)); and each of claims 17–20 is directed to a “computer-readable medium” and therefore falls within category of manufacture.1
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of detecting deviation from an operational state.
The abstract idea is set forth or described by the following italicized limitations:
1. A method of detecting deviation from an operational state of a device, the method comprising: obtaining data corresponding to sensor data sensed by a sensor package coupled to the device, wherein the sensor package comprises a plurality of sensors;
providing a first portion of the data as input to a neural network, the neural network configured to compress the input to generate a compressed space representation of the input, and to generate an output from the compressed space representation, wherein the output comprises a reconstruction of the input;
generating a first residual corresponding to the first portion of the data based on a difference between the input and the output;
generating a first anomaly score based on the first residual, the first residual distinct from the first anomaly score;
determining whether a first value of the first anomaly score satisfies a criteria to generate an alert;
refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold;
generating a second residual corresponding to a second portion of the data;
generating a second anomaly score based on the second residual and the first residual, the second residual distinct from the second anomaly score;
determining whether a second value of the first anomaly score satisfies the criteria; and
generating a second alert based on a combination of the first value, the second value of the second anomaly score, and a second difference between the first value and the second value,
wherein the second alert is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold..
The italicized limitations above represent a combination of mathematical concepts(i.e., a process that can be performed by mathematical relationships) and mental step. 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. See MPEP 2106.04(a)(2)(I)
For example, the limitations “compress the input to generate[..]a reconstruction of the input; generating a first residual [..]; generating a first anomaly score[..]; determining whether a first value of the first anomaly score satisfies a criteria[..]; generating a second residual [..]; generating a second anomaly score [..]; determining [..] satisfies the criteria; a second difference between the first value and the second value[..] ” are mathematical concepts or formulas ([0025],[0044]-[0060]).
For example, the limitation “refraining from generating a first alert [..];generating a second alert [..],” is a mental evaluation that can be performed mentally and/or with pen and paper. Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claim 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.
The first additional element is “a device”. This element amounts to mere use of a generic computer component and this element individually does not provide a practical application. In view of the above, the “additional element” individually do not provide a practical application of the abstract idea. (See MPEP 2106.05(h))
The second additional element is “obtaining data corresponding to sensor data sensed by a sensor package coupled to the device, wherein the sensor package comprises a plurality of sensors;”. This element appears to limit the “collecting data” to be performed, at least in-part, by use of a memory and 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)
The third additional element “as input to a neural network”. This element appears to limit the “collecting data” to be performed, at least in-part, by use of a memory and 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)
The fourth additional element is “neural network”. This element amounts to mere use of a generic computer component with a high level of generality to be performed, at least in-part, by use of monitoring a generic system with generic components. Therefore, this element individually does not provide a practical application(see fig. 1-2). (See MPEP 2106.05(h))
In view of the above four “additional elements” individually does not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic device with software, where such computers, database and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea. See, MPEP §§2106.05(a).
Step 2B
Claim 1 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. 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.
Dependent Claims 2–8
Dependent claims 2–8 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2–8 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 example: Claims 2-3, 6,7: are mathematical concepts or formulas ([0025],140: fig. 2);
Claim 4: “receive a first time series of data [..]” insignificant extra-solution activity (e.g., data gathering)
train an autoencoder based on the first time series (data gathering)
Claim 5: This element appears to limit the “collecting data” to be performed, at least in-part, by use of a memory and to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering).
Clam 8: These elements are an intended result as well as extra solution activity and are therefore not given patentable weight.
Claims 9-20
Regarding Claims 9 and 17
Claims 9 and 17 contains language similar to claims 1 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 20-21 and 24 are also rejected under 35 U.S.C. § 101(abstract idea).
Furthermore, Claim 9 and 17, recites additional element is “A system to detect deviation from an operational state of a device, the system comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to: ”. This element amounts to mere use of a generic computer system which is well understood routine and conventional (see background of current discloser and IDS) 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).
