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 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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Yes.
Claim 1 recites “a method of identifying fault resolution steps for an equipment…the location of the fault” which is a process.
Step 2A, Prong 1: Judicial exception? Yes.
Claim 1 recites “capturing…obtaining…detecting…determining…identifying…” which fall within both mental processes and mathematical calculation. The steps may be carried out as a mental process if the algorithm is simple enough, and as a mathematical process if the algorithm is more complicated. Therefore, the claimed invention recites an abstract idea. Claim 1 recites mental processes that may be carried out in the human mind or with the aid of pencil and paper in simple situations, or by hardware processors, for more complicated situations.
The claimed invention thus recited as an abstract idea. Claim 1 recites mathematical concepts and/or mental processes, that may be carried out in human mind or with the aid of pencil and paper in simple situations. The claim does not recite a particular equation or algorithm for making the recited combining and performing steps, this just means that the abstract idea is being recited broadly enough to monopolize all possible equations or algorithms that might be used (Please also see MPEP 2106.04(a)(2)(III)(A), (B), (C), and (D).
The broadest reasonable interpretation of the steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Step 2, Prong Two: Practical application? No.
The step “determining location…” and “identifying…” provide nothing more than mere instruction to determining a fault location and identifying faulty resolution steps…See MPEP 2106.05(f) which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instruction to implement an abstract idea: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Limitations “detecting…determining…identifying…” do not show in details how to accomplish the detecting condition state of the at least one primary part…, how to determine location of a fault and how to identifying primary fault resolution steps. The steps are not performed by any particular machine. They are all mere instructions. The “capturing…obtaining…detecting…determining and identifying” are merely data gathering.
The “(determined) location of a fault….” And “(identified) primary fault resolution steps for the at least one primary part….” are data and significant extra solution.
Claim 1 when viewed as a whole does not provide meaningful limitations beyond generally linking the use of the judicial exception to a particular environment to transform the judicial exception into patent-eligible subject matter (see MPEP 2106.05(e)).
Per MPEP 2106.04(d)(1) and 2106.05(a), the claim as a whole does not provide an improvement to other technology or technical field. The claim limitations as recited when viewed as a whole do not include the components or steps of the invention that provide the improvement described in the specification.
The recited limitations “Part” and “equipment” are field of use.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, for reasons that are analogous to the discussion of additional elements at Prong 2.
Claim 7 recites a system and claim 13 recites a non-transitory computer readable medium which do not offer a meaningful limitation beyond generally linking the device to a particular technological environment, that is, implementation via a processor. The recited processor is a tool or a field and is not a particular machine.
In other words, the system claim and medium claim are no different from the method claim 1 in substance; the method claim recites the abstract idea while the device claim recites generic components configured to implement the same abstract idea. The claims do not amount to significantly more than the underlying abstract idea.
Dependent claims 2, 8, and 14 add limitations that can be considered as an additional limitation. However, the claim when viewed as a whole with other limitations does not integrate the abstract idea into a practical application as “rendering…to the at least one primary part” is insignificant as it is merely a data gathering. The use of “(rendered) at least one primary fault…” is unlimited. Further the “display interface….”, “AR device” are not particular and “(rendered)…” represents extra solution activity because it is a mere nomial or tangential addition to the claim. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S.208, 224-26 (2014). This limitation represents extra-solution activity because it is a mere nominal or tangential addition to the claim. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity, for instance the step of printing a menu that was generated through an abstract process in Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241-42 (Fed. Cir. 2016) and the mere generic presentation of collected and analyzed data in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016).
Further, the display interface and AR device are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Dependent claims 3-6, 9-12, and 15-18 add limitations which are data and data gathering merely extending the abstract idea without adding any additional elements.
Claim Rejections - 35 USC § 102
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.
(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.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yan et al. (hereinafter ‘Yan) (USPAP. 20190219994).
