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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 3/20/2026, Applicant responded on 7/20/2026. Amended claim 1, 3, 12, 15.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 3, 12, 15 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 3, 12, 15 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…As amended, independent claim 1 recites, in part, "determining, using the normalized values, a priority associated with each of the plurality of devices; and presenting, via a user interface, the priority associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues." Viewed as a whole, the claim obtains operating data associated with a plurality of physical devices in a plant, performs a calculation on that data using one or more algorithms, normalizes the calculation to obtain normalized values, uses those values both to identify a device having an issue and to determine a priority of the issues with the devices, and then presents, via a user interface, a concrete action directed to devices. This is not a concept a human can perform in the mind or with pen and paper; it is a specific technological process implemented across a plurality of plant devices using one or more sensors....the claims cannot reasonably be characterized as "certain methods of organizing human activity." The claims recite the acquisition and computational processing of machine-generated operating data from industrial devices, the normalization and prioritization of that data, and the presentation of a device-specific corrective action. In other words, a specific implementation of a plant issue-identification and prioritization system, not an abstract idea….the claims implement a practical application. The invention provides a technological improvement to plant operation by facilitating performance and/or issue identification for one or more pieces of equipment and/or processes within a plant or enterprise, and that adjustments may be made to accommodate changes in data prior to running algorithms, which may improve the algorithm output…Claim 1 then applies that prioritization in a meaningful way by "presenting, via a user interface, the priority associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues." Rather than merely applying an abstract idea using generic components, the claim reflects a specific technical improvement enabling plant operators to identify and address the most consequential equipment issues, improving plant performance and reducing downtime. This particular ordered combination achieves a concrete, real-world result in the technical field of industrial process control and automation, thereby integrating any recited exception into a practical application….” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, by Applicant’s own admission in Applicant’s specification and remarks, the claims and the argued elements, indeed recite and direct to, …facilitate performance and/or issue identification for one or more pieces of equipment and/or processes within a plant or enterprise.…to recommend at least one action to the user (i.e. human), where the action (i.e. activities) may be one or more of: an investigation step to further identify root causes of issues of interest (i.e. activities, mental process); an improvement path to reduce or eliminate issues of interest (i.e. activities, mental process); a prioritization of investigation steps and/or improvement paths (i.e. activities, mental process); an estimate of the invasiveness of the investigation steps and/or improvement paths (i.e. activities, mental process); and/or an estimate of the importance or financial (i.e. fundamental economic principles, commercial interactions) impact of the investigation steps and/or improvement paths (i.e. activities, mental process).…dynamically adjust the investigation steps or improvement paths based on multiple statistical calculations done within the analysis component. (i.e. mental statistical process) Dynamically adjusting can be performed on the same data sets or can use multiple data sets. Dynamically adjusting the analysis component based on a metric defining the importance of the input variables in question can also be performed (i.e. mental statistical process)….improve the algorithm output (i.e. mental process)…enabling plant operators (i.e. human) to identify (i.e. mental process) and address the most consequential equipment issues (i.e. organizing human activities, fundamental economic principles, commercial interactions), improving plant performance and reducing downtime (i.e. organizing human activities, fundamental economic principles, commercial interactions)…, which is a problem directed to organizing human activity (i.e. human calculating and determining device having issues, normalizing issues’ statistical variables to determine priorities of issues and instructing humans to address device issues based on priorities, i.e. fundamental economic practice, commercial interactions) and a mental process (i.e. human calculating and determining device having issues, normalizing issues’ statistical variables to determine priorities of issues and instructing humans to address device issues based on priorities), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination as a whole, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer, sensors. Further, as an ordered combination as a whole, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, sensors, performing extra solution activities. Therefore, as an ordered combination as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link) or amount to significantly more in Step 2B (apply it and wurc).
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and certain methods of organizing human activities implemented using or applying generic computer components.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s remarks are moot in light of new grounds of rejections necessitated by Applicant’s amendments.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 12) recites, “A process for identifying improvements in a plant, comprising:
obtaining operating data associated with a plurality of devices in the plant;
performing, using one or more algorithms, at least one calculation relative to the operating data;
normalizing the at least one calculation to obtain normalized values;
identifying, using the normalized values, one or more issues associated with one or more of the plurality of devices;
determining, using the normalized values, a priority associated with each of the plurality of devices; and
presenting, via a …, the priority associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …A process for identifying improvements in a plant, comprising: obtaining operating data associated with a plurality of devices in the plant; performing, using one or more algorithms, at least one calculation relative to the operating data; normalizing the at least one calculation to obtain normalized values; identifying, using the normalized values, one or more issues associated with one or more of the plurality of devices; determining, using the normalized values, a priority associated with each of the plurality of devices; and presenting, via a …, the priority associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims are directed to a mental process.
Further, … A process for identifying improvements in a plant, comprising: obtaining operating data associated with a plurality of devices in the plant; performing, using one or more algorithms, at least one calculation relative to the operating data; normalizing the at least one calculation to obtain normalized values; identifying, using the normalized values, one or more issues associated with one or more of the plurality of devices; determining, using the normalized values, a priority associated with each of the plurality of devices; and presenting, via a …, the priority associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues.…, are human calculating and determining device having issues, normalizing issues’ statistical variables to determine priorities of issues and instructing humans to address device issues based on priorities, which are fundamental economic principles or practices, managing personal behavior or relationships or interactions between people, therefore the claims, are directed to certain methods of organizing human activities.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 12: A system, comprising: a plurality of devices in a plant; one or more sensors, a computing device processor configured to, user interface
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…obtaining…”, “…presenting…”, “…dynamically adjust…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…obtaining…”, data output – “…presenting…”, “…dynamically adjust…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[00103] Multiple computing devices can be deployed in implementing the disclosed systems and methods. Computing devices include one or more: computing device processors, memories, storage devices, high-speed interfaces connecting to memory and high-speed expansion ports, and low speed interfaces connecting to low speed bus and storage device. Each of the components of the one or more computing devices can also be interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. Processor can process instructions for execution within computing device, including instructions stored in memory or on storage device to display graphical data for a GUI on an external input/output device, including, e.g., each computing device can include a display coupled to high speed interface. In other implementations, multiple processors and/or multiple busses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices can be connected, with each device providing portions of the operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
[00107] Computing device includes processor, memory, an input/output device (e.g., display, communication interface, and transceiver) among other components. Device also can be provided with a storage device, (e.g., a microdrive or other device) to provide additional storage. Each of the devices, processor, display, memory, communication interfaces, and transceiver, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.
[00108] A processor can execute instructions within computing device, including instructions stored in memory. The processor can be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor can provide, for example, for coordination of the other components of device, e.g., control of user interfaces, applications run by device, and wireless communication by device.
[00114] Computing device can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as cellular telephone. It also can be implemented as part of smartphone, tablet, a personal digital assistant, or other similar mobile device.
[00115] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor. The programmable processor can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
[00116] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. The programs can use one or more algorithms. As used herein, the terms machine-readable medium and computer-readable medium refer to a computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions.
[00117] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a device for displaying data to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor), and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be a form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in a form, including acoustic, speech, or tactile input.
[00118] The systems and techniques described here can be implemented in a computing system that includes a backend component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a frontend component (e.g., a client computer having a user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or a combination of such back end, middleware, or frontend components. The components of the system can be interconnected by a form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
[00119] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
[00120] In some implementations, the engines described herein can be separated, combined or incorporated into a single or combined engine. The engines depicted in the figures are not intended to limit the systems described here to the software architectures shown in the figures. Components of the system can be distributed by short, medium, and long distances depending on the location of the target under measurement. In some configurations the devices, such as measurement devices, operate asynchronously and capture data locally and then transit/retransmit when a signal is detected.
[00121] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that any claims presented define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20090043539A1 to Frank et al., (hereinafter referred to as “Frank”) in view of US Patent Publication to US20210088563A1 to Yan et al., (hereinafter referred to as “Yan”) in view of US Patent Publication to US20240362240A1 to Chakraborty., (hereinafter referred to as “Chakraborty”)
As per Claim 1, Frank teaches: A process for identifying improvements in a plant, comprising:
obtaining operating data associated with a plurality of devices in the plant; (in at least [0027] In step 210, the method 200 may receive a plurality of operating data 120 from at least one power plant machine 110 (not illustrated in FIG. 2). An embodiment of the present invention may allow for receiving the plurality of operating data 120 from multiple power plant machines.)
performing, using one or more algorithms, at least one calculation relative to the operating data; (in at least [0030] In step 230, the method 200 may apply at least one analysis engine to the plurality of operating data corresponding to the performance indicators. Generally, the analysis engine may evaluate, in real-time, the status of at least one performance indicator. The evaluation may determine whether or not the at least one performance indicator is within a specified range. The performance indicators may include: power input, power output, fuel flow, airflow, fluid flow, and other operating data that may be used to directly or indirectly evaluate the performance of the power plant machine 110.)
normalizing the at least one calculation to obtain normalized values; (in at least [0031] an operator may select the signal name for power plant output (DWATT, or the like) as the performance indicator. Here, the method 200, in step 230 may apply the at least one analysis engine to the plurality of operating data 120 corresponding to the DWATT signal. As discussed in FIG. 3, the analysis engine may organize, filter, and normalize the plurality of operating data 120 for DWATT.)
identifying, using the normalized values, one or more issues associated with one or more of the plurality of devices; (in at least [0033] In step 240, the method 200 may determine whether or not at least one rule of a plurality of rules is met. Each rule of the plurality of rules may be associated with a specific performance related issue. For example, but not limiting of, a rule may be associated with a DWATT signal. Here, if the DWATT signal is not within a specified range, the rule may be met. An embodiment of the present invention may allow for a third-party support expert, or the like, to define or modify each rule of the plurality rules. An alternate embodiment of the present invention may provide for the operator of the power plant machine 110 to define or modify each rule of the plurality rules. An embodiment of the present invention may utilize at least one math engine to determine whether the performance indicator may be within the specified range. The math engine may also perform a plurality of statistically tests, including: normality testing; SPC rules or the like; confidence intervals; etc.)
…; and
presenting, via a user interface, the … associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues. (in at least [0036] In step 260, the method 200 may automatically generate an operator notification of a potential performance issue. The operator notification may inform the operator of the power plant machine 110 of a plurality of operating conditions related to the potential performance issue. The operator notification may also provide recommendations for investigating the performance issue. For example, but not limiting of, an operator notification informing the operator of an issue with the DWATT signal may provide a recommendation on how to determine whether the issue may be a fault with the DWATT signal or a true performance issue with the power plant machine 110. [0037] In step 270, the method 200 may automatically notify a support system of the potential performance issue. The support system may include a performance expert. The expert may analyze the plurality of operating data 120 to develop a root cause analysis, or the like, of the performance issue. The support system may be a third-party service of which the operator of the power plant machine 110 subscribes. For example, but not limiting of, the support system may be provided by the original equipment manufacturer (OEM), or the like.)
Although implied, Frank does not expressly disclose the following limitations, which however, are taught by Yan,
determining, using the normalized values, a … associated with each of the plurality of devices (in at least [0057] Signature identification module 240 may rank and select signatures for use in detected power system events based on the residuals and residual statistics. For example, signatures may be ranked based on how well they model data. In one particular embodiment, the signatures may be directly based on the residuals generated by residual generation module 235. In another embodiment, the statistics obtained from residual analysis module 235 may be used to normalize the generated residuals, in which case the signatures may be ranked based on the normalized residuals [0066] Signature identification module 355 may rank and select signatures for use in detected power system events. For example, signatures may be ranked based on how well they model data. In one particular embodiment, the signatures may be directly based on the residuals generated by residual generation module 350. In another embodiment, the statistics obtained from residual analysis module 340 may be used to normalize the generated residuals, in which case the signatures may be ranked based on the normalized residuals)
presenting, via a user interface, the … associated with each of the plurality of devices and at least one action for a user to undertake to address the one or more issues. (in at least [0045] a PMU based event detection and diagnosis be as fast as possible or (sub-second level) be able to take remedy actions for certain failures, such as s bushing failure [0059] FIG. 3 illustrates an embodiment 300 a system diagram of a signature identification system 310 and corresponding inputs 305 and output 315 according to an embodiment. As illustrated, various inputs may include PMU data (30-60 Hz), SCADA data (e.g., at 2-4 seconds), weather data, DGA data, and PD monitor data, for example. PMU data may include three phase current magnitude, three phase current phase angle, three phase voltage magnitude, three phase voltage phase angle, frequency, and frequency delta, for example. SCADA data may include voltage magnitude, current magnitude, transformer (Xfmr) tap position, digital inputs (e.g., circuit breaker (CB) status), and digital outputs (e.g., trips/alarms) [0086] a server may implement signature identification module 916. In this example, the signature identification module 916 may monitor the health of one or more assets of a power grid system and/or of the grid itself. In some embodiments, the signature identification module 916 may also store and display asset health history for one or more assets and/or of the grid itself and a variety of other statistical information related to disturbances and events, including on a graphical user interface, or in a generated report)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Frank as taught by Yan, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Frank with the motivation of, …An asset monitoring system may be improved in accordance with an embodiment, for example, to provide an automatic solution (e.g., software) to correlate PMU captured event data to determine a status of an asset or equipment based on a detected event signature. PMU data may be analyzed to provide a relatively fast diagnosis (e.g., at a subsecond level) to avoid more severe equipment failure or explosion…improved detection performance by maximally leveraging information from both conventional sensor data (e.g., sensor measurements from gas turbines) and unconventional data through multi-modal, multi-disciplinary feature discovery 650…provide improved visibility into dynamic grid conditions and/or of asset health and may allow for real-time wide area monitoring of power system and/or asset health dynamics…, as recited in Yan.
Although implied, Frank in view of Yan does not expressly disclose the following limitations, which however, are taught by Chakraborty,
…priority… (in at least [0078] an insight may be interpreted as a recommendation for one or more actions to take, or not take, based on an analysis of at least the sensor data. A recommendation may be a predefined course of action that is selected based on the comparison of at least the sensor data to one or more predefined rule sets. In one embodiment, the result of a comparison of one or more portions of at least the sensor data to a rule set may determine the course of action. As discussed below, when one or more rule sets determine multiple courses of action, the courses of action may be ranked by priority (e.g., according to the course of action, the type of emergency, or rule set corresponding to the recommended course of action). Specifically, the insight should be understood as being a transformation of data collected from a plurality of sensors over a predetermined time frame to a concrete recommendation for a course of action based on an analysis of the collected data through the use of one or more rule sets.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Frank in view of Yan as taught by Chakraborty, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Frank in view of Yan with the motivation of, …implementing an improved insight and intuition generation process through the use of aggregating multiple data sources for use with predictive analytics and preventive models. One goal of embodiments of the present invention is, using an aggregation of collected data, obtaining improved, reliable and accurate insights, forecasts and recommendations for taking current action regarding, among others, commercial decisions. Using the insights and/or intuition outputs of an intuition generator system (IGS) 100, a course of action pertaining to a business or personal decision may be recommended. The following description begins with an application of the intuition generation process to the oil and gas industry as a primary example. However, the ideas and inventive aspects portrayed in the examples may be applied to other industries (e.g., nuclear energy plants, recycling plants, etc.), commercial ventures or personal motives, and are described in a later portion of this disclosure as applied to techniques for identifying a truth-telling data population in a data stream…Upon completion of historical even analysis, hypotheses outcomes can be refined to improve identifying conditions that can signal a target event…determine new machine learning models may need updated or improvement…conditions and thresholds of the outputs can be further refined accordingly in which forewarning hypotheses can be improved to map to various situation conditions that are experienced.…provide techniques in generating real time forewarning of contamination level of the oil sample and guide the engineers of the oil rig with advanced notice of lead time to collect decontaminated fluid, which can save probe operations and cost and improve operations.…provide an improved fitting of one or more trends 1606 to the data set…., as recited in Chakraborty.
As per Claim 2, Frank teaches: The process of claim 1, further comprising:
identifying one or more batch events using the operating data; and (in at least [0039] In step 320, the method 300 may organize the operating data 120 to allow for further processing. The organizing may include arranging the unprocessed, or raw, plurality of operating data 120 into a format allowing for averaging, or other mathematical processing. For example, but not limiting of, in step 320, the raw data may be segmented into blocks, or the like, whereby each block corresponds to a distinct performance indicator. [0040] In step 330, the method 300, may determine whether or not the received plurality of operating data 120 is approximately at a “steady state”. Steady state refers to an operating condition where the power plant machine 110 may be experiencing minimal mechanical, electrical, chemical, or thermal transients. The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period.)
obtaining additional data associated with the operating data, wherein the at least one calculation is performed relative to the operating data, the additional data, and the one or more batch events. (in at least [0041] After step 330 determines that the plurality of operating data may be approximately at a steady state, the method 300 may apply at least one averaging method to the plurality of operating data 120. The averaged plurality of operating data 120 may then be transmitted to step 340 for further processing. This averaging method may increase the accuracy of the analysis engine 230 by possibly ensuring the uniformity of the data. The averaging method may include for example, but not limiting of, calculating 10 minutes averages of the raw plurality of operating data 120 and then selecting one data point every 5 minutes from the 10-minute averaged data. If the plurality of operating data 120 is approximately at a steady state, then the method 300 may proceed to step 340; otherwise the method 300 may revert to step 310. [0042] In step 340, the method 300 may generate a plurality of pseudo indicators. A pseudo indicator may be considered any calculated parameter that may not be directly measured; such as, but not limiting of: corrected performance parameters, intermediate flow calculations, or the like. For example, but not limiting oft in a gas turbine, the compressor discharge pressure (CPD) is a performance indicator. The value of CPD may be used to calculate the pseudo indicator CPD_ABS (compressor discharge pressure accounting for a giving barometric pressure).)
As per Claim 3, Frank teaches: (Currently Amended) The process of claim 2, wherein the additional data is one or more of:
a type of the operating data, an importance of the operating data, an identification of redundancies in the operating data, an identification of a range for the operating data associated with an operational state, an identification of a range for the operating data associated with a shutdown state, a purpose associated with the plurality of devices, and a purpose associated with the plant. (in at least [0041] After step 330 determines that the plurality of operating data may be approximately at a steady state, the method 300 may apply at least one averaging method to the plurality of operating data 120. The averaged plurality of operating data 120 may then be transmitted to step 340 for further processing. This averaging method may increase the accuracy of the analysis engine 230 by possibly ensuring the uniformity of the data. The averaging method may include for example, but not limiting of, calculating 10 minutes averages of the raw plurality of operating data 120 and then selecting one data point every 5 minutes from the 10-minute averaged data. If the plurality of operating data 120 is approximately at a steady state, then the method 300 may proceed to step 340; otherwise the method 300 may revert to step 310. [0042] In step 340, the method 300 may generate a plurality of pseudo indicators. A pseudo indicator may be considered any calculated parameter that may not be directly measured; such as, but not limiting of: corrected performance parameters, intermediate flow calculations, or the like. For example, but not limiting oft in a gas turbine, the compressor discharge pressure (CPD) is a performance indicator. The value of CPD may be used to calculate the pseudo indicator CPD_ABS (compressor discharge pressure accounting for a giving barometric pressure).)
As per Claim 4, Frank teaches: The process of claim 2,
wherein the batch events are arranged in one or more batch groups. (in at least [0039] In step 320, the method 300 may organize the operating data 120 to allow for further processing. The organizing may include arranging the unprocessed, or raw, plurality of operating data 120 into a format allowing for averaging, or other mathematical processing. For example, but not limiting of, in step 320, the raw data may be segmented into blocks, or the like, whereby each block corresponds to a distinct performance indicator.)
As per Claim 5, Frank teaches: The process of claim 4, wherein the arrangement of the batch events into the one or more batch groups is based on at least one of:
a start time associated with the batch events; an end time associated with the batch events; a combination of the start time and the end time; an adherence of the operating data to a set of preferred operating data; and an adherence of the operating data to a preferred archetype, where the preferred archetype is described by an aggregate approximation method. (in at least [0039] In step 330, the method 300, may determine whether or not the received plurality of operating data 120 is approximately at a “steady state”. Steady state refers to an operating condition where the power plant machine 110 may be experiencing minimal mechanical, electrical, chemical, or thermal transients. The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period.)
As per Claim 6, Frank teaches: The process of claim 2,
wherein normalizing the at least one calculation comprises using one or more … values of the at least one calculations obtained from the one or more batch events. (in at least [0040] The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period. [0044] In step 360, the method 300 may apply at least one normalization engine. A normalization engine may determine the performance of a power plant machine 110 under a standard reference model, or the like (typically ISO conditions). The present invention may provide a specific normalization engine for a specific type of power plant machine. For example, but not limiting of, the present invention may include a separate normalization engine for a gas turbine, a steam turbine, and the like; and combinations thereof.)
Although implied, Frank in view of Yan does not expressly disclose the following limitations, which however, are taught by Chakraborty,
…percentile… (in at least [0172] to obtain homogeneity across historical data runs, data values can be normalized and expressed in the percentile of the maximum value of that variable in that historical data series. Changing relevance scores can be tracked and stored in database 919 at intuition platform 915. In this way, a set of hypotheses outcomes can be framed to provide forewarning conditions to be provided to the presentation layer 950. [0211] before historical event/data analysis, the final event timestamp is tagged to the final event timestamp. The time stamp can be called when a good, decontaminated sample was collected, which was later confirmed by, e.g., lab analysis, as T=0 and then FGS 900 can ingest a fixed length back window, e.g., 1 hour for each time series to isolate abnormal patterns from core and data. In this example, if a 1 hour back window is chosen, FGS 900 can scan from T=−3600 seconds to T=0. However, for one embodiment, before lead time generator 911 performs lead time training algorithm, each time series data can be properly prepared by normalizing and expressing in percentiles so that observed patterns can be compared. [0216] FIGS. 15A-15C provides another numerical example showing a Historical Data Analysis Table 1500, Isolated Patterns Table 1510, and Hypotheses Table 1520. Referring to FIG. 15A, Table 1500 shows a historical analysis of core variables C1 and C2, ring variables R1 and associated patterns for C1 and C2. The values for the C1 and C2 and R1 variables refer to normalized and percentile values.)
The reason and rationale to combine Frank, Yan, Chakraborty is the same as recited above.
As per Claim 7, Frank teaches: The process of claim 2,
wherein the one or more batch events are normalized on a time basis. (in at least [0041] After step 330 determines that the plurality of operating data may be approximately at a steady state, the method 300 may apply at least one averaging method to the plurality of operating data 120. The averaged plurality of operating data 120 may then be transmitted to step 340 for further processing. This averaging method may increase the accuracy of the analysis engine 230 by possibly ensuring the uniformity of the data. The averaging method may include for example, but not limiting of, calculating 10 minutes averages of the raw plurality of operating data 120 and then selecting one data point every 5 minutes from the 10-minute averaged data. If the plurality of operating data 120 is approximately at a steady state, then the method 300 may proceed to step 340; otherwise the method 300 may revert to step 310. [0044] In step 360, the method 300 may apply at least one normalization engine. A normalization engine may determine the performance of a power plant machine 110 under a standard reference model, or the like (typically ISO conditions). The present invention may provide a specific normalization engine for a specific type of power plant machine. For example, but not limiting of, the present invention may include a separate normalization engine for a gas turbine, a steam turbine, and the like; and combinations thereof.)
As per Claim 8, Frank teaches: The process of claim 2,
wherein the one or more batch events are normalized on a value basis. (in at least [0040] In step 330, the method 300, may determine whether or not the received plurality of operating data 120 is approximately at a “steady state”. Steady state refers to an operating condition where the power plant machine 110 may be experiencing minimal mechanical, electrical, chemical, or thermal transients. The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period. [0044] In step 360, the method 300 may apply at least one normalization engine. A normalization engine may determine the performance of a power plant machine 110 under a standard reference model, or the like (typically ISO conditions). The present invention may provide a specific normalization engine for a specific type of power plant machine. For example, but not limiting of, the present invention may include a separate normalization engine for a gas turbine, a steam turbine, and the like; and combinations thereof.)
As per Claim 9, Frank teaches: The process of claim 1, wherein normalizing the at least one calculation comprises
using one or more … values of the at least one calculations obtained from the operating data. (in at least [0030] In step 230, the method 200 may apply at least one analysis engine to the plurality of operating data corresponding to the performance indicators. Generally, the analysis engine may evaluate, in real-time, the status of at least one performance indicator. The evaluation may determine whether or not the at least one performance indicator is within a specified range. The performance indicators may include: power input, power output, fuel flow, airflow, fluid flow, and other operating data that may be used to directly or indirectly evaluate the performance of the power plant machine 110. [0040] In step 330, the method 300, may determine whether or not the received plurality of operating data 120 is approximately at a “steady state”. Steady state refers to an operating condition where the power plant machine 110 may be experiencing minimal mechanical, electrical, chemical, or thermal transients. The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period. [0044] In step 360, the method 300 may apply at least one normalization engine. A normalization engine may determine the performance of a power plant machine 110 under a standard reference model, or the like (typically ISO conditions). The present invention may provide a specific normalization engine for a specific type of power plant machine. For example, but not limiting of, the present invention may include a separate normalization engine for a gas turbine, a steam turbine, and the like; and combinations thereof.)
Although implied, Frank in view of Yan does not expressly disclose the following limitations, which however, are taught by Chakraborty,
…percentile… (in at least [0172] to obtain homogeneity across historical data runs, data values can be normalized and expressed in the percentile of the maximum value of that variable in that historical data series. Changing relevance scores can be tracked and stored in database 919 at intuition platform 915. In this way, a set of hypotheses outcomes can be framed to provide forewarning conditions to be provided to the presentation layer 950. [0211] before historical event/data analysis, the final event timestamp is tagged to the final event timestamp. The time stamp can be called when a good, decontaminated sample was collected, which was later confirmed by, e.g., lab analysis, as T=0 and then FGS 900 can ingest a fixed length back window, e.g., 1 hour for each time series to isolate abnormal patterns from core and data. In this example, if a 1 hour back window is chosen, FGS 900 can scan from T=−3600 seconds to T=0. However, for one embodiment, before lead time generator 911 performs lead time training algorithm, each time series data can be properly prepared by normalizing and expressing in percentiles so that observed patterns can be compared. [0216] FIGS. 15A-15C provides another numerical example showing a Historical Data Analysis Table 1500, Isolated Patterns Table 1510, and Hypotheses Table 1520. Referring to FIG. 15A, Table 1500 shows a historical analysis of core variables C1 and C2, ring variables R1 and associated patterns for C1 and C2. The values for the C1 and C2 and R1 variables refer to normalized and percentile values.)
The reason and rationale to combine Frank, Yan, Chakraborty is the same as recited above.
As per Claim 10, Frank teaches: The process of claim 1,
wherein the identifying the issue is further determined based on a length of time which the normalized values satisfy a threshold value. (in at least [0033] In step 240, the method 200 may determine whether or not at least one rule of a plurality of rules is met. Each rule of the plurality of rules may be associated with a specific performance related issue. For example, but not limiting of, a rule may be associated with a DWATT signal. Here, if the DWATT signal is not within a specified range, the rule may be met. An embodiment of the present invention may allow for a third-party support expert, or the like, to define or modify each rule of the plurality rules. An alternate embodiment of the present invention may provide for the operator of the power plant machine 110 to define or modify each rule of the plurality rules. An embodiment of the present invention may utilize at least one math engine to determine whether the performance indicator may be within the specified range. The math engine may also perform a plurality of statistically tests, including: normality testing; SPC rules or the like; confidence intervals; etc. [0040] In step 330, the method 300, may determine whether or not the received plurality of operating data 120 is approximately at a “steady state”. Steady state refers to an operating condition where the power plant machine 110 may be experiencing minimal mechanical, electrical, chemical, or thermal transients. The method 300 may utilize at least one calculation, such as averaging, to determine whether or not the plurality of operating data 120 is approximately at a steady state. For example, but not limiting of, the plurality of operating data 120 may be considered approximately at a steady state if the values in data does not fluctuate outside of a ±5% band over a 10 minute period.)
As per Claim 11, Frank teaches: The process of claim 1, wherein the at least one action comprises one or more of:
one or more investigation steps to identify a root cause of the issue; one or more improvement paths for the user to reduce an effect associated with the issue; a prioritization of the one or more investigation steps; a prioritization of the one or more improvement paths; a first estimate of an invasiveness of the one or more investigation steps on the plant or the plurality of devices; a second estimate of the invasiveness of the one or more improvement paths on the plant or the plurality of devices; a third estimate of a financial impact of the one or more investigation steps on the plant or the plurality of devices; and a fourth estimate of the financial impact of the one or more improvement paths on the plant or the plurality of devices. (in at least [0036] In step 260, the method 200 may automatically generate an operator notification of a potential performance issue. The operator notification may inform the operator of the power plant machine 110 of a plurality of operating conditions related to the potential performance issue. The operator notification may also provide recommendations for investigating the performance issue. For example, but not limiting of, an operator notification informing the operator of an issue with the DWATT signal may provide a recommendation on how to determine whether the issue may be a fault with the DWATT signal or a true performance issue with the power plant machine 110. [0037] In step 270, the method 200 may automatically notify a support system of the potential performance issue. The support system may include a performance expert. The expert may analyze the plurality of operating data 120 to develop a root cause analysis, or the like, of the performance issue. The support system may be a third-party service of which the operator of the power plant machine 110 subscribes. For example, but not limiting of, the support system may be provided by the original equipment manufacturer (OEM), or the like.)
As per Claim 13, Frank teaches: The system of claim 12, wherein the computing device processor is further configured to ([0016][0022][0027]-[0030])
dynamically adjust the at least one action based on additional calculations performed on additionally obtained operating data. (in at least [0036] In step 260, the method 200 may automatically generate an operator notification of a potential performance issue. The operator notification may inform the operator of the power plant machine 110 of a plurality of operating conditions related to the potential performance issue. The operator notification may also provide recommendations for investigating the performance issue. For example, but not limiting of, an operator notification informing the operator of an issue with the DWATT signal may provide a recommendation on how to determine whether the issue may be a fault with the DWATT signal or a true performance issue with the power plant machine 110. [0037] In step 270, the method 200 may automatically notify a support system of the potential performance issue. The support system may include a performance expert. The expert may analyze the plurality of operating data 120 to develop a root cause analysis, or the like, of the performance issue. The support system may be a third-party service of which the operator of the power plant machine 110 subscribes. For example, but not limiting of, the support system may be provided by the original equipment manufacturer (OEM), or the like.)
As per Claim 12, 14-20 for a system (see at least Frank [0016][0022][0027]-[0030]), respectively, substantially recite the subject matter of Claim 1-3, 5, 7-9, 11 and are rejected based on the same reasoning and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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