Dependent Claims 10–16 and 18-20
Dependent claims 10-16 and 18-20 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 10-16 and 18-20 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 example:
Claim 11: insignificant extra-solution activity (e.g., data gathering);
Claim 12: “receive a first time series of data [..]” insignificant extra-solution activity (e.g., data gathering)
train an autoencoder based on the first time series (insignificant extra-solution activity (e.g., data gathering)
Claim 13 : are mathematical concepts or formulas ([0025],140: fig. 2);
Claim 16:post solution activity and an intended result, well known in the industries.
Claims 14 and 15: generic sensor component, Therefore, this element individually does not provide a practical application.
Also, Claims 18-20 contains a language similar to claims 1-16 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 18-20 are also rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 8-10 and 12-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra et al. (US 2020/0012918)(hereinafter Malhotra’918) in view of Trinh et al. (US 2020/0379454) (best understood by the Examiner based on 112 (a) rejection).
Regarding Claims 1, 9 and 17. Malhotra’918 teaches a method of detecting deviation from an operational state of a device, the method comprising(abstract, fig. 1& 3A, [0004]):
(A system to detect deviation from an operational state of a device, the system comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to)(as cited in claim 9) (Abstract, fig. 1& 3A, [0004]; 104: fig. 1);
(A computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations to detect deviation from an operational state of a device, the operations comprising:)(as cited in claim 17) (Abstract, fig. 1& 3A, [0004]; 104: fig. 1 );
obtaining data corresponding to sensor data sensed by a sensor package coupled to the device, wherein the sensor package comprises a plurality of sensors(106: fig. 1; [0028]);
providing a first portion of the data as input to a neural network(input X1: fig. 3A), the neural network configured to compress the input to generate a compressed space representation of the input(dimensionality reduction: fig. 3B), and to generate an output from the compressed space representation(
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: fig. 3B), wherein the output comprises a reconstruction of the input(
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: fig. 3B);
generating a first residual corresponding to the first portion of the data based on a difference between the input and the output(et: fig. 3B);
generating a first anomaly score based on the first residual, the first residual distinct from the first anomaly score(at: fig. 3B);
determining whether a first value of the first anomaly score satisfies a criteria to generate an alert([0034]);
refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold([0034]);
generating a second residual corresponding to a second portion of the data( classifying at least one time instance in the multi-dimensional time series as normal if the anomaly score is less than or equal to the threshold: [0041]);
generating a second anomaly score based on the second residual and the first residual, the second residual distinct from the second anomaly score(F-score: [0041]);
determining whether a second value of the first anomaly score satisfies the criteria([0041]); and
generating a second analysis based on a combination of the first value, the second value of the second anomaly score, and a second difference between the first value and the second value([0041], [0050]),
wherein the second analysis is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold([0041], [0050]).
Malhotra’918 silent about second alert based on second analysis(([0065]-[0066]))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, second alert based on second analysis, as taught by Trinh, so as to detect anomaly of equipment based on sensor measurements of equipment in compact and inexpensive way.
Regarding claims 2 and 18: Malhotra’918 further teaches the first anomaly score is further generated based on a comparison of a plurality of anomaly scores ([0033]-[0034]) to a plurality of reference anomaly scores, wherein the second alert is further generated responsive to the comparison indicating that the plurality of anomaly scores do not follow a normal behavior of the plurality of reference anomaly scores([0041]).
Regarding claim 3: Malhotra’918 further teaches providing the first residual as particular input to a statistics module( μ and Σ are the mean and covariance matrix of the error vectors: [0033]-[0034]); and
generating the first anomaly score at the statistics module using a multivariate test statistic based on residual data and reference residual data([0032]-[0034]).
Regarding Claim 4. Malhotra’918 further teaches prior to obtaining the data, receiving a first time series of data indicative of normal operation of the device from the sensor package([0004], [0022]); and training an auto-encoder based on the first time series([0004], [0022], [0057]).
Regarding Claim 5. Malhotra’918 silent about wherein the second alert includes a display device output, a light output, a buzzer output, a signal port output, and a transmitter output.
Trinh teaches wherein the second alert includes a display device output, a light output, a buzzer output, a signal port output, and a transmitter output([0065]-[0066]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, the second alert includes a display device output, a light output, a buzzer output, a signal port output, and a transmitter output, as taught by Trinh, so as to detect anomaly of equipment based on sensor measurements of equipment in compact and inexpensive way.
Regarding Claims 6. Malhotra’918 further teaches wherein the threshold corresponds to a rate of change threshold (dynamic threshold: [0011][0050]).
Regarding Claims 8: Malhotra’918 silent about generating a graphical user interface including: a graph indicative of a performance metric of the device over time; an alert indication corresponding to a portion of the graph; and an indication of one or more sets of feature data associated with the alert indication.
However, Trinh further teaches generating a graphical user interface including: a graph indicative of a performance metric of the device over time (figs. 4C, 19 A-C); an alert indication corresponding to a portion of the graph(figs. 4C, 19 A-C); and an indication of one or more sets of feature data associated with the alert indication (figs. 4C, 19 A-C).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, generating a graphical user interface including: a graph indicative of a performance metric of the device over time; an alert indication corresponding to a portion of the graph; and an indication of one or more sets of feature data associated with the alert indication, as taught by Trinh, so as to detect anomaly of equipment based on sensor measurements of equipment in compact and inexpensive way.
Regarding Claim 10. Malhotra’918 further teaches the data comprises time domain data(log data: (abstract; [0028]).
Trinh also teaches also teaches the data comprises time domain data ([0041]).
Regarding Claim 12. Malhotra’918 silent about wherein the memory includes a calibration module that is executable by the one or more processors to: receive a first time series of data indicative of normal operation of the device from the sensor package; and train an autoencoder based on the first time series.
However, Trinh teaches wherein the memory includes a calibration module that is executable by the one or more processors to: receive a first time series of data indicative of normal operation of the device from the sensor package([0081]); and train an autoencoder based on the first time series(fig. 9; structure of a second example model of anomaly detection, machine learning model of anomaly detection may be referred to as a variational autoencoder (VAE) model. The VAE model may be an unsupervised learning model that is trained based on training data that does not include labels or only includes a small number of labels on whether a piece of equipment is normal or defective or on the repair history of the equipment. [0081]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Malhotra’918, wherein the memory includes a calibration module that is executable by the one or more processors to: receive a first time series of data indicative of normal operation of the device from the sensor package; and train an autoencoder based on the first time series, as taught by Trinh, so as to detect anomaly of equipment based on sensor measurements of equipment in compact and inexpensive way.
.
Regarding claim 13. Trinh further teaches wherein the memory includes an alert generation model that determines whether a set of one or more anomaly scores indicates deviation from normal operation of the device(570: fig. 5; distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation: abstract ; the predictive maintenance server 110 may have a function that maps the dissimilarity metrics to an overall anomaly score. The predictive maintenance server 110 may generate 570 an alert based on the anomaly score. For example, an alert of maintenance may be triggered when the overall anomaly score is higher than a threshold value. For example, the threshold value may be a value that is multiple times (e.g., 3 x) of the normal range of anomaly scores. The normal value of the anomaly score may be close to 1 because the anomaly score is often normalized. Another threshold value or a dynamic threshold value may be used: [0075]; 1070: fig. 10, [0091] (figs. 4C, 19 A-C)).
Regarding claim 14. Malhotra’918 further teaches the plurality of sensors includes a vibration sensor([0028]).
Trinh also teaches the plurality of sensors includes a vibration sensor([0040])
Regarding claim 15. Malhotra’918 further teaches the plurality of sensors further include a motion sensor ([0028]).
Regarding claim 16. Malhotra’918 further teaches the memory further includes a graphical user interface module that is executable by the one or more processors to generate a graphical user interface to display an output (106: fig. 1; a graphical user interface: [0026]; figs. 4A-C)
Trinh further teaches the memory further includes a graphical user interface module that is executable by the one or more processors to generate a graphical user interface to display an output([0066], [0053]; 466: fig. 4C).
Regarding Claims 19. Malhotra’918 further teaches the sensor data indicates at least one of a motion or an acceleration associated with vibration of the device [0028].
Trinh also teaches the sensor data indicates at least one of a motion or an acceleration associated with vibration of the device [0040].
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra’918 in view of Trinh, further in view of Schuster et al. (US 2021/0116904).
Regarding Claim 7: The modified Malhotra silent about applying a transform to the sensor data to generate a set of features in a frequency domain; and determining a contribution of each feature to the first anomaly score.
However, Schuster further teaches applying a transform to the sensor data to generate a set of features in a frequency domain (FFT spectra: fig. 7); and
determining a contribution of each feature to the first anomaly score (716: fig. 7;[0119], [0136])..
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of the modified Malhotra, applying a transform to the sensor data to generate a set of features in a frequency domain; and determining a contribution of each feature to the first anomaly score, as taught by Schuster, so as to initiate a corrective action responsive to identifying the abnormal state of the system in compact and inexpensive way.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra’918 et al. (US 2020/0012918) in view of Trinh et al. (US 2020/0379454), further in view of Wen et al. (Differential Equation-Based Prediction Model for Early Change Detection in Transient Running Status, 2019)
Regarding Claim 11. Malhotra’918 about the data includes a plurality of values, each value of the plurality of values corresponding to a particular frequency band.
However, Wen teaches the data includes a plurality of values, each value of the plurality of values corresponding to a particular frequency band (page 8, section 5.1, first paragraph ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, the data includes a plurality of values, each value of the plurality of values corresponding to a particular frequency band, as taught by Wen, so as to inspect/diagnose whether an abnormal running status change occurs or not in successive machine operations in compact and inexpensive way.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra’918 et al. (US 2020/0012918) in view of Trinh et al. (US 2020/0379454), further in view of Schuster et al. (US 2021/0116904).
Regarding claim 20. Malhotra’918 silent about the data comprises frequency domain data.
However, Schuster teaches the data comprises frequency domain data (FFT spectra: fig. 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, the data comprises frequency domain data, as taught by Schuster, so as to initiate a corrective action responsive to identifying the abnormal state of the system in compact and inexpensive way.
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claims 1, 9 and 17. The applicant did not agree with it. see, pages 8-11.
In response, the Examiner respectfully disagree because claims 1-20, specially claim 1 represent a combination of mathematical concepts(i.e., a process that can be performed by mathematical relationships) and mental step. Therefore, the above italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance. See MPEP 2106.04(a)(2)(I).
For example, the limitations “compress the input to generate[..]a reconstruction of the input; generating a first residual [..]; generating a first anomaly score[..]; determining whether a first value of the first anomaly score satisfies a criteria[..]; generating a second residual [..]; generating a second anomaly score [..]; determining [..] satisfies the criteria; a second difference between the first value and the second value[..] ” are mathematical concepts or formulas ([0025],[0044]-[0060]).
For example, the limitation “refraining from generating a first alert [..];generating a second alert [..],” is a mental evaluation that can be performed mentally and/or with pen and paper. Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2: Claim 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. The first additional element is “a device”. This element amounts to mere use of a generic computer component and this element individually does not provide a practical application. In view of the above, the “additional element” individually do not provide a practical application of the abstract idea. (See MPEP 2106.05(h)). The second additional element is “obtaining data corresponding to sensor data sensed by a sensor package coupled to the device, wherein the sensor package comprises a plurality of sensors;”. This element appears to limit the “collecting data” to be performed, at least in-part, by use of a memory and 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). The third additional element “as input to a neural network”. This element appears to limit the “collecting data” to be performed, at least in-part, by use of a memory and 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). The fourth additional element is “neural network”. This element amounts to mere use of a generic computer component with a high level of generality to be performed, at least in-part, by use of monitoring a generic system with generic components. Therefore, this element individually does not provide a practical application(see fig. 1-2). (See MPEP 2106.05(h)). In view of the above four “additional elements” individually does not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic device with software, where such computers, database and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea. See, MPEP §§2106.05(a).
Step 2B: Claim 1 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. 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. Furthermore, Current claim invention still similar to claim 2 of example 47 and patent ineligible and not like claim 3 of example 47. As such the 101 rejection is maintained.
Regarding Claim 17, the Applicant argument regarding "computer readable medium" is persuasive and withdrawn.
Applicant’s arguments with respect 103 rejection, specially claims 1, 9 and 17 have been considered but are moot because a new ground of rejection made over amended limitation based on change of scope, see the rejection above where Malhotra et al. (US 2020/0012918)(hereinafter Malhotra’918) in view of Trinh et al. (US 2020/0379454) (best understood by the Examiner based on 112 (a) rejection) teaches all the limitations including amended limitations as providing a first portion of the data as input to a neural network(input X1: fig. 3A), the neural network configured to compress the input to generate a compressed space representation of the input(dimensionality reduction: fig. 3B), and to generate an output from the compressed space representation(
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: fig. 3B), wherein the output comprises a reconstruction of the input(
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: fig. 3B); generating a first residual corresponding to the first portion of the data based on a difference between the input and the output(et: fig. 3B); generating a first anomaly score based on the first residual, the first residual distinct from the first anomaly score(at: fig. 3B); determining whether a first value of the first anomaly score satisfies a criteria to generate an alert([0034]); refraining from generating a first alert while the first value satisfies the criteria responsive to a first difference between a prior residual and the first residual failing to satisfy a threshold([0034]); generating a second residual corresponding to a second portion of the data( classifying at least one time instance in the multi-dimensional time series as normal if the anomaly score is less than or equal to the threshold: [0041]); generating a second anomaly score based on the second residual and the first residual, the second residual distinct from the second anomaly score(F-score: [0041]); determining whether a second value of the first anomaly score satisfies the criteria([0041]); and generating a second analysis based on a combination of the first value, the second value of the second anomaly score, and a second difference between the first value and the second value([0041], [0050]), wherein the second analysis is generated responsive to the second value satisfying the criteria and the second difference satisfying the threshold([0041], [0050]). Malhotra’918 silent about second alert based on second analysis(([0065]-[0066])). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Malhotra’918, second alert based on second analysis, as taught by Trinh, so as to detect anomaly of equipment based on sensor measurements of equipment in compact and inexpensive way.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Iskander et al. (US 20220019863 A1) disclose a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.
b) Kaw. Et al. (US 2021/0302197) disclose adjusting an anomaly score such that the anomaly score for operational data under normal operation falls within a predetermined range, the anomaly score being based on a deviation of the operational data acquired from the monitoring target device from a prediction result obtained by the predictive model, processing of detecting an anomaly or a sign of an anomaly based on the adjusted anomaly score, and processing of displaying information on at least one of the anomaly score and a result of the detection on an output device”.
c) Kim et al. (US 20210125323 A1 ) disclose determining if the abnormal situation occurred at the query time may include generating a situation secondary image by applying the situation image to a pre-learned situation determination model, calculating an anomaly score at the query time based on the situation image and the situation secondary image, and determining that the abnormal situation occurred at the query time when the anomaly score is higher than a preset threshold.
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
1 Applicant’s specification defines “computer-readable medium” as “any non-transitory storage medium that participates in providing data (for example, Any suitable computer-readable storage medium or device may be utilized, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and/or other storage media. As used herein, a “computer-readable storage medium” or “computer-readable storage device” is not a signal” ([0083] of PgPub).