Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This initial office action is based on the application filed on 03/06/2024, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 9 and 16 are presented in independent form.
Priority
3. No priority document has been filed in this application.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 06/06/2024 and 08/20/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Objections
6. Claim 5 are objected to because of the following informalities:
Claims 5, 13 recites the limitation/element "DCS" in lines 2, 2 and 3 respectively, should be spelled out.
Claim 2 recites the limitation “wherein the design artifact comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof” should be amended to -- wherein the design artifact comprises at least one of a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code
Claim 5 recites the limitation “wherein the second input comprises a selection of a type of custom view, and wherein the type corresponds to a DCS view, a plant view, a fleet view, or any combination thereof” should be amended to -- wherein the second input comprises at least one of a selection of a type of custom view, and wherein the type corresponds to a DCS view, a plant view, a fleet view
Claims 10, 13, 18 and 20 should be amended similarly to claims 2 and 5 above.
Appropriate correction is required.
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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1 and 15 is being presented below.
Claims 1, 9 and 16:
Step 1 Analysis:
Claim 1 of the instant application is direct to apparatus.
Claim 9 of the instant application is direct to process.
Claim 16 of the instant application is direct to product.
Step 2 Analysis:
Claim 1 recites:
(a) receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts stored in the memory, wherein the design artifact defines an industrial automation project associated with an industrial automation system, and wherein the industrial automation system comprises one or more industrial automation components configured to perform an industrial automation process based on the industrial automation project;
(b) parsing the design artifact in response to receiving the first input;
(c) identifying one or more causal relationships between the one or more industrial automation components based on the parsed design artifact;
(d) storing data indicative of the identified causal relationships in a database;
(e) generating one or more causal graphs based on the stored data, wherein the causal graphs comprise a visual representation of input/output relationships between the one or more industrial automation components;
(f) receiving a second input indicative of a request to generate a custom view of the industrial automation system;
(g) generating the custom view of the industrial automation system in response to the second input, wherein the custom view comprises the one or more causal graphs; and
(h) presenting the custom view within a graphical user interface (GUI) via an electronic display.
Step 2A -- Prong 1:
The claim 1 the limitations of:
(b) parsing the design artifact in response to receiving the first input;
(c) identifying one or more causal relationships between the one or more industrial automation components based on the parsed design artifact;
Limitations (b)-(c) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “A system”, “processing circuitry”, “a memory”, and “a memory accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry”. The limitations of “A system”, “processing circuitry”, “a memory”, and “a memory accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitations (a), (d), (f) and (h) perform as well-understood, routine and conventional activity and limitations (e) and (g) are merely insignificant extra solution activity of gathering data and outputting data. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 9 recites:
(a) receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts associated with an industrial automation system comprising one or more industrial automation devices, wherein each design artifact of the plurality design artifacts defines an industrial automation project configured to control the industrial automation devices to perform an industrial automation process;
(b) parsing the selected design artifact in response to receiving the first input;
(c) identifying input/output relationships between the one or more industrial automation devices based on the parsed design artifact;
(d) generating one or more causal graphs based on the identified input/output relationships, wherein the causal graphs comprise a visual representation of the identified input/output relationships between the one or more industrial automation devices;
(e) receiving a second input indicative of a request to generate a custom view of the industrial automation system;
(f) generating the custom view of the industrial automation system in response to the second input, wherein the custom view comprises the one or more causal graphs;
(g) presenting the custom view within a graphical user interface (GUI).
Step 2A -- Prong 1:
The claim 9 the limitations of:
(b) parsing the selected design artifact in response to receiving the first input;
(c) identifying input/output relationships between the one or more industrial automation devices based on the parsed design artifact;
Limitations (b)-(c) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 9 recites the additional limitations of “an industrial automation system” and “one or more industrial automation devices”. The limitations of “an industrial automation system” and “one or more industrial automation devices” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitations (a), (e) and (g) perform as well-understood, routine and conventional activity and limitations (d) and (f) are merely insignificant extra solution activity of gathering data and outputting data. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 16 recites:
(a) receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts associated with an industrial automation system comprising one or more industrial automation devices, wherein each design artifact of the plurality design artifacts defines an industrial automation project configured to control the one or more industrial automation devices to perform an industrial automation process;
(b) parsing the design artifact in response to receiving the first input;
(c) identifying one or more causal relationships between the one or more industrial automation devices based on the parsed design artifact;
(d) storing data indicative of the identified causal relationships in a database;
(e) generating one or more causal graphs based on the stored data, wherein the causal graphs comprise a visual representation of input/output relationships between the one or more industrial automation devices;
(f) receiving a second input indicative of a request to generate a custom view of the industrial automation system;
(g) generating the custom view of the industrial automation system in response to the second input, wherein the custom view comprises the one or more causal graphs; and
(h) presenting the custom view within a graphical user interface (GUI) via an electronic display.
Step 2A -- Prong 1:
The claim 16 the limitations of:
(b) parsing the design artifact in response to receiving the first input;
(c) identifying one or more causal relationships between the one or more industrial automation devices based on the parsed design artifact;
Limitations (b)-(c) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 16 recites the additional limitations of “A non-transitory, tangible, computer readable medium”, “processing circuitry“, “an industrial automation system” and “one or more industrial automation devices”. The limitations of “A non-transitory, tangible, computer readable medium”, “processing circuitry“, “an industrial automation system” and “one or more industrial automation devices” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitations (a), (d), (f) and (h) perform as well-understood, routine and conventional activity and limitations (e) and (g) are merely insignificant extra solution activity of gathering data and outputting data. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (claims 1, 9 and 16):
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing/uploading data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claim 2 recites “wherein the design artifact comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 2 is ineligible.
Additionally, claim 3 recites “wherein the one or more causal graphs represent the one or more industrial automation components as nodes and the input/output relationships between the one or more industrial automation components as edges between the nodes” is merely insignificant extra solution activity of gathering/outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 3 is ineligible.
Additionally, claim 4 recites “wherein the custom view comprises the causal graphs organized according to hierarchical relationships, relative physical positions of the one or more industrial automation components, or both” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 4 is ineligible.
Additionally, claim 5 recites “wherein the second input comprises a selection of a type of custom view, and wherein the type corresponds to a DCS view, a plant view, a fleet view, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 5 is ineligible.
Additionally, claim 6 recites “(a) receiving a third input indicative of a change to the design artifact; (b) parsing the design artifact in response to receiving the third input; (c) identifying one or more changes in the one or more causal relationships between the one or more industrial automation components based on the parsed design artifact, wherein the one or more changes are associated with the change to the design artifact; (d) updating the data indicative of the causal relationships stored in the database to reflect the identified changes; (e) updating a portion of the one or more causal graphs based on the updated data to reflect the identified changes; (f) updating the custom view of the industrial automation system, wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more updated causal graphs; and (g) presenting the updated custom view within the GUI” The addition limitations (b) and (c) as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. The addition limitations (a) and (d-g) “ which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 6 is ineligible.
Additionally, claim 7 recites “wherein an unexpected update to the custom view indicates a security breach, an unauthorized control modification within the industrial automation system, or both” is merely insignificant extra solution activity of recognizing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 7 is ineligible.
Additionally, claim 8 recites “wherein the database is stored locally in the memory and remotely in a cloud server” which performs as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 8 is ineligible.
Additionally, claim 10 recites “wherein each design artifact of the plurality of design artifacts comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 10 is ineligible.
Additionally, claim 11 recites “wherein the one or more causal graphs represent the one or more industrial automation devices as nodes and the input/output relationships between the one or more industrial automation devices as edges between the nodes” is merely insignificant extra solution activity of gathering/outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 11 is ineligible.
Additionally, claim 12 recites “wherein the custom view comprises the causal graphs organized according to hierarchical relationships, relative physical positions of the one or more industrial automation devices, or both” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 12 is ineligible.
Additionally, claim 13 recites “wherein the second input comprises a selection of a type of custom view, and wherein the type corresponds to a DCS view, a plant view, a fleet view, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 13 is ineligible.
Additionally, claim 14 recites “(a) receiving a third input indicative of a change to the design artifact; (b) parsing the design artifact in response to receiving the third input; (c) identifying one or more changes in the input/output relationships between the one or more industrial automation devices based on the parsed design artifact, wherein the one or more changes are associated with the change to the design artifact; (d) identifying a portion of the one or more causal graphs containing input/output relationships that do not reflect the change to the design artifact; (e) updating the portion of the one or more causal graphs based on the identified changes in the input/output relationships; (f) updating the custom view of the industrial automation system, wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs; and (g) presenting the updated custom view within the GUI” The addition limitations (b-d) as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. The addition limitations (a) and (e-g) which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 14 is ineligible.
Additionally, claim 15 recites “detecting an occurrence of an unexpected event during operation of the industrial automation system based on an unexpected update to the custom view; and identifying a cause of the unexpected event based on the custom view” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “detecting” and “identifying” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 15 is ineligible.
Additionally, claim 17 recites “(a) receiving a third input indicative of a change to the design artifact; (b) parsing the design artifact in response to receiving the third input; (c) identifying one or more changes in the one or more causal relationships between the one or more industrial devices, wherein the one or more changes are associated with the change to the design artifact; (d) updating the stored data to reflect the identified changes in the one or more causal relationships; (e) identifying a portion of the one or more causal graphs that do not reflect the changes in the one or more causal relationships; (f) updating the portion of the one or more causal graphs based on the identified changes in the causal relationships; (g) updating the custom view of the industrial automation system, wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs; and (h) presenting the updated custom view within the GUI” The addition limitations (b), (c) and (e) as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “parsing/analyzing” and “identifying” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. The addition limitations (a), (d) and (f-h) “ which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 17 is ineligible.
Additionally, claim 18 recites “wherein each design artifact of the plurality of design artifacts comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 18 is ineligible.
Additionally, claim 19 recites “wherein the one or more causal graphs represent the one or more industrial automation devices as nodes and the input/output relationships between the one or more industrial automation devices as edges between the nodes” is merely insignificant extra solution activity of gathering/outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 19 is ineligible.
Additionally, claim 20 recites “wherein the second input comprises a selection of a type of custom view, and wherein the type corresponds to one of a DCS view, a plant view, a fleet view, or any combination thereof” is merely insignificant extra solution activity of analyzing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
8. Claim(s) 1-3, 5-6, 9-11, 13-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Von der Fakultat (Capturing and Exploiting Plant Topology and Process Information as a Basis to Support Engineering and Operational Activities in Process Plants, 2017 – IDS filed on 08/20/2025 -- herein after Von) in view Chenghao Liu (PyRCA: A Library for Metric-based Root Cause Analysis, 2023 – herein after Liu).
Regarding claim 1.
Von discloses
A system comprising:
processing circuitry (electrical circuits – See page 21); and
a memory accessible by the processing circuitry (memory – See page 69), the memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations comprising:
receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts stored in the memory (this contribution focuses specifically on the analysis of piping and instrumentation diagrams (P&IDs) and control logic diagrams (CLDs), both on scanned media and scalable vector graphics formats.– See page 5), wherein the design artifact defines an industrial automation project associated with an industrial automation system (As the main outcome of the Front End Engineering Design (FEED) phase, P&IDs embody the main source of plant and process information. Therefore, several authors have regarded these documents as the most important basis for the (re)engineering and implementation of control systems – See page 5), and wherein the industrial automation system comprises one or more industrial automation components configured to perform an industrial automation process based on the industrial automation project (Connectivity among components, devices, and blocks; specifically pipelines and information connectors – See page 6. Plant asset catalogues or sets of symbols commonly used to embody specific devices, processes, or logical operators. The definition of libraries allow not only for modularity, which in turn results in more efficient search procedures, but also for effective capture and reuse of structural knowledge within different projects – See page 28);
parsing the design artifact in response to receiving the first input (plant abnormal behavior analysis is based on a graph-scheme which compartmentalizes plant topology and process knowledge into modular components associated with plant equipment– See page 112. Performs a systematic evaluation of process causalities by combining topology and process information for the discovery of disturbance propagation paths linking observed alarms. Such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information – See page 112);
identifying one or more causal relationships between the one or more industrial automation components based on the parsed design artifact (defined DCDG causal properties –i.e., strengths, lags, and qualitative causalities– link types are not modeled as edge attributes – See page 116 and Fig. 6.5);
storing data indicative of the identified causal relationships in a database (the linkage between topology information and causal process knowledge (i.e., first principles, heuristics, and static and dynamic process data) is accomplished by storing the causal relations of plant components such as heat exchangers, pumps, and reactors as templates in a knowledge repository – See page 117);
generating one or more causal graphs based on the stored data (dynamic causal digraphs define a specific set of model variables, so-called propagation carriers, to describe the process causalities occurring within and among components in a process – See page 114. Research topic is the integration of formalized process descriptions and topology models as a basis for the generation of modular causal graphs – See page 138), wherein the causal graphs comprise a visual representation of input/output relationships between the one or more industrial automation components (diagnostic results are presented to the operator for visualization. the algorithm generates four different representations of inferred causalities: (i) a colored DCDG highlighting those nodes which triggered alarms as well as the edges found to link these nodes, (ii) a graph-based representation of the process schematic highlighting those components found to be affected by the disturbance propagation, (iii) a colored connectivity matrix depicting the connectivity between alarms and the resulting alarm groups, and (iv) a list of ranked propagation paths with respective causal alarms – See page 126);
receiving a second input indicative of a request to generate a custom view of the industrial automation system (dynamic causal digraphs are generated in an event-triggered basis, i.e., an individual DCDG is dynamically constructed when a process disturbance (signalized by alarms) is detected or alternatively upon request of the user. The particular DCDG for that event considers up-to-date information of – See page 116);
generating the custom view of the industrial automation system in response to the second input (verification and ensure conversion correctness and consistency, a graphical depiction of the model is generated and shown to the user in a topology editor (see Figure 3.6). The graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections. Thus, users can visualize found elements and compare them with the original document, effecting changes where necessary. Corrections carried out in the graphical interface have a direct effect on the OO model. Warnings generated during the recognition process can be used at this stage to prioritize the crosschecking procedure – See pages 57-58),
presenting the custom view within a graphical user interface (GUI) via an electronic display (a graphical depiction of the model is generated and shown to the user in a topology editor…the graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections – See pages 57-58).
Von does not disclose
wherein the custom view comprises the one or more causal graphs, and
Liu discloses
wherein the custom view comprises the one or more causal graphs (Fig. 6, the interactive dashboard of Causal Graph Discovery Tab), and
Liu also discloses
presenting the custom view within a graphical user interface (GUI) via an electronic display (PyRCA supports multiple causal graph construction and root cause scoring models. Additionally, it comes with a GUI dashboard to conduct root cause analysis (RCA) in an interactive way, which better aligns with the user experience in real-world scenarios – See page 2).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to enable to support multiple causal graph construction and root cause scoring models and come with a GUI dashboard to conduct root cause analysis in an interactive way as suggested by Liu (page 2)
Regarding claim 2, the system of claim 1,
Von discloses
wherein the design artifact comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof (generated P&ID and CLD models have been regarded as separate information artifacts – See page 64).
Regarding claim 3, the system of claim 1,
Von discloses
wherein the one or more causal graphs represent the one or more industrial automation components as nodes (causal graph having nodes as pipe, valve – See Fig. 6.5, page 116) and the input/output relationships between the one or more industrial automation components as edges between the nodes (connects nodes within a given row and symbolizes existing bonds between variables in a determined piece of equipment due to a physical principle; for instance, the relation temperature-pressure in a two-phase closed tank (due to the ideal gas law) – See Fig. 6.5 and page 116).
Regarding claim 5, the system of claim 1,
Liu discloses
wherein the second input comprises a selection of a type of custom view (the input layer directly loads metric data in dataframe format – See pages 5-6), and wherein the type corresponds to a DCS view, a plant view, a fleet view (the interactive dashboard, GUI dashboard with user experience in real-word scenario – See page 2), or any combination thereof.
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to enable to conduct root cause analysis in an interactive way as suggested by Liu (page 2).
Regarding claim 6, the system of claim 1, wherein the operations comprise:
Von discloses
receiving a third input indicative of a change to the design artifact (Simulation-based engineering allows assessing new solutions early during the project, enabling thereby a smoother, faster, and safer deployment of changes in real production sites – See page 83);
parsing the design artifact in response to receiving the third input (the dimensions of process plants and their vast amount of process signals have limited their usability in large-scale processes, as calculations required for disturbance detection and root-cause analysis become time- and resource-intensive – See page 103);
identifying one or more changes in the one or more causal relationships between the one or more industrial automation components based on the parsed design artifact (existing plants change their physical structure as sensors and actuators are exchanged, pipes added or removed, plants modules replaced, and automation or communication systems upgraded. In theory, all those changes should be reflected in a back-documentation process, where every change is registered as an adjustment in existing plant documents, such as P&IDs and CLDs – See page 99), wherein the one or more changes are associated with the change to the design artifact (the developed algorithm correctly performed the recognition of linking artifacts among the analyzed models, merging, removing, and updating the associated XML-tree structures as required – See page 81);
updating the data indicative of the causal relationships stored in the database to reflect the identified changes (modules can be easily extended, updated, or exchanged in complete independence of the causal graph itself, the tedious inspection process required for the update of large models can be avoided. In turn, updated knowledge can be automatically and systematically transferred from the PLUT to those instances in the causal graph affected by the underlying changes – See page 120);
updating a portion of the one or more causal graphs based on the updated data to reflect the identified changes (the event-triggered generation of dynamic causal digraphs, i.e., modular causal models interconnected based on plant connectivity information, and (b) the recursive consultation of a propagation look-up table containing general and process-specific information for establishing updated process causalities within and among plant components – See page 136);
updating the custom view of the industrial automation system (information for establishing updated process causalities within and among plant components – See page 136),
Von does not disclose
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more updated causal graphs; and
presenting the updated custom view within the GUI.
Liu discloses
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more updated causal graphs (With the ability to visualize causal graphs and the root cause of incidents, practitioners can quickly gain insights and improve their workflow efficiency – See Abstract); and
presenting the updated custom view within the GUI (these causal graphs can be reconstructed from the topology of a specific application (i.e., domain knowledge from log analysis and trace analysis) – See page 6).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to reconstruct the casual graphs from the topology as suggested by Liu (page 6).
Regarding claim 9.
Von discloses
A method, comprising:
receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts associated with an industrial automation system comprising one or more industrial automation devices (this contribution focuses specifically on the analysis of piping and instrumentation diagrams (P&IDs) and control logic diagrams (CLDs), both on scanned media and scalable vector graphics formats. As the main outcome of the Front End Engineering Design (FEED) phase, P&IDs embody the main source of plant and process information. Therefore, several authors have regarded these documents as the most important basis for the (re)engineering and implementation of control systems – See page 5), wherein each design artifact of the plurality design artifacts defines an industrial automation project configured to control the industrial automation devices to perform an industrial automation process (As the main outcome of the Front End Engineering Design (FEED) phase, P&IDs embody the main source of plant and process information. Therefore, several authors have regarded these documents as the most important basis for the (re)engineering and implementation of control systems – See page 5. Connectivity among components, devices, and blocks; specifically pipelines and information connectors – See page 6. Identified artifacts and their relations – See page 21. Plant asset catalogues or sets of symbols commonly used to embody specific devices, processes, or logical operators. The definition of libraries allow not only for modularity, which in turn results in more efficient search procedures, but also for effective capture and reuse of structural knowledge within different projects – See page 28);
parsing the selected design artifact in response to receiving the first input (plant abnormal behavior analysis is based on a graph-scheme which compartmentalizes plant topology and process knowledge into modular components associated with plant equipment– See page 112. Performs a systematic evaluation of process causalities by combining topology and process information for the discovery of disturbance propagation paths linking observed alarms. Such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information – See page 112);
identifying input/output relationships between the one or more industrial automation devices based on the parsed design artifact (such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information – See page 112. Defined DCDG causal properties –i.e., strengths, lags, and qualitative causalities– link types are not modeled as edge attributes – See page 116 and Fig. 6.5);
generating one or more causal graphs based on the identified input/output relationships (casual relations are constructed – See page 105. Dynamic causal digraphs define a specific set of model variables, so-called propagation carriers, to describe the process causalities occurring within and among components in a process – See page 114. Research topic is the integration of formalized process descriptions and topology models as a basis for the generation of modular causal graphs – See page 138), wherein the causal graphs comprise a visual representation of the identified input/output relationships between the one or more industrial automation devices (diagnostic results are presented to the operator for visualization. the algorithm generates four different representations of inferred causalities: (i) a colored DCDG highlighting those nodes which triggered alarms as well as the edges found to link these nodes, (ii) a graph-based representation of the process schematic highlighting those components found to be affected by the disturbance propagation, (iii) a colored connectivity matrix depicting the connectivity between alarms and the resulting alarm groups, and (iv) a list of ranked propagation paths with respective causal alarms – See page 126);
receiving a second input indicative of a request to generate a custom view of the industrial automation system (dynamic causal digraphs are generated in an event-triggered basis, i.e., an individual DCDG is dynamically constructed when a process disturbance (signalized by alarms) is detected or alternatively upon request of the user. The particular DCDG for that event considers up-to-date information of – See page 116);
generating the custom view of the industrial automation system in response to the second input (verification and ensure conversion correctness and consistency, a graphical depiction of the model is generated and shown to the user in a topology editor (see Figure 3.6). The graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections. Thus, users can visualize found elements and compare them with the original document, effecting changes where necessary. Corrections carried out in the graphical interface have a direct effect on the OO model. Warnings generated during the recognition process can be used at this stage to prioritize the crosschecking procedure – See pages 57-58),
presenting the custom view within a graphical user interface (GUI) (The graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections – See pages 57-58).
Von does not disclose
wherein the custom view comprises the one or more causal graphs;
Liu discloses
wherein the custom view comprises the one or more causal graphs (Fig. 6, the interactive dashboard of Causal Graph Discovery Tab), and
Liu also discloses
presenting the custom view within a graphical user interface (GUI) (PyRCA supports multiple causal graph construction and root cause scoring models. Additionally, it comes with a GUI dashboard to conduct root cause analysis (RCA) in an interactive way, which better aligns with the user experience in real-world scenarios – See page 2).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to enable to support multiple causal graph construction and root cause scoring models and come with a GUI dashboard to conduct root cause analysis in an interactive way as suggested by Liu (page 2).
Regarding claim 10, the method of claim 9,
Von discloses
wherein each design artifact of the plurality of design artifacts comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof (generated P&ID and CLD models have been regarded as separate information artifacts – See page 64).
Regarding claim 11, the method of claim 9,
Von discloses
wherein the one or more causal graphs represent the one or more industrial automation devices as nodes (causal graph having nodes as pipe, valve – See Fig. 6.5, page 116) and the input/output relationships between the one or more industrial automation devices as edges between the nodes (connects nodes within a given row and symbolizes existing bonds between variables in a determined piece of equipment due to a physical principle; for instance, the relation temperature-pressure in a two-phase closed tank (due to the ideal gas law) – See Fig. 6.5 and page 116).
Regarding claim 13, recites the same limitations as rejected claim 5 above.
Regarding claim 14, the method of claim 9, comprising:
Von discloses
receiving a third input indicative of a change to the design artifact (Simulation-based engineering allows assessing new solutions early during the project, enabling thereby a smoother, faster, and safer deployment of changes in real production sites – See page 83);
parsing the design artifact in response to receiving the third input (the dimensions of process plants and their vast amount of process signals have limited their usability in large-scale processes, as calculations required for disturbance detection and root-cause analysis become time- and resource-intensive – See page 103);
identifying one or more changes in the input/output relationships between the one or more industrial automation devices based on the parsed design artifact (existing plants change their physical structure as sensors and actuators are exchanged, pipes added or removed, plants modules replaced, and automation or communication systems upgraded. In theory, all those changes should be reflected in a back-documentation process, where every change is registered as an adjustment in existing plant documents, such as P&IDs and CLDs – See page 99), wherein the one or more changes are associated with the change to the design artifact (the developed algorithm correctly performed the recognition of linking artifacts among the analyzed models, merging, removing, and updating the associated XML-tree structures as required – See page 81);
identifying a portion of the one or more causal graphs containing input/output relationships that do not reflect the change to the design artifact (the automatic conversion of digital CAD drawings into object-oriented representations, their applicability in practice is limited since this type of records represents only a small portion of documents found in process facilities – See page 3);
updating the portion of the one or more causal graphs based on the identified changes in the input/output relationships (the developed algorithm correctly performed the recognition of linking artifacts among the analyzed models, merging, removing, and updating the associated XML-tree structures as required – See page 91);
updating the custom view of the industrial automation system (such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information, and (b) the recursive consultation of a propagation look-up table (PLUT) containing general and process-specific information for establishing updated causalities within and among plant components – See page 112),
Von does not disclose
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs (; and
presenting the updated custom view within the GUI.
Liu discloses
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs (With the ability to visualize causal graphs and the root cause of incidents, practitioners can quickly gain insights and improve their workflow efficiency – See Abstract); and
presenting the updated custom view within the GUI (these causal graphs can be reconstructed from the topology of a specific application (i.e., domain knowledge from log analysis and trace analysis) – See page 6).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to reconstruct the casual graphs from the topology as suggested by Liu (page 6).
Regarding claim 16.
Von discloses
A non-transitory, tangible, computer readable medium comprising instructions that, when executed by processing circuitry, causes the processing circuitry to perform operations comprising:
receiving a first input indicative of a selection of a design artifact from a plurality of design artifacts associated with an industrial automation system comprising one or more industrial automation devices (this contribution focuses specifically on the analysis of piping and instrumentation diagrams (P&IDs) and control logic diagrams (CLDs), both on scanned media and scalable vector graphics formats.– See page 5. As the main outcome of the Front End Engineering Design (FEED) phase, P&IDs embody the main source of plant and process information. Therefore, several authors have regarded these documents as the most important basis for the (re)engineering and implementation of control systems – See page 5), wherein each design artifact of the plurality design artifacts defines an industrial automation project configured to control the one or more industrial automation devices to perform an industrial automation process (As the main outcome of the Front End Engineering Design (FEED) phase, P&IDs embody the main source of plant and process information. Therefore, several authors have regarded these documents as the most important basis for the (re)engineering and implementation of control systems – See page 5. Connectivity among components, devices, and blocks; specifically pipelines and information connectors – See page 6. Identified artifacts and their relations – See page 21. Plant asset catalogues or sets of symbols commonly used to embody specific devices, processes, or logical operators. The definition of libraries allow not only for modularity, which in turn results in more efficient search procedures, but also for effective capture and reuse of structural knowledge within different projects – See page 28);
parsing the design artifact in response to receiving the first input (plant abnormal behavior analysis is based on a graph-scheme which compartmentalizes plant topology and process knowledge into modular components associated with plant equipment– See page 112. Performs a systematic evaluation of process causalities by combining topology and process information for the discovery of disturbance propagation paths linking observed alarms. Such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information – See page 112);
identifying one or more causal relationships between the one or more industrial automation devices based on the parsed design artifact (defined DCDG causal properties –i.e., strengths, lags, and qualitative causalities– link types are not modeled as edge attributes – See page 116 and Fig. 6.5);
storing data indicative of the identified causal relationships in a database (the linkage between topology information and causal process knowledge (i.e., first principles, heuristics, and static and dynamic process data) is accomplished by storing the causal relations of plant components such as heat exchangers, pumps, and reactors as templates in a knowledge repository – See page 117);
generating one or more causal graphs based on the stored data (dynamic causal digraphs define a specific set of model variables, so-called propagation carriers, to describe the process causalities occurring within and among components in a process – See page 114. Research topic is the integration of formalized process descriptions and topology models as a basis for the generation of modular causal graphs – See page 138), wherein the causal graphs comprise a visual representation of input/output relationships between the one or more industrial automation devices (diagnostic results are presented to the operator for visualization. the algorithm generates four different representations of inferred causalities: (i) a colored DCDG highlighting those nodes which triggered alarms as well as the edges found to link these nodes, (ii) a graph-based representation of the process schematic highlighting those components found to be affected by the disturbance propagation, (iii) a colored connectivity matrix depicting the connectivity between alarms and the resulting alarm groups, and (iv) a list of ranked propagation paths with respective causal alarms – See page 126);
receiving a second input indicative of a request to generate a custom view of the industrial automation system (dynamic causal digraphs are generated in an event-triggered basis, i.e., an individual DCDG is dynamically constructed when a process disturbance (signalized by alarms) is detected or alternatively upon request of the user. The particular DCDG for that event considers up-to-date information of – See page 116);
generating the custom view of the industrial automation system in response to the second input (verification and ensure conversion correctness and consistency, a graphical depiction of the model is generated and shown to the user in a topology editor (see Figure 3.6). The graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections. Thus, users can visualize found elements and compare them with the original document, effecting changes where necessary. Corrections carried out in the graphical interface have a direct effect on the OO model. Warnings generated during the recognition process can be used at this stage to prioritize the crosschecking procedure – See pages 57-58),
presenting the custom view within a graphical user interface (GUI) via an electronic display (The graphical representation is overlaid with the original black and white diagram as colored forms representing the identified objects and their connections – See pages 57-58).
Von does not disclose
wherein the custom view comprises the one or more causal graphs,
Liu discloses
wherein the custom view comprises the one or more causal graphs (Fig. 6, the interactive dashboard of Causal Graph Discovery Tab), and
Liu also discloses
presenting the custom view within a graphical user interface (GUI) via an electronic display (PyRCA supports multiple causal graph construction and root cause scoring models. Additionally, it comes with a GUI dashboard to conduct root cause analysis (RCA) in an interactive way, which better aligns with the user experience in real-world scenarios – See page 2).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to enable to support multiple causal graph construction and root cause scoring models and come with a GUI dashboard to conduct root cause analysis in an interactive way as suggested by Liu (page 2)
Regarding claim 17, the computer readable medium of claim 16, wherein the operations comprise:
Von discloses
receiving a third input indicative of a change to the design artifact (Simulation-based engineering allows assessing new solutions early during the project, enabling thereby a smoother, faster, and safer deployment of changes in real production sites – See page 83);
parsing the design artifact in response to receiving the third input (the dimensions of process plants and their vast amount of process signals have limited their usability in large-scale processes, as calculations required for disturbance detection and root-cause analysis become time- and resource-intensive – See page 103);
identifying one or more changes in the one or more causal relationships between the one or more industrial devices (existing plants change their physical structure as sensors and actuators are exchanged, pipes added or removed, plants modules replaced, and automation or communication systems upgraded. In theory, all those changes should be reflected in a back-documentation process, where every change is registered as an adjustment in existing plant documents, such as P&IDs and CLDs – See page 99), wherein the one or more changes are associated with the change to the design artifact (the developed algorithm correctly performed the recognition of linking artifacts among the analyzed models, merging, removing, and updating the associated XML-tree structures as required – See page 81);
updating the stored data to reflect the identified changes in the one or more causal relationships (modules can be easily extended, updated, or exchanged in complete independence of the causal graph itself, the tedious inspection process required for the update of large models can be avoided. In turn, updated knowledge can be automatically and systematically transferred from the PLUT to those instances in the causal graph affected by the underlying changes – See page 120);
identifying a portion of the one or more causal graphs that do not reflect the changes in the one or more causal relationships (the automatic conversion of digital CAD drawings into object-oriented representations, their applicability in practice is limited since this type of records represents only a small portion of documents found in process facilities – See page 3);
updating the portion of the one or more causal graphs based on the identified changes in the causal relationships (the developed algorithm correctly performed the recognition of linking artifacts among the analyzed models, merging, removing, and updating the associated XML-tree structures as required – See page 91);
updating the custom view of the industrial automation system (such a procedure comprises two main parallel tasks: (a) the event-triggered generation of dynamic causal digraphs (DCDGs), i.e., modular causal models interconnected based on plant connectivity information, and (b) the recursive consultation of a propagation look-up table (PLUT) containing general and process-specific information for establishing updated causalities within and among plant components – See page 112),
Von does not disclose
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs; and
presenting the updated custom view within the GUI.
Liu discloses
wherein the update comprises replacing the portion of the one or more causal graphs with the updated portion of the one or more causal graphs (With the ability to visualize causal graphs and the root cause of incidents, practitioners can quickly gain insights and improve their workflow efficiency – See Abstract); and
presenting the updated custom view within the GUI (these causal graphs can be reconstructed from the topology of a specific application (i.e., domain knowledge from log analysis and trace analysis) – See page 6).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Liu’s teaching into Von’s invention because incorporating Liu’s teaching would enhance Von to reconstruct the casual graphs from the topology as suggested by Liu (page 6).
Regarding claim 18, the computer readable medium of claim 16,
Von discloses
wherein each design artifact of the plurality of design artifacts comprises a portion of PLC code, a piping and instrumentation diagram (P&ID) digital design, a digital twin model, a portion of Intelligent Device code, or any combination thereof (generated P&ID and CLD models have been regarded as separate information artifacts – See page 64).
Regarding claim 19, the computer readable medium of claim 16,
Von discloses
wherein the one or more causal graphs represent the one or more industrial automation devices as nodes (causal graph having nodes as pipe, valve – See Fig. 6.5, page 116) and the input/output relationships between the one or more industrial automation devices as edges between the nodes (connects nodes within a given row and symbolizes existing bonds between variables in a determined piece of equipment due to a physical principle; for instance, the relation temperature-pressure in a two-phase closed tank (due to the ideal gas law) – See Fig. 6.5 and page 116).
Regarding claim 20, recites the same limitations as rejected claim 5 above.
9. Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Von and Liu as applied to claims 1 and 9 respectively above, and further in view of Jan Wilch (A Distributed Framework for Knowledge-Driven Root-Cause Analysis on Evolving Alarm Data–An Industrial Case Study, 2023 – herein after Wilch).
Regarding claim 4, the system of claim 1,
Wilch discloses
wherein the custom view comprises the causal graphs organized according to hierarchical relationships, relative physical positions of the one or more industrial automation components, or both (Results are compared to the previously detected and postprocessed alarm sets, and only newly detected ones are further processed. Others are stored as additional occurrences of known alarm sets. Postprocessing proceeds by gathering relevant information for each individual set that confirms or refutes a causal relationship between the alarms – See page 3735).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Wilch’s teaching into Von’s and Liu’s inventions because incorporating Wilch’s teaching would enhance Von and Liu to enable to conduct root cause analysis in an interactive way as suggested by Wilch (page 3735).
Regarding claim 12, recites the same limitations as rejected claim 4 above.
10. Claim(s) 7-8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Von and Liu as applied to claims 6, 1 and 9 respectively above, and further in view of Cella et al. (US Pub. No. 2020/0225655 A1 – herein after Cella).
Regarding claim 7, the system of claim 6,
Cella discloses
wherein an unexpected update to the custom view indicates a security breach, an unauthorized control modification within the industrial automation system, or both (data security, including long-term security due to storage media, geographic, and/or unauthorized access, is considered throughout the data storage life cycle. An example system further includes the organizing structures providing enhanced resolution of the number of sensor values in response to at least one of an enhanced data request value or an alert value corresponding to the industrial system – See paragraph [1713]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Von’s and Liu’s inventions because incorporating Cella’s teaching would enhance Von and Liu to enable to data security, including long-term security due to storage media, geographic, and/or unauthorized access, is considered throughout the data storage life cycle as suggested by Cella (paragraph [1713]).
Regarding claim 8, the system of claim 1,
Cella discloses
wherein the database is stored locally in the memory and remotely in a cloud server (the remote repository may act as a storage medium for program code, instructions, and programs – See paragraph [1874]. The predictive maintenance facility 5903 may further be coupled with a local or remote user interface for providing reports, facilitating control, interacting with the predictive maintenance facility 5903 to facilitate user participation in maintenance actions, planning, and analysis – See paragraphs [2215-2216]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Von’s and Liu’s inventions because incorporating Cella’s teaching would enhance Von and Liu to enable to provide the remote repository as suggested by Cella (paragraph [1874]).
Regarding claim 15, the method of claim 14, comprising:
Cella discloses
detecting an occurrence of an unexpected event during operation of the industrial automation system based on an unexpected update to the custom view (the monitoring device may process the detection values to identify unexpected vibrations in the shaft or unexpected temperature values or temperature changes in the bearings or in the housing in proximity to the bearings – See paragraph [0623]); and
identifying a cause of the unexpected event based on the custom view (encrypt the content within the block, so that the content may not be read by unauthorized devices – See paragraph [2287]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Von’s and Liu’s inventions because incorporating Cella’s teaching would enhance Von and Liu to enable to data security, including long-term security due to storage media, geographic, and/or unauthorized access, is considered throughout the data storage life cycle as suggested by Cella (paragraph [1713]).
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singhal et al. (US Pub. No. 2024/0028026 A1) discloses he causal network learns for a dynamic non-stationary and nonlinear complex process or system fault using observed data without any prior process knowledge. The causal networks of faults are identified in real-time using a deep learning-based causal network learning technique. The system identifies causal connections and temporal lag information among variables to generate a directed causal graph of fault called the causal network, which is used to identify fault propagation paths and root cause variables – See Abstract and specification for more details.
Vip et al. (US Patent No. 12,360,878 B1) discloses root cause anomaly that causes other anomalies occurred within a distributed system. The at least one root cause anomaly is determined by at least using a graph that represents the distributed system and metrics that are associated with the distributed system – See Abstract and specification for more details.
Briant et al. (US Pub. No. 2023/0275897 A1) discloses the IIH system serves as a trusted information broker between the ecosystem and the OT environments of plant facilities, and provides a platform for connecting assets, contextualizing asset data and providing secure access to the ecosystem. As part of this ecosystem, the IIH system uses a secure remote access architecture to allow users to remotely access data on their plant floor assets via a virtual private network connection – See Abstract and specification for more details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONGBAO NGUYEN whose telephone number is (571)270-7180. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached at 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MONGBAO NGUYEN/ Examiner, Art Unit 2192