Regarding claims 1, 7, and 13, Yan discloses a method of identifying fault resolution steps for an equipment, the method comprising:
capturing multi-modal diagnosis data (Abstract: multi-modal data) associated with at least one primary part in the equipment (Par. 34, Step 210. See Figs. 2 and 8: a plurality of real-time heterogeneous monitoring node signal inputs may receive streams of monitoring node signal values over time that represent a current operation of an industrial asset. At least one of the monitoring nodes (e.g., controller nodes, etc.) may be associated with, for example, sensor data, an auxiliary equipment input signal, a control intermediary parameter, and/or a control logic value. The industrial asset might be associated with, for example, a gas turbine, electric power grid, dam, locomotive, airplane, one or more autonomous vehicles, etc.);
obtaining multi-modal features of the at least one primary part from the multi-modal diagnosis data (Par. 35, Step 220, Figs. 2 and 8. Par. 4: feature extraction process using a multi-modal to generate an initial set of feature vectors and subset. See Pars. 29 and 35 generated/obtained features. See Par. 41 for different types of features that can be used such as principal components, statistical features, deep learning features etc. And see Pars. 43-48 for how other types of features are processed);
detecting a condition state of the at least one primary part using the multi-modal features and a trained object fault detection model (Pars. 4, 5, 36, S230-Fig. 2; system generate a domain level features based on difference between sensor measurements and digital model outputs. Each generated current monitoring node feature vector may be compared to a decision boundary for that monitoring node in real-time. The decision boundary is a feature-based learning algorithm and high fidelity model or a normal operation of the industrial asset and the decision boundary separates a normal state from an abnormal state for that monitoring node. See Pars. 43-48 for how other types of features are processed);
determining location of a fault on the at least one primary part using a trained fault location prediction model (Pars. 4 and 5: abnormal state detection model) when the condition state of the at least one primary part is detected as a faulty state (Pars. 4, 5, 38 and 39: system generate a domain level features based on difference between sensor measurements and digital model output based on selected feature vector subset and the plurality of generated domain level features. For “location of fault”, please see Par. 37’s abnormal state such as plant state attack, physical damage of asset, actuator attack, controller attack);
and identifying primary fault resolution steps (performing response actions) for the at least one primary part based on historic data (see historical data at Par. 41) associated with the at least one primary part and the location of the fault (Par. 37: identifying root cause of the detected fault and automatically transmitting alert signal as well as performing response actions when the alert signal is transmitted. Actions such as automatically shut down all or a portion of industrial asset. Also see Par. 50).
Regarding claims 2, 8, and 14, Yan discloses identifying secondary resolution steps for secondary parts based on the primary fault resolution steps (Par. 37: one or more response actions may be performed when an abnormal alert signal is transmitted. The system might shut down all or portion of the industrial asset, e.g. let the detected potential cyber-attack be further investigated. Further, Par. 37 discloses one or more parameters might be captured automatically modified, a software application might be automatically triggered to capture data and/or isolate possible causes etc., which meets the secondary resolution steps);
and rendering at least one of the primary fault resolution steps and the secondary resolution steps on at least one of a display interface and an Augmented Reality (AR) device for resolution of the fault, wherein the secondary parts are communicably connected to the at least one primary part (See Pars. 32, 59, 61, 92, and 106: display/interactive display and augmented reality).
Regarding claims 3, 9, and 15, Yan discloses wherein the obtaining multi-modal features of the at least one primary part from the multi-modal diagnosis data comprises: extracting a plurality of object feature data from the multi-modal diagnosis data; and combining the plurality of object feature data to obtain the multi-modal features of the at least one primary part (See Pars. 29 and 35 generated/obtained features. See Par. 41 for different types of features that can be used such as principal components, statistical features, deep learning features etc. And see Pars. 43-48 for how other types of features are processed).
Regarding claims 4, 10, and 16, Yan discloses wherein the multi-modal diagnosis data comprises at least one of visual data, audio data, and sensor data (Abstract for the multi-modal diagnosis data, e.g. sensor data).
Regarding claims 5, 11, and 17, Yan discloses wherein the condition state is one of the faulty state and a healthy state (Pars. 39-43: abnormality condition and detection. Also see Fig. 6, 640 data sets that includes 642- normal state data set).
Regarding claims 6, 12, and 18, Yan discloses “providing a check list (Par. 29: Information from the normal space data source 110 and the abnormal space data source 120 may be provided to an offline abnormal state detection model creation computer 140 that uses this data to create a decision boundary (that is, a boundary that separates normal behavior from abnormal behavior). The decision boundary may then be used by an abnormal state detection computer 150 executing an abnormal state detection model 155. “Automatically” output a threat alert signal to one or more remote monitoring devices 170 when appropriate (e.g., for display to a user)) to identify a problem in at least one of secondary parts and related parts of the at least one primary part, based on at least one of the historic data associated with the secondary parts (see Par. 41 for historical data sets), the historic data associated with the related parts of the at least one primary part and the primary fault resolution steps, when the problem associated with at least one of the secondary parts and the related parts of the at least one primary part is not detectable (cyber attacks or stealthy threats);
rendering at least one of the primary fault resolution steps and the hidden check list on at least one of a display interface and an Augmented Reality (AR) device for resolution of the fault, wherein the secondary parts are communicably connected to the at least one primary part (See Pars.29, 32, 59, 61, 92, and 106: display/interactive display and augmented reality).
Please see the following for further explanation of how Yan meets the claimed limitations as recited in the claims.
Yan discloses a conventional art problem and how to solve it. That is how to solve when there are stealthy attacks and problems go undetectable in industrial assets. Yan discloses at Par. 3 that subtle consequences of cyber-attacks, such as stealthy attacks occurring at the domain layer, might not be readily detectable (e.g., when only one monitoring node, such as a sensor node, is used in a detection algorithm). It may also be important to determine when a monitoring node is experiencing a fault (as opposed to a malicious attack) and, in some cases, exactly what type of fault is occurring. Existing approaches to protect an industrial control system, such as failure and diagnostics technologies, may not adequately address these problems—especially when multiple, simultaneous attacks and/faults occur since such multiple faults/failure diagnostic technologies are not designed for detecting stealthy attacks in an automatic manner. It would therefore be desirable to protect an industrial asset from cyber-attacks in an automatic and accurate manner even when attacks percolate through the IT and OT layers and directly harm control systems. Par. 37 discloses one or more parameters might be captured automatically modified, a software application might be automatically triggered to capture data and/or isolate possible causes. Par. 45 discloses a feature extraction on each monitoring node element 352 and a normalcy decision 354 with an algorithm to detect attacks in individual signals using sensor specific decision boundaries, as well rationalize attacks on multiple signals, to declare which signals were attacked, and which became anomalous due to a previous attack on the system via a localization module 356. An accommodation element 358 may generate outputs 370, such as an anomaly decision indication (e.g., threat alert signal), a controller action, and/or a list of attacked monitoring nodes).
Yan discloses at Par. 28 that Industrial assets that operate physical systems are increasingly connected to the Internet. As a result, these control systems may be vulnerable to threats and existing approaches to protect an industrial asset, such as FDIA approaches, might not adequately address these threats. It would therefore be desirable to protect an industrial asset from malicious intent such as cyber-attacks in an automatic and accurate manner. FIG. 1 is a high-level architecture of a system 100 in accordance with some embodiments. The system 100 may include a “normal space” data source 110 and an “abnormal space” data source 120. The normal space data source 110 might store, for each of a plurality of heterogeneous “monitoring nodes” 130 (shown in FIG. 1 as “MN.sub.1,” “MN.sub.2,” . . . “MN.sub.N” for “1, 2, . . . N” different monitoring nodes), a series of normal values over time that represent normal operation of an industrial asset (e.g., generated by a model or collected from actual monitoring node 130 data as illustrated by the dashed line in FIG. 1). As used herein, the phrase “monitoring node” might refer to, for example, sensor data, signals sent to actuators and auxiliary equipment, intermediary parameters that are not direct sensor signals, and/or control logical(s). These may represent, for example, threat monitoring nodes that receive data from the threat monitoring system in a continuous fashion in the form of continuous signals or streams of data or combinations thereof. Moreover, the nodes 130 may be used to monitor occurrences of cyber-threats or other abnormal events. This data path may be designated specifically with encryptions or other protection mechanisms so that the information may be secured and cannot be tampered with via cyber-attacks. The abnormal space data source 120 might store, for each of the monitoring nodes 130, a series of abnormal values that represent an abnormal operation of the industrial asset (e.g., when the system is experiencing a cyber-attack). According to some embodiments, the monitoring nodes 130 provide “heterogeneous” data. That is, the data may represent information from widely diverse areas or domains.
Yan discloses at Par. 29 that information from the normal space data source 110 and the abnormal space data source 120 may be provided to an offline abnormal state detection model creation computer 140 that uses this data to create a decision boundary (that is, a boundary that separates normal behavior from abnormal behavior). The decision boundary may then be used by an abnormal state detection computer 150 executing an abnormal state detection model 155. The abnormal state detection model 155 may, for example, monitor streams of data from the monitoring nodes 130 comprising data from sensor nodes, actuator nodes, and/or any other critical monitoring nodes (e.g., monitoring nodes MN.sub.1 through MN.sub.N), calculate at least one “feature” for each monitoring node based on the received data, and “automatically” output a threat alert signal to one or more remote monitoring devices 170 when appropriate (e.g., for display to a user). According to some embodiments, a threat alert signal might be transmitted to a unit controller, a plant Human-Machine Interface (“HMI”), or to a customer via a number of different transmission methods. Note that one receiver of a threat alert signal might be a cloud database that correlates multiple attacks on a wide range of gas turbine assets. As used herein, the term “feature” may refer to, for example, mathematical characterizations of data. Examples of features as applied to data might include the maximum, minimum, mean, standard deviation, variance, range, current value, settling time, Fast Fourier Transform (“FFT”) spectral components, linear and non-linear principal components, independent components, sparse coding features, deep learning features, etc. Moreover, term “automatically” may refer to, for example, actions that can be performed with little or no human intervention. According to some embodiments, information about a detected threat may be transmitted back to the industrial asset.
Therefore, in the Examiner’s position, Yan discloses the limitations as recited in claims 6, 12, and 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
USPAP. 20130067277 discloses A checkpointing fault tolerance network architecture enables a backup computer system to be remotely located from a primary computer system. An intermediary computer system is situated between the primary computer system and the backup computer system to manage the transmission of checkpoint information to the backup VM in an efficient manner. The intermediary computer system is networked to the primary VM through a first connection and is networked to the backup VM through a second connection. The intermediary computer system identifies updated data corresponding to memory pages that have been less frequently modified by the primary VM and transmits such updated data to the backup VM through the first connection. In such manner, the intermediary computer system holds back updated data corresponding to more frequently modified memory pages, since such memory pages may be more likely to be updated again in the future (Abstract; Pars. 22-29).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG HUYNH whose telephone number is (571)272-2718. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/PHUONG HUYNH/ Primary Examiner, Art Unit 2857 January 23, 2026