DETAILED ACTION
This is a non-final office action on the merits. Claims 1-20 are pending and addressed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/4/2024 is being considered by the examiner.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (Zhiqiang Yin, Lin Shi, Yang Yuan, Xinxin Tan and Shoukun Xu, A Study on a Knowledge Graph Construction Method of Safety Reports for Process Industries, 3 January 2023, MDPI, pages 1-22; a reference in IDS 9/4/2024) in view of KUMAR et al. (US 20230196242).
Regarding claims 1, 15, 18, Yin et al. teaches:
operations comprising:
receiving industrial data collected from an industrial automation system during performance of an industrial automation process;
applying an asset model to the industrial data to contextualize the industrial data;
determining event data based on the industrial data;
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities);
generating a knowledge graph based on the industrial data and the event data;
identifying an event based on the event data;
determining one or more causes of the event based on the knowledge graph;
determining one or more remedies based on the one or more causes; and
providing for display the one or more remedies for the event based on the knowledge graph;
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19);
Yin et al. does not explicitly teach:
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processing system to perform operations;
providing for display the one or more remedies includes providing for display via a graphical user interface (GUI);
However, KUMAR et al. teaches:
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processing system to perform operations;
providing for display the one or more remedies includes providing for display via a graphical user interface (GUI);
(at least figs. 3-4 [0073]-[0102] discussed computer system 302, dashboard visualization component 348, in particular [0096]-[0097]) to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management ([0073]-[0102]);
It would have been obvious to one of ordinary skill in the art at the time of filing and at the time of the invention to modify the system and method of Yin et al. with a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processing system to perform operations; and providing for display the one or more remedies includes providing for display via a graphical user interface (GUI); as taught by KUMAR et al. to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management.
Regarding claim 2, Yin et al. teaches:
wherein the operations comprise determining one or more symptoms, one or more causes, or both, of the event based on the knowledge graph
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19);
Regarding claim 3, Yin et al. teaches:
wherein the operations comprise determining the one or more remedies based on the one or more causes. (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19);
Regarding claim 4, Yin et al. teaches:
wherein the operations comprise providing one or more recommendations based on the one or more causes
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19);
Regarding claim 5, Yin et al. teaches:
wherein the industrial data comprises human data, machine data, enterprise data, or any combination thereof (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19);
Regarding claim 6, Yin et al. teaches:
wherein the event data comprises one or more human events, one or more machine events, or both (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10);
Regarding claim 7, Yin et al. teaches:
wherein the operations comprise identifying the event based on one or more rules, wherein the one or more rules comprise one or more conditions for triggering the event (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10, at least page 7 discussed identifying causes, causes would trigger event; fig. 2 page 10);
Regarding claim 8, Yin et al. teaches:
wherein identifying the event is based on an artificial intelligence (AI) model, wherein the AI model comprises a machine learning algorithm (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10, page 5 and pages 9-10 discussed IEC/TS 62832-1 standard,” artificial intelligence-based INERM model”, “an advanced industrial safety information extraction model, INERM, which is a named entity information extraction model with a mixture of multilayer neural network and machine learning”);
Regarding claim 9, Yin et al. teaches:
wherein the asset model comprises a representation of an expected condition of an industrial automation device in the industrial automation system (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 pages 9-10);
Regarding claim 10, Yin et al. teaches:
wherein the asset model is based on one or more industry standards (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10, page 5 and pages 9-10 discussed IEC/TS 62832-1 standard,” artificial intelligence-based INERM model”, “an advanced industrial safety information extraction model, INERM, which is a named entity information extraction model with a mixture of multilayer neural network and machine learning”);
Regarding claim 11, Yin et al. teaches:
wherein the operations comprise receiving a library of one or more additional asset models (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10, page 5 and pages 9-10 discussed IEC/TS 62832-1 standard,” artificial intelligence-based INERM model”, “an advanced industrial safety information extraction model, INERM, which is a named entity information extraction model with a mixture of multilayer neural network and machine learning”);
Regarding claim 12, Yin et al. teaches:
wherein the knowledge graph comprises one or more nodes connected by one or more edges (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10)
Regarding claim 13, Yin et al. teaches:
wherein the one or more nodes represent one or more objects, one or more locations, one or more events, or any combination thereof (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10)
Regarding claim 14, Yin et al. teaches:
wherein the one or more edges represent one or more symptoms, one or more causes, the one or more remedies, one or more recommendations, a feedback loop, or any combination thereof (at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10)
Regarding claim 16, Yin et al. teaches:
knowledge graph includes one or more remedies;
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10)
Yin et al. does not explicitly teach:
wherein the operations comprise receiving feedback on the knowledge graph;
However, KUMAR et al. teaches:
wherein the operations comprise receiving feedback on the knowledge graph;
(at least figs. 3-4 [0073]-[0102] discussed computer system 302, dashboard visualization component 348, in particular [0096]-[0097]; [0092]-[0097] discussing adjusting operation limits, settings, discussed “updates the knowledge graph data structure based on the one or more insights”) to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management ([0073]-[0102]);
It would have been obvious to one of ordinary skill in the art at the time of filing and at the time of the invention to modify the system and method of Yin et al. with wherein the operations comprise receiving feedback on the knowledge graph; as taught by KUMAR et al. to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management.
Regarding claim 17, Yin et al. teaches:
knowledge graph includes one or more remedies;
(at least fig. 1 pages 4-7 discussed safety reports, knowledge of process industry, receiving data collected from systems during process; discussed decomposition/parsing of safety reports, using international standard; cause entities, resulting entities, recommended entities; figs. 5-6 pages 17-19; fig. 2 page 10)
Yin et al. does not explicitly teach:
wherein the operations comprise updating the knowledge graph based on the feