DETAILED ACTION
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 .
Priority
This application is a national stage of International Application No. PCT/EP2020/081181, filed November 5, 2020, which claims the benefit of European Application Nos. 19207760.0 and 19207749.3, both filed November 7, 2019, in the European Patent Office.
Status of the Claims
The Amendment filed on 02/12/2026 has been entered. Claims 1-8, 11-13, 16, 21-23 and 25-26 are pending in the instant patent application. Claims 1, 16, 21, 23 and 25-26 are amended. Claims 9-10, 14-15, 17-20 and 24 are cancelled. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to claims are sufficient to overcome the 35 U.S.C. §112 and thus have been withdrawn.
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that the claims do not recite abstract ideas, Examiner respectfully disagrees. Examiner will remind Applicant the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)). Examiner notes that prevalence of generic computing devices performing their generic functions. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea).
Applicant asserts that Example 39 is similar, however the key distinction is Examples 39 was found eligible due to the training limitation not falling into any grouping of abstract ideas because it is not practical for a human mind to train a neural network. The claim language provided does not go into any type of training but rather teaches limitations that can be performed in the mind, with pen/paper and the use of generic computing devices performing generic functions.
Applicant further asserts that the claims as currently written are align with Desjardins, for which the Examiner respectfully disagrees. As noted in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. There were clear improvements noted and further reflected in the claim language. The same cannot be said of the current claims in light of Ex Parte Desjardins.
Examiner will further note in Diamond v Diehr, Diehr utilized the Arrhenius equation to improve the process of controlling the operations of a mold in curing rubber parts In contrast, Examiner finds the present claims to be more similar to concepts identified as abstract by the courts. Furthermore, utilizing generic computer components for a generic application of data analysis and outputting the results still falls within the enumerated grouping of abstract ideas.
Regarding Berkheimer, the Berkheimer Memo states "In a Step 2B analysis, an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, an expressly supports a rejection in writing with, one or more of the following: 1. A citation to express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s). A specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a)..., 2. a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s), 3. A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional elements... and 4. A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s)...". In light of the new requirements under Berkheimer, Examiner has sufficiently identified portions in the specification that describes the additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). Therefore, for at least the above reasons, the claims remain rejected under 35 U.S.C. 101. In addition, Examiner states that while the Applicant has asserted alleged improvements, the claims as currently written do no recite the alleged improvements. Furthermore, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. For in at least these reasons, Examiner maintains the rejections under 35 USC 101. In addition, (ref: 2106.04(d)(1)).
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.
Regarding Claims 1-8, 11-13, 23 and 25, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 1-8, 11-13, 23 and 25 are directed to the abstract idea of risk assessment.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites to perform for each of at least one current condition-state of at least a subset of the current condition states determining a current condition state value for each of the current condition states - comparing each condition-state value and a respective condition-state threshold,- thus generating a comparison result for each of compared current condition-states, and - further correlating each comparison result to the respective current condition-state, and generating a condition-node modifier for each of at least one condition-nodes of at least a subset of the condition-nodes by aggregating the comparison results of the current condition- states of at least the subset of the current condition- states which current condition-states are assigned to the respective condition-node, and stores at least one set of pre-calculated condition-state values generated at least in part by using predetermined values not collected which are anticipated to occur and which are associated with anticipated problem conditions which can be compared to the current condition-state values and at least one set of pre- calculated condition- nodes such that if the current condition-state values are determined to match one of the sets of pre-calculated condition-state values, the pre-calculated condition- nodes are immediately applied; assess a risk of failure based upon application of the pre-calculated condition-state nodes; adjust functioning to reduce the risk of failure based upon application of the pre- calculated condition state nodes; activate a warning device which will warn a user if the risk of failure exceeds a warning threshold, wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit, wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the upper condition-state value limit, the condition-state value is set to the upper condition-state value limit, and wherein configured for storing a plurality of condition- nodes and wherein storing the plurality of the condition-nodes comprises storing the plurality of the condition-nodes, storing directed node-links connecting the condition-nodes, wherein the directed node-links represent causal relationships between mechanical failure conditions.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion). Examiner will further note that the comparing, correlating, assigning, generating and aggregating aspects of the claim can be practically performed by a human in their mind or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 2-8, 11-13, 23 and 25 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a mechanical system, a first mechanical subsystem, a second mechanical subsystem, at least one first sensor, at least one second sensor, a first data processing system, at least one second data processing system, a processing component, graph database and a data storage component. The mechanical system, a first mechanical subsystem, a second mechanical subsystem, at least one first sensor, at least one second sensor, a first data processing system, at least one second data processing system, a processing component, graph database and a data storage component are merely generic computing devices.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 and 8 includes various elements that are not directed to the abstract idea under 2A. These elements include mechanical system, a first mechanical subsystem, a second mechanical subsystem, at least one first sensor, at least one second sensor, a first data processing system, at least one second data processing system, a processing component, a data storage component, at least one user interface, a third data processing system, graph database and the generic computing elements described in the Applicant's specification in at least Pg 2. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 1 and 8, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 16, 21-22 and 26, they are directed to an apparatus, however the claims are directed to a judicial exception without significantly more. Claims 16, 21-22 and 26 are directed to the abstract idea of risk assessment.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 16, claim 16 recites to store both at least one measurement and at least one condition-state threshold and an instruction, storing a plurality of condition-nodes and at least one condition-state, the plurality of condition-nodes generated at least in part by using predetermined values not collected which are anticipated to occur and which are associated with anticipated problem conditions, the condition-states corresponding to operating parameters wherein each condition-state is assigned to at least one condition-node, and wherein each condition-state comprises - a condition-state threshold and - a condition-state value- storing at least one node-link, wherein each node-link connects two condition-nodes and each node-link is directed, - and wherein the directed node-links represent causal relationships between mechanical failure condition, - comparing the respective condition-state value of at least a subset of the condition- states and the respective condition-state threshold and thus generating a respective comparison result,- assigning the comparison result to the respective condition-state,- aggregating for each condition-node of the subset of the condition nodes the comparison results of the condition-states of the subset of the condition-states relating to the respective condition-node and thus generating a condition-node modifier for the respective condition-node,- storing at least one set of pre-calculated condition-state values and/or at least one set of pre-calculated comparison results which can be compared to the condition-state values or the comparison results respectively, and- storing at least one set of pre-calculated condition-node values such that if the current condition-state values are determined to match one of the sets of pre-calculated condition-state values and/or the pre-calculated comparison results are determined to match the comparison results, the pre-calculated condition-node values are immediately applied;- assessing a risk of failure based upon application of the pre- calculated condition-state nodes;- adjusting to reduce the risk of failure if the risk of failure exceeds a predetermined risk threshold; activating a warning device which will warn a user if the risk of failure exceeds a warning threshold.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and claims 21-22 and 26 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a first data processing unit, a second data processing unit, a processor, a memory, a fixed storage system, an input system, an output system, first sensor, second sensor, a first mechanical subsystem, a second mechanical subsystem, and data processing system. The first data processing unit, a second data processing unit, a processor, a memory, a fixed storage system, an input system, an output system, first sensor, second sensor, a first mechanical subsystem, a second mechanical subsystem, and data processing system are merely generic computing devices.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 16 includes various elements that are not directed to the abstract idea under 2A. These elements include a first data processing unit, a second data processing unit, a processor, a memory, a fixed storage system, an input system, an output system, first sensor, second sensor, a first mechanical subsystem, a second mechanical subsystem, data processing system and the generic computing elements described in the Applicant's specification in at least Pg 2. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 16 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
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 (i.e., changing from AIA to pre-AIA ) 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8, 11-13, 23 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tankersley et al. (US 2017/0083572 A1) in view of Boe et al. (US 9,130,860 B1) in view of Do et al. (US 2021/0072733 A1) in view of Ata et al. (US 2022/0043148 A1) further in view of Cheng et al. (US 2018/0307994 A1).
Regarding Claim 1, Tank teaches the limitations of Claim 1 which state
a mechanical system having a first mechanical subsystem monitored by at least one first sensor and a second mechanical subsystem monitored by at least one second sensor, each mechanical subsystem having at least one mechanical condition associated with the first or second sensor that can be expressed as a condition-state (Tankersley: Para 0263, 0269, 0494, 1445 via A triggering condition can be applied to the data produced by the search query to determine whether the produced data satisfies the triggering condition. Using the above example, the triggering condition can be applied to the produced KPI data to determine whether the number of occurrences of a KPI reaching a certain threshold over a specified period of time exceeds a value in the triggering condition. If the produced data satisfies the triggering condition, a particular action can be performed...FIG. 1 illustrates a block diagram of an example service provided by entities, in accordance with one or more implementations of the present disclosure. One or more entities 104A, 104B provide service 102. An entity 104A, 104B can bea component in an IT environment. Examples of an entity can include, and are not limited to a host machine, a virtual machine, a switch, a firewall, a router, a sensor, etc. For example, the service 102 may be a web hosting service, and the entities 104A, 104B may be web servers running on one or more host machines to provide the web hosting service. In another example, an entity could represent a single process on different (physical or virtual) machines. In another example, an entity could represent communication between two different machines... Processing for the method illustrated by FIG. LOAF that supports, for example, automatic entity definition for a service monitoring system begins at block 10110. At block 10110, machine data is received from a number of machine entities, each a data source, and processed for storage in a machine data store 10144. The types of machines or entities from which block 10110 may receive machine data are wide and varied and may include computers of all kinds, network devices, storage devices, virtual machines, servers, embedded processors, intelligent machines, intelligent appliances, sensors, telemetry, and any other kind or category of data generating device as may be discussed within this document or appreciated by one of skill in the art. The machine data may be minimally processed before storage and may be organized and stored as a collection of timestamped events...performance data is stored as “events,” wherein each event comprises a collection of performance data and/or diagnostic information that is generated by a computer system and is correlated with a specific point in time. Events can be derived from “time series data,” wherein time series data comprises a sequence of data points (e.g., performance measurements from a computer system)... The data generated by such data sources can be produced in various forms including, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements and sensor measurements).
However, it does not explicitly disclose the limitation of Claim 1 which states a first data-processing system and at least one second data-processing system, a processing component, and data- storage component.
Boe though, with the teachings of Tankersley, teaches of
a first data-processing system and at least one second data-processing system, a processing component, and data- storage component (Boe: Col 85 lines 55-67 via Processing device 7802 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 7802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 7802 may also be one or more special- purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley with the teachings of Boe, in order to have a first data-processing system and at least one second data-processing system, a processing component, and data- storage component. The motivations behind this being to incorporate the teachings of system monitoring using KPIs. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Tankersley/Boe further teaches the limitations of Claim 1 which state
which can store both at least one measurement from at least one of the first or second sensors and at least one condition-state threshold associated with the measurement from the first or second sensors (Boe: Col 7 lines 37-48 and Col 42 line 57 - Col 43 line 17 via Implementations of the present disclosure are described for customizing various states that a KPI can be in. For example, a user may define a Normal state, a Warning state, and a Critical state for a KPI, and the value produced by the search query of the KPI can indicate the current state of the KPI. In one implementation, one or more thresholds are created for each KPI. Each threshold defines an end of a range of values that represent a particular state of the KPI. A graphical interface can be provided to facilitate user input for creating one or more thresholds for each KPI, naming the states for the KPI, and associating a visual indicator (e.g., color, pattern) to represent a respective state... At block 3418, the computing machine compares the score for the aggregate KPI to one or more thresholds. As discussed above with respect to FIG. 33B, one or more thresholds can be defined and can be configured to apply to a specific individual KPI and/or an aggregate KPI including the specific individual KPI. The thresholds can be stored in a data store that is coupled to the computing machine. If the thresholds are configured to be applied to the aggregate KPI, the computing machine compares the score of the aggregate KPI to the thresholds. If the computing machine determines that the aggregate KPI score exceeds or reaches any of the thresholds, the computing machine determines what action should be triggered in response to this comparison. Referring to FIG. 34, at block 3420, the computing machine causes an action be performed based on the comparison of the aggregate KPI score with the one or more thresholds. For example, the computing machine can generate an alert if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the highest threshold). In another example, the computing machine can generate a notable event if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the second highest threshold). In one implementation, the KPIs of multiple services is aggregated and used to create a notable event. In one implementation, the configuration for which of one or more actions to be performed is received as input (e.g., user input), stored in a data store coupled to the computing machine, and accessed by the computing machine);
wherein the system is configured to perform for each condition-state of at least a subset of the condition states determining a current condition state value for each of the current condition states and then - comparing each condition-state value and a respective condition-state threshold, - thus generating a comparison result for each of compared current condition-states, and - further correlating each comparison result to the respective current condition-state and wherein the system is further configured for generating a condition-node modifier for each of at least one condition-nodes of at least a subset of the condition-nodes by aggregating the comparison results of the current condition-states of at least the subset of the current condition-states which current condition-states are assigned to the respective condition-node (Boe: Col 7 lines 37- 48, Col 42 line 57 - Col 43 line 17 via Implementations of the present disclosure are described for customizing various states that a KPI can be in. For example, a user may define a Normal state, a Warning state, and a Critical state for a KPI, and the value produced by the search query of the KPI can indicate the current state of the KPI. In one implementation, one or more thresholds are created for each KPI. Each threshold defines an end of a range of values that represent a particular state of the KPI. A graphical interface can be provided to facilitate user input for creating one or more thresholds for each KPI, naming the states for the KPI, and associating a viSual indicator (e.g., color, pattern) to represent a respective state...At block 3418, the computing machine compares the score for the aggregate KPI to one or more thresholds. As discussed above with respect to FIG. 33B, one or more thresholds can be defined and can be configured to apply to a specific individual KPI and/or an aggregate KPI including the specific individual KPI. The thresholds can be stored in a data store that is coupled to the computing machine. If the thresholds are configured to be applied to the aggregate KPI, the computing machine compares the score of the aggregate KPI to the thresholds. If the computing machine determines that the aggregate KPI score exceeds or reaches any of the thresholds, the computing machine determines what action should be triggered in response to this comparison...the computing machine causes an action be performed based on the comparison of the aggregate KPI score with the one or more thresholds. For example, the computing machine can generate an alert if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the highest threshold). In another example, the computing machine can generate a notable event if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the second highest threshold). In one implementation, the KPIs of multiple services is aggregated and used to create a notable event. In one implementation, the configuration for which of one or more actions to be performed is received as input (e.g., user input), stored in a data store coupled to the computing machine, and accessed by the computing machine). Wherein the KPI result serves as the condition-state value, the defined thresholds serve as the condition-state threshold, the various KPI states serve as the condition states and the KPIs of multiple services to create a notable event serves as the condition-node modifier);
wherein the system is further configured for generating a condition-node modifier for each of at least one condition-nodes of at least a subset of the condition-nodes by aggregating the comparison results of the current condition- states of at least the subset of the current condition- states which current condition-states are assigned to the respective condition-node, and wherein the data-storage component stores at least one set of pre-calculated condition-state values generated at least in part by using predetermined values not collected by the system which are anticipated to occur and which are associated with anticipated problem conditions which can be compared to the current condition-state values and at least one set of pre-calculated condition- nodes such that if the current condition-state values are determined to match one of the sets of pre-calculated condition-state values, the pre-calculated condition- nodes are immediately applied by the system (Tankersley: Para 0772-0773 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non-working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.sup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique...the computing machine may cause the different sets of KPI thresholds to be available for determining a KPI state (e.g., at a later time). This may involve storing the sets of KPI thresholds in a data structure or data store that may be accessible by the machine determining the states of the KPIs. In one example, a client device may be used to set the KPI threshold values and another machine (e.g., server machine) may evaluate the KPI values to determine the state of the KPI. In other examples, any device may be used to define the sets of KPI thresholds. In some implementations, the different sets of KPI thresholds are stored as part of the service definition (e.g., in the same database or file), or in association with the service definition (e.g., in a separate database or file). Using the example illustrated in FIG. 17B, different sets of KPI thresholds can be stored in a service definition structure 1720 as part of a KPI component 1727);
wherein the system is further configured to assess a risk of failure of the mechanical system based upon application of the pre-calculated condition-state nodes (Tankersley: Para 0980 via Weight adjustment display component 34330 may display the KPIs selected by the user and may provide a mechanism for the user to adjust the weights of the KPIs and display a resulting aggregate KPI value. Weight adjustment display component 34330 may include aggregate KPI value 34332, weights 34334A-C and graphical control elements 34336A-C. Aggregate KPI value 34332 may be a numeric value (e.g., score), non-numeric value, alphanumeric value, symbol, or the like, that may characterize the performance of one or more services. In one example, the aggregate KPI value 34332 may be used to detect a pattern of activity or diagnose abnormal activity (e.g., decrease in performance or system failure). Aggregate KPI value 34332 may be determined in view of weights 34334A-C, which may indicate the importance or influence a particular KPI has on a calculation of the aggregate KPI. Weights 34334A-C may be considered when calculating the aggregate KPI value for the services and a KPI with a higher weight may be considered more important or have a larger influence on the aggregate KPI value than other KPIs. The weights of the KPIs may be adjusted by the user by manipulating graphical control elements 34336A-C. Each of graphical control elements 34336A-C may correspond to a specific KPI and may be used to adjust a weight of a specific KPI);
wherein the system is further configured to activate a warning device which will warn a user if the risk of failure exceeds a warning threshold (Tankersley: Para 0872, 1004 via Alert setting control 34697 can be, for example, a selectable button, checkbox, etc., or any other such selectable element or interface item that, upon selection (e.g., by a user) enables a user to select or define whether or not various alerts, notifications, etc. (e.g., email alerts, notable events, etc., as are described herein), are to be generated and/or provided, e.g., upon identification of various anomalies... At block 34910, the processing device may receive a user indication to notify (e.g., alert) the user when the value of the aggregate KPI exceeds a threshold, such as a threshold associated with a critical state. In one example, the user indication may be the result of a user selecting a button to create a correlation search. The alert may be advantageous because it may be configured to identify a pattern of interest to a user and may notify the user when the pattern occurs. In response to receiving the user indication, the method may proceed to 34912).
and wherein the data storage component is configured for storing a plurality of condition- nodes and wherein storing the plurality of the condition-nodes comprises storing the plurality of the condition-nodes in a graph database (Boe: Col 84 lines 16-20, Col 14 lines 47-52 via the SPLUNK.RTM. APP FOR VMWARE.RTM. stores large volumes of minimally processed performance information and log data at ingestion time for later retrieval and analysis at search time when a live performance issue is being investigated…A data store 290 can be a persistent storage that is capable of storing data. A persistent storage can be a local storage unit or a remote storage unit. Persistent storage can be a magnetic storage unit, optical storage unit, solid state storage unit, electronic storage units (main memory), or similar storage unit).
However, the Tankersley/Boe combination does not explicitly disclose the limitation of wherein the system is further configured to adjust the functioning of the mechanical system to reduce the risk of failure based upon application of the pre-calculated condition-state nodes.
Do though, with the teachings of Tankersley/Boe, teaches of
wherein the system is further configured to adjust the functioning of the mechanical system to reduce the risk of failure based upon application of the pre- calculated condition-state nodes (Do: Para 0011 via The systems of the exemplary embodiments can also be configured to update the slope signature/failure mode library through manual or machine learning processes based on measurements and component failure determinations. The systems of the exemplary embodiments can also be configured to automatically adjust the operation of the reciprocating compressor to reduce the risk of failure, to prolong equipment life or to improve operational efficiency).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley/Boe with the teachings of Do in order to have wherein the system is further configured to adjust the functioning of the mechanical system to reduce the risk of failure based upon application of the pre-calculated condition-state nodes. The motivations behind this being to incorporate the teachings of monitoring the health of machines. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
However, the Tankersley/Boe/Do combination does not explicitly disclose the limitation of wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit, wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the upper condition-state value limit, the condition-state value is set to the upper condition-state value limit.
Ata though, with the teachings of Tankersley/Boe/Do, teaches of
wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step $925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated),
wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the upper condition-state value limit, the condition-state value is set to the upper condition-state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step $925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley/Boe/Do with the teachings of Ata, in order to have wherein at least one condition-state has a upper condition-state value limit, and if the condition-state value of the condition-state is higher than the upper condition-state value limit, the condition-state value is set to the upper condition-state value limit. The motivations behind this being to incorporate the teachings of setpoint adjustments. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, the combination does not explicitly disclose the limitation of Claim 1 which states wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems.
Cheng though, with the teachings of Tankersley/Boe/Do/Ata, teaches of
wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems (Cheng: Para 0005, 0037, 0073, 0111 via an aspect of the present invention, a system is provided for identifying multiple causal anomalies in a power plant system having multiple system components. The system includes a processor. The processor is configured to construct an invariant network model having (i) a plurality of nodes, each representing a respective one of the multiple system components and (ii) a plurality of invariant links, each representing a stable component interaction…Further regarding the cluster-level label propagation model 180A, the following factors were considered. A system failure can occur due to a set of root causes, or causal anomalies. As time flows, causal anomalies can propagate their impacts towards neighbors along the paths as represented by the invariant links…anomalous system status can always be traced back to a set of initial seed nodes, i.e., causal anomalies. These anomalies can propagate along the invariant network, most probably towards neighbors via paths represented by the invariant links in A. To model this process, we employ a label propagation technique. Suppose there is an unknown seed vector e ε R.sub.+.sup.n×1 with e.sub.x denoting the degree that node x is a causal anomaly. After propagation, each node x will obtain a status score r.sub.x to indicate to what extent it is impacted by the causal anomalies).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley/Boe/Do/Ata with the teachings of Cheng, in order to have wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems. The motivations behind this being to incorporate the teachings of identifying cause anomalies in power plant systems as taught by Cheng. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 2, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 2 which states
wherein the processing component comprises a condition-node processing component, and wherein the system is configured for generating a condition-node value for each condition- node of at least the subset of the condition-nodes (Boe: Col 7 lines 49-67, Col 9 lines 15-23 via Implementations of the present disclosure are described for monitoring a service at a more abstract level, as well. In particular, an aggregate KPI can be configured and calculated for a service to represent the overall health of a service. For example, a service may have 10 KPIs, each monitoring a various aspect of the service. The service may have 7 KPIs in a Normal state, 2 KPIs in a Warning state, and 1 KPI in a Critical state. The aggregate KPI can be a value representative of the overall performance of the service based on the values for the individual KPIs. Implementations of the present disclosure allow individual KPIs of a service to be weighted in terms of how important a particular KPI is to the service relative to the other KPIs in the service, thus giving users control of how to represent the overall performance of a service and control in providing a more accurate representation of the performance of the service. In addition, specific actions can be defined that are to be taken when the aggregate KPI indicating the overall health of a service, for example, exceeds a particular threshold... The service 102 can be monitored using one or more KPIs 106 for the service. A KPI is a type of performance measurement. One or more KPIs can be defined for a service. In the illustrated example, three KPIs 106A-C are defined for service 102. KPI 106A may be a measurement of CPU (central processing unit) usage for the service 102. KPI 106B may be a measurement of memory usage for the service 102. KPI 106C may be a measurement of request response time for the service 102).
Regarding Claim 3, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 3 which states
wherein the system is configured for outputting data, wherein the processing component comprises an output-determining component, and wherein the output- determining component is configured for determining at least one of condition- nodes and condition-states to be outputted that are each linked to at least one of a specified condition-node and a specified condition-state (Boe: Col 8 lines 12-32, Col 45 lines 9-29 via Implementations of the present disclosure are described for providing a service-monitoring dashboard that displays one or more KPI widgets. Each KPI widget can provide a numerical or graphical representation of one or more values for a corresponding KPI or service health score (aggregate KPI for a service) indicating how a service or an aspect of a service is performing at one or more points in time. Users can be provided with the ability to design and draw the service-monitoring dashboard and to customize each of the KPI widgets. A dashboard-creation graphical interface can be provided to define a service- monitoring dashboard based on user input allowing different users to each create a customized service-monitoring dashboard. Users can select an image for the service-monitoring dashboard (e.g., image for the background of a service- monitoring dashboard, image for an entity and/or service for service-monitoring dashboard), draw a flow chart or a representation of an environment (e.g., IT environment), specify which KPIs to include in the service-monitoring dashboard, configure a KPI widget for each specified KPI, and add one or more adhoc KPI searches to the service-monitoring dashboard... The dashboard creation graphical interface can be a wizard or any other type of tool for creating a service-monitoring dashboard that presents a visual overview of how one or more services and/or one or more aspects of the services are performing. The services can be part of an IT environment and can include, for example, a web hosting service, an email service, a database service, a revision control service, a sandbox service, a networking service, etc. A service can be provided by one or more entities such as host machines, virtual machines, switches, firewalls, routers, sensors, etc. Each entity can be associated with machine data that can have different formats and/or use different aliases for the entity. As discussed above, each service can be associated with one or more KPIs indicating how aspects of the service are performing. The KPI-selection interface of the dashboard creation GUI allows a user to select KPIs for monitoring the performance of one or more services, and the modifiable dashboard template of the dashboard creation GUI allows the user to specify how these KPIs should be presented on a service-monitoring dashboard that will be created based on the dashboard template).
Regarding Claim 4, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 4 which states
wherein the processing component comprises a pre-calculating component, and wherein the pre- calculating component is configured for performing pre- calculations of for at least a portion of the data to be outputted and to thus generate pre-calculated data (Tankersley: Para 0772 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non-working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.sup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique).
Regarding Claim 5, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 5 which states
wherein the output- determining component is configured to determine at least a portion of the condition- states and/or condition-nodes based on the pre- calculated data (Tankersley: Para 0772 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non- working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.Ssup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique).
Regarding Claim 6, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 6 which states
wherein the data-storage component is configured for storing at least one or a plurality of subset(s) of the condition-nodes , wherein further the at least one second data processing system is a plurality of second data processing systems and wherein the data-storage component is configured for storing at least an indicator of one subset of the condition- nodes and of one of the condition-states for each of the second data processing systems (Tankersley: Para 0694, 0697, 0742 via the computing machine causes display of a GUI that presents information specifying a service definition for a service and a specification for determining a KPI for the service. In one implementation, the service definition identifies a service provided by a plurality of entities each having corresponding machine data. The specification for determining the KPI refers to the KPI definitional information (e.g., which entities, which records/fields from machine data, what time frame, etc.) that is being defined and is stored as part of the service definition or in association with the service definition. In one implementation, the KPI is defined by a search query that produces a value derived from the machine data pertaining to one or more KPI entities selected from among the plurality of entities. The KPI entities may include a set of entities of the service (i.e., service entities) whose relevant machine data is used in the calculation of the KPI. Thus, the KPI entities may include either whole set or a subset of the service entities...the computing machine makes the stored entity thresholds available for determining a state of the KPI. In one implementation, determining the state of the KPI includes determining a contribution of an individual KPI entity by applying the determination component to an aggregate of machine data corresponding to the individual KPI entity, and then applying at least one entity threshold to a KPI contribution of the individual KPI entity. Further, the computing machine selects a KPI state based at least in part on the determined contribution of the individual KPI entity in view of the applied entity threshold...the computing machine causes the display of the calculated aggregate KPI score in one or more graphical interfaces and the aggregate KPI score is updated in the one or more graphical interfaces each time the aggregate KPI score is calculated. In one implementation, the configuration for displaying the calculated aggregate KPI in one or more graphical interfaces is received as input).
Regarding Claim 7, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 7 which states
wherein the system is further configured to store and process for each second data-processing system at least one of - a set of condition-node values, - a set of condition-state values, - a set of comparison results, and - a set of condition- state thresholds (Tankersley: Para 0694, 0697, 0742 via the computing machine causes display of a GUI that presents information specifying a service definition for a service and a specification for determining a KPI for the service. In one implementation, the service definition identifies a service provided by a plurality of entities each having corresponding machine data. The specification for determining the KPI refers to the KPI definitional information (e.g., which entities, which records/fields from machine data, what time frame, etc.) that is being defined and is stored as part of the service definition or in association with the service definition. In one implementation, the KPI is defined by a search query that produces a value derived from the machine data pertaining to one or more KPI entities selected from among the plurality of entities. The KPI entities may include a set of entities of the service (i.e., service entities) whose relevant machine data is used in the calculation of the KPI. Thus, the KPI entities may include either whole set or a subset of the service entities...the computing machine makes the stored entity thresholds available for determining a state of the KPI. In one implementation, determining the state of the KPI includes determining a contribution of an individual KPI entity by applying the determination component to an aggregate of machine data corresponding to the individual KPI entity, and then applying at least one entity threshold to a KPI contribution of the individual KPI entity. Further, the computing machine selects a KPI state based at least in part on the determined contribution of the individual KPI entity in view of the applied entity threshold...the computing machine causes the display of the calculated aggregate KPI score in one or more graphical interfaces and the aggregate KPI score is updated in the one or more graphical interfaces each time the aggregate KPI score is calculated. In one implementation, the configuration for displaying the calculated aggregate KPI in one or more graphical interfaces is received as input).
Regarding Claim 8, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 8 which states
wherein the system comprises at least one user interface and the system is configured for outputting at least a portion or all of the data to be outputted to at least one of the at least one user interface, and/or - wherein the system is configured for outputting at least a portion or all of the data to be outputted by transmitting them to a third data-processing system (Boe: Col 85 lines 8-25 via The SPLUNK.RTM. APP FOR VMWARE.RTM. also provides a user interface that enables a user to select a specific time range and then view heterogeneous data, comprising events, log data and associated performance metrics, for the selected time range. For example, the screen illustrated in FIG. 77D displays a listing of recent "tasks and events" and a listing of recent "log entries" for a selected time range above a performance-metric graph for "average CPU core utilization" for the selected time range. Note that a user is able to operate pull-down menus 7742 to selectively display different performance metric graphs for the selected time range. This enables the user to correlate trends in the performance-metric graph with corresponding event and log data to quickly determine the root cause of a performance problem. This user interface is described in more detail in U.S. patent application Ser. No. 14/167,316 filed on 29 Jan. 2014, which is hereby incorporated herein by reference for all possible purposes).
Regarding Claim 11, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 11 which states
wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the higher condition-state value limit, the condition-state value is set to the higher condition- state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step $922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step S925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
Regarding Claim 12, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 12 which states
wherein the at least one condition-state having a lower condition-state value limit and the at least one condition state having an upper condition-state value are the same condition-state(s) (Ata: Para 0333-0338 via in Step $920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step $922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step S925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper- limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
Regarding Claim 13, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 13 which states
wherein the pre-calculating step is performed by the first data-processing system and wherein the outputting data is based on the pre-calculated data (Tankersley: Para 0772 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non- working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.sup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique).
Regarding Claim 23, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 23 which states
wherein the system automatically adjusts at least one operating condition of at least one mechanical subsystem such that the risk of failure goes below the warning threshold (Do: Para 0011 via The systems of the exemplary embodiments can also be configured to update the slope signature/failure mode library through manual or machine learning processes based on measurements and component failure determinations. The systems of the exemplary embodiments can also be configured to automatically adjust the operation of the reciprocating compressor to reduce the risk of failure, to prolong equipment life or to improve operational efficiency).
Regarding Claim 25, Tankersley/Boe/Do/Ata/Cheng teaches the limitations of Claim 25 which states
wherein the system assesses a risk of trouble based upon application of the pre-calculated condition-state nodes and if the risk of trouble exceeds an allowable threshold, automatically adjusts at least one operating condition of at least one mechanical subsystem such that the risk of trouble goes below the allowable threshold (Do: Para 0011 via The systems of the exemplary embodiments can also be configured to update the slope signature/failure mode library through manual or machine learning processes based on measurements and component failure determinations. The systems of the exemplary embodiments can also be configured to automatically adjust the operation of the reciprocating compressor to reduce the risk of failure, to prolong equipment life or to improve operational efficiency).
Claim(s) 16, 21-22 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boe et al. (US 9,130,860 B1) in view of Tankersley et al. (US 2017/0083572 A1) in view of Do et al. (US 2021/0072733 A1) further in view of Ata et al. (US 2022/0043148 A1) further in view of Cheng et al. (US 2018/0307994 A1).
Regarding Claim 16, Boe teaches the limitations of Claim 16 which state
a first data-processing unit and a second data-processing unit, each of the data-processing units comprising a processor coupled to a memory, a fixed storage system, an input system, and an output system (Boe: Col 85 lines 48 - Col 86 line 9 via The exemplary computer system 7800 includes a processing device (processor) 7802, a main memory 7804 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 7806 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 7818, which communicate with each other via a bus 7830. Processing device 7802 represents one or more general- purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 7802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 7802 may also be one or more special- purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like...The computer system 7800 may further include a network interface device 7808. The computer system 7800 also may include a video display unit 7810 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 7812 (e.g., a keyboard), a cursor control device 7814 (e.g., a mouse), and a signal generation device 7816 (e.g., a speaker)).
However, Boe does not explicitly disclose the limitation of Claim 16 which states first or second sensors.
Tankersley though, with the teachings of Boe, teaches of
first or second sensors (Tankersley: Para 1445 via performance data is stored as “events,” wherein each event comprises a collection of performance data and/or diagnostic information that is generated by a computer system and is correlated with a specific point in time... Examples of data sources from which an event may be derived include, but are not limited to: web servers; application servers; databases; firewalls; routers; operating systems; and software applications that execute on computer systems, mobile devices, and sensors. The data generated by such data sources can be produced in various forms including, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements and sensor measurements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Boe with the teachings of Tankersley, in order to have first or second sensors. The motivations behind this being to incorporate the teachings of monitoring the KPIs of consoles. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Boe/Tankersley further teaches the limitations of Claim 16 which state
to store both at least one measurement from at least one of the first or second sensors and at least one condition-state threshold associated with the measurement from the first or second sensors (Boe: Col 7 lines 37-48 and Col 42 line 57 - Col 43 line 17 via Implementations of the present disclosure are described for customizing various states that a KPI can be in. For example, a user may define a Normal state, a Warning state, and a Critical state for a KPI, and the value produced by the search query of the KPI can indicate the current state of the KPI. In one implementation, one or more thresholds are created for each KPI. Each threshold defines an end of a range of values that represent a particular state of the KPI. A graphical interface can be provided to facilitate user input for creating one or more thresholds for each KPI, naming the states for the KPI, and associating a viSual indicator (e.g., color, pattern) to represent a respective state... At block 3418, the computing machine compares the score for the aggregate KPI to one or more thresholds. As discussed above with respect to FIG. 33B, one or more thresholds can be defined and can be configured to apply to a specific individual KPI and/or an aggregate KPI including the specific individual KPI. The thresholds can be stored in a data store that is coupled to the computing machine. If the thresholds are configured to be applied to the aggregate KPI, the computing machine compares the score of the aggregate KPI to the thresholds. If the computing machine determines that the aggregate KPI score exceeds or reaches any of the thresholds, the computing machine determines what action should be triggered in response to this comparison. Referring to FIG. 34, at block 3420, the computing machine causes an action be performed based on the comparison of the aggregate KPI score with the one or more thresholds. For example, the computing machine can generate an alert if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the highest threshold). In another example, the computing machine can generate a notable event if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the second highest threshold). In one implementation, the KPIs of multiple services is aggregated and used to create a notable event. In one implementation, the configuration for which of one or more actions to be performed is received as input (e.g., user input), stored in a data store coupled to the computing machine, and accessed by the computing machine),
storing a plurality of condition-nodes and at least one condition-state, the plurality of condition-nodes generated at least in part by using predetermined values not collected by the apparatus which are anticipated to occur and which are associated with anticipated problem condition (Tankersley: Para 0772-0773 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non-working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.sup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique...the computing machine may cause the different sets of KPI thresholds to be available for determining a KPI state (e.g., at a later time). This may involve storing the sets of KPI thresholds in a data structure or data store that may be accessible by the machine determining the states of the KPIs. In one example, a client device may be used to set the KPI threshold values and another machine (e.g., server machine) may evaluate the KPI values to determine the state of the KPI. In other examples, any device may be used to define the sets of KPI thresholds. In some implementations, the different sets of KPI thresholds are stored as part of the service definition (e.g., in the same database or file), or in association with the service definition (e.g., in a separate database or file). Using the example illustrated in FIG. 17B, different sets of KPI thresholds can be stored in a service definition structure 1720 as part of a KPI component 1727);
the condition-states corresponding to operating parameters of a mechanical system comprising a first mechanical subsystem monitored by at least one first sensor and a second mechanical subsystem monitored by at least one second sensor (Tankersley: Para 0263, 0269, 0494, 1445 via A triggering condition can be applied to the data produced by the search query to determine whether the produced data satisfies the triggering condition. Using the above example, the triggering condition can be applied to the produced KPI data to determine whether the number of occurrences of a KPI reaching a certain threshold over a specified period of time exceeds a value in the triggering condition. If the produced data satisfies the triggering condition, a particular action can be performed...FIG. 1 illustrates a block diagram of an example service provided by entities, in accordance with one or more implementations of the present disclosure. One or more entities 104A, 104B provide service 102. An entity 104A, 104B can be a component in an IT environment. Examples of an entity can include, and are not limited to a host machine, a virtual machine, a switch, a firewall, a router, a sensor, etc. For example, the service 102 may be a web hosting service, and the entities 104A, 104B may be web servers running on one or more host machines to provide the web hosting service. In another example, an entity could represent a single process on different (physical or virtual) machines. In another example, an entity could represent communication between two different machines... Processing for the method illustrated by FIG. LOAF that supports, for example, automatic entity definition for a service monitoring system begins at block 10110. At block 10110, machine data is received from a number of machine entities, each a data source, and processed for storage in a machine data store 10144. The types of machines or entities from which block 10110 may receive machine data are wide and varied and may include computers of all kinds, network devices, storage devices, virtual machines, servers, embedded processors, intelligent machines, intelligent appliances, sensors, telemetry, and any other kind or category of data generating device as may be discussed within this document or appreciated by one of skill in the art. The machine data may be minimally processed before storage and may be organized and stored as a collection of timestamped events...performance data is stored as “events,” wherein each event comprises a collection of performance data and/or diagnostic information that is generated by a computer system and is correlated with a specific point in time. Events can be derived from “time series data,” wherein time series data comprises a sequence of data points (e.g., performance measurements from a computer system)... The data generated by such data sources can be produced in various forms including, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements and sensor measurements);
wherein each condition-state is assigned to at least one condition-node, and wherein each condition-state comprises — a condition-state threshold and - a condition-state value (Boe: Col 7 lines 37-48 via Implementations of the present disclosure are described for customizing various states that a KPI can be in. For example, a user may define a Normal state, a Warning state, and a Critical state for a KPI, and the value produced by the search query of the KPI can indicate the current state of the KPI. In one implementation, one or more thresholds are created for each KPI. Each threshold defines an end of a range of values that represent a particular state of the KPI. A graphical interface can be provided to facilitate user input for creating one or more thresholds for each KPI, naming the states for the KPI, and associating a visual indicator (e.g., color, pattern) to represent a respective state);
- storing at least one node-link, wherein each node-link connects two condition-nodes and each node-link is directed (Boe: Col 84 lines 50-67 via The SPLUNK.RTM. APP FOR VMWARE.RTM. additionally provides various visualizations to facilitate detecting and diagnosing the root cause of performance problems. For example, one such visualization is a "proactive monitoring tree" that enables a user to easily view and understand relationships among various factors that affect the performance of a hierarchically structured computing system. This proactive monitoring tree enables a user to easily navigate the hierarchy by selectively expanding nodes representing various entities (e.g., virtual centers or computing clusters) to view performance information for lower-level nodes associated with lower-level entities (e.g., virtual machines or host systems). Exemplary node- expansion operations are illustrated in FIG. 77C, wherein nodes 7733 and 7734 are selectively expanded. Note that nodes 7731-7739 can be displayed using different patterns or colors to represent different performance states, such as a critical state, a warning state, a normal state or an unknown/offline state),
- comparing the respective condition-state value of at least a subset of the condition-states and the respective condition- state threshold and thus generating a respective comparison result, and - assigning the comparison result to the respective condition-state, aggregating for each condition node of the subset of the condition nodes the comparison results of the condition-states of the subset of the condition-states relating to the respective condition-node and thus generating a condition-node modifier for the respective condition-node (Boe: Col 7 lines 37-48, Col 42 line 57 - Col 43 line 17 via Implementations of the present disclosure are described for customizing various states that a KPI can be in. For example, a user may define a Normal state, a Warning state, and a Critical state for a KPI, and the value produced by the search query of the KPI can indicate the current state of the KPI. In one implementation, one or more thresholds are created for each KPI. Each threshold defines an end of a range of values that represent a particular state of the KPI. A graphical interface can be provided to facilitate user input for creating one or more thresholds for each KPI, naming the states for the KPI, and associating a viSual indicator (e.g., color, pattern) to represent a respective state...At block 3418, the computing machine compares the score for the aggregate KPI to one or more thresholds. As discussed above with respect to FIG. 33B, one or more thresholds can be defined and can be configured to apply to a specific individual KPI and/or an aggregate KPI including the specific individual KPI. The thresholds can be stored in a data store that is coupled to the computing machine. If the thresholds are configured to be applied to the aggregate KPI, the computing machine compares the score of the aggregate KPI to the thresholds. If the computing machine determines that the aggregate KPI score exceeds or reaches any of the thresholds, the computing machine determines what action should be triggered in response to this comparison...the computing machine causes an action be performed based on the comparison of the aggregate KPI score with the one or more thresholds. For example, the computing machine can generate an alert if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the highest threshold). In another example, the computing machine can generate a notable event if the aggregate KPI score exceeds or reaches a particular threshold (e.g., the second highest threshold). In one implementation, the KPIs of multiple services is aggregated and used to create a notable event. In one implementation, the configuration for which of one or more actions to be performed is received as input (e.g., user input), stored in a data store coupled to the computing machine, and accessed by the computing machine);
- storing at least one set of pre-calculated condition-state values and/or at least one set of pre-calculated comparison results which can be compared to the condition-state values or the comparison results respectively, and- storing at least one set of pre-calculated condition-node values such that if the current condition- state values are determined to match one of the sets of pre-calculated condition- state values and/or the pre-calculated comparison results are determined to match the comparison results, the pre-calculated condition-node values are immediately applied by the apparatus (Tankersley: Para 0772-0773 via the user may specify thresholds for the first time frame (e.g., working hours), and then the computing machine may automatically predict, based on prior history, how KPI values during the second time frame (e.g., non-working hours) would differ from KPI values during the first time frame, and suggest thresholds for the second time frame based on the predicted difference. In one example, if average KPI values during the first time frame are 80 percent higher than average KPI values during the second time frame, the computing machine may suggest KPI thresholds for the second time frame that are 80 percent lower than the KPI thresholds specified for the first time frame. The user may then either accept suggested KPI thresholds or modify them as needed. In another example, a suggestion of a KPI threshold for the second time frame may be based on the KPI values within the second time frame without relying on the values within other time frames. In this example, the computing machine may suggest a KPI threshold at a particular percentile of the values in the second time frame (e.g., 75.sup.th percentile). In either example, the suggestion may be based on a statistical method such as, percentile, average, median, standard deviation or other statistical technique... the computing machine may cause the different sets of KPI thresholds to be available for determining a KPI state (e.g., at a later time). This may involve storing the sets of KPI thresholds in a data structure or data store that may be accessible by the machine determining the states of the KPIs. In one example, a client device may be used to set the KPI threshold values and another machine (e.g., server machine) may evaluate the KPI values to determine the state of the KPI. In other examples, any device may be used to define the sets of KPI thresholds. In some implementations, the different sets of KPI thresholds are stored as part of the service definition (e.g., in the same database or file), or in association with the service definition (e.g., in a separate database or file). Using the example illustrated in FIG. 17B, different sets of KPI thresholds can be stored in a service definition structure 1720 as part of a KPI component 1727);
- assessing a risk of failure of the mechanical system based upon application of the pre- calculated condition-state nodes (Tankersley: Para 0980 via Weight adjustment display component 34330 may display the KPIs selected by the user and may provide a mechanism for the user to adjust the weights of the KPIs and display a resulting aggregate KPI value. Weight adjustment display component 34330 may include aggregate KPI value 34332, weights 34334A-C and graphical control elements 34336A-C. Aggregate KPI value 34332 may be a numeric value (e.g., score), non-numeric value, alphanumeric value, symbol, or the like, that may characterize the performance of one or more services. In one example, the aggregate KPI value 34332 may be used to detect a pattern of activity or diagnose abnormal activity (e.g., decrease in performance or system failure). Aggregate KPI value 34332 may be determined in view of weights 34334A-C, which may indicate the importance or influence a particular KPI has on a calculation of the aggregate KPI. Weights 34334A-C may be considered when calculating the aggregate KPI value for the services and a KPI with a higher weight may be considered more important or have a larger influence on the aggregate KPI value than other KPIs. The weights of the KPIs may be adjusted by the user by manipulating graphical control elements 34336A-C. Each of graphical control elements 34336A-C may correspond to a specific KPI and may be used to adjust a weight of a specific KPI);
- activating a warning device which will warn a user if the risk of failure exceeds a warning threshold (Tankersley: Para 0872, 1004 via Alert setting control 34697 can be, for example, a selectable button, checkbox, etc., or any other such selectable element or interface item that, upon selection (e.g., by a user) enables a user to select or define whether or not various alerts, notifications, etc. (e.g., email alerts, notable events, etc., aS are described herein), are to be generated and/or provided, e.g., upon identification of various anomalies... At block 34910, the processing device may receive a user indication to notify (e.g., alert) the user when the value of the aggregate KPI exceeds a threshold, such as a threshold associated with a critical state. In one example, the user indication may be the result of a user selecting a button to create a correlation search. The alert may be advantageous because it may be configured to identify a pattern of interest to a user and may notify the user when the pattern occurs. In response to receiving the user indication, the method may proceed to 34912).
However, the Boe/Tankersley combination does not explicitly disclose the limitation of adjusting the mechanical system to reduce the risk of failure of the mechanical system if the risk of failure exceeds a predetermined risk threshold.
Do though, with the teachings of Boe/Tankersley, teaches of
adjusting the mechanical system to reduce the risk of failure of the mechanical system if the risk of failure exceeds a predetermined risk threshold (Do: Para 0011, 0024 via The systems of the exemplary embodiments can also be configured to update the slope signature/failure mode library through manual or machine learning processes based on measurements and component failure determinations. The systems of the exemplary embodiments can also be configured to automatically adjust the operation of the reciprocating compressor to reduce the risk of failure, to prolong equipment life or to improve operational efficiency...At step 208, the control system 104 queries whether any of the measurement signals produced by the sensors 134 indicate a condition that is out of spec by more than an allowable amount. If the sensors 134 detect conditions that remain within accepted tolerances, the method 200 returns to step 206 for continued monitoring).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Boe/Tankersley with the teachings of Do in order to have adjusting the mechanical system to reduce the risk of failure of the mechanical system if the risk of failure exceeds a predetermined risk threshold. The motivations behind this being to incorporate the teachings of monitoring the health of machines. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination further does not teach the limitations of wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit, and wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the higher condition-state value limit, the condition-state value is set to the lower condition-state value limit.
Ata though, with the teachings of Tankersley/Boe/Do, teaches of
wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step $925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated), and
wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the higher condition-state value limit, the condition-state value is set to the lower condition-state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step $925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley/Boe/Do with the teachings of Ata, in order to have wherein at least one condition-state has a lower condition-state value limit, and if the condition-state value of the condition-state is lower than the lower condition-state value limit, the condition-state value is set to the lower condition-state value limit, and wherein at least one condition-state has an upper condition-state value limit, and if the condition-state value of the condition-state is higher than the higher condition-state value limit, the condition-state value is set to the lower condition-state value limit. The motivations behind this being to incorporate the teachings of setpoint adjustments. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, the combination does not explicitly disclose the limitation of Claim 16 which states wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems.
Cheng though, with the teachings of Boe/Tankersley/Do/Ata, teaches of
wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems (Cheng: Para 0005, 0037, 0073, 0111 via an aspect of the present invention, a system is provided for identifying multiple causal anomalies in a power plant system having multiple system components. The system includes a processor. The processor is configured to construct an invariant network model having (i) a plurality of nodes, each representing a respective one of the multiple system components and (ii) a plurality of invariant links, each representing a stable component interaction…Further regarding the cluster-level label propagation model 180A, the following factors were considered. A system failure can occur due to a set of root causes, or causal anomalies. As time flows, causal anomalies can propagate their impacts towards neighbors along the paths as represented by the invariant links…anomalous system status can always be traced back to a set of initial seed nodes, i.e., causal anomalies. These anomalies can propagate along the invariant network, most probably towards neighbors via paths represented by the invariant links in A. To model this process, we employ a label propagation technique. Suppose there is an unknown seed vector e ε R.sub.+.sup.n×1 with e.sub.x denoting the degree that node x is a causal anomaly. After propagation, each node x will obtain a status score r.sub.x to indicate to what extent it is impacted by the causal anomalies).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tankersley/Boe/Do/Ata with the teachings of Cheng, in order to have wherein the directed node-links represent causal relationships between mechanical failure conditions of the first and second mechanical subsystems. The motivations behind this being to incorporate the teachings of identifying cause anomalies in power plant systems as taught by Cheng. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 21, the combination of Boe/Tankersley/Do/Ata/Cheng teaches the limitations of Claim 21 which states
wherein at least one condition- state has an upper condition-state value limit, and if the condition-state value of the condition- state is higher than the higher condition-state value limit, the condition-state value is set to the lower condition- state value limit (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step S923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step $924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step $925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
Regarding Claim 22, the combination of Boe/Tankersley/Do/Ata/Cheng teaches the limitations of Claim 22 which states
wherein at least one of the at least one condition-state having a lower condition-state value limit and the at least one condition-state having an upper condition-state value limit are the same condition-state(s) (Ata: Para 0333-0338 via in Step S920, the acquirer 341 reads the desired measurement value 352 from the second storage 35, and thus acquires the upper-limit and lower-limit thresholds serving as the determination thresholds. In Step $921, the threshold corrector 347 acquires multiple measurement values from the reference workpiece acquired using the optimized setpoints X_opt. The threshold corrector 347 then extracts the maximum and minimum measurement values from the multiple measurement values. In Step S922, the threshold corrector 347 calculates a first difference, which is the difference between the upper-limit threshold and the maximum measurement value. In Step $923, the threshold corrector 347 calculates a second difference, which is the difference between the lower-limit threshold and the minimum measurement value. In Step S924, the threshold corrector 347 selects a smaller one of the first difference and the second difference as an adjustment value. In Step S925, the threshold corrector 347 subtracts the adjustment value from the upper-limit threshold set by an operation of the user in Step S3 in FIG. 6 and adds the adjustment value to the lower-limit threshold, thereby correcting the upper-limit and lower-limit thresholds. The determination thresholds defined by the corrected upper-limit and lower-limit thresholds are then stored into the second storage 35 as the desired measurement value 352. The determination threshold correction process is then terminated).
Regarding Claim 26, the combination of Boe/Tankersley/Do/Ata/Cheng teaches the limitations of Claim 26 which states
assessing a risk of trouble based upon application of the pre-calculated condition- state nodes and if the risk of trouble exceeds an allowable threshold, automatically;- adjusting at least one operating condition of at least one mechanical subsystem such that the risk of trouble is likely to go below the allowable threshold (Do: Para 0011 via The systems of the exemplary embodiments can also be configured to update the slope signature/failure mode library through manual or machine learning processes based on measurements and component failure determinations. The systems of the exemplary embodiments can also be configured to automatically adjust the operation of the reciprocating compressor to reduce the risk of failure, to prolong equipment life or to improve operational efficiency).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 5:30.
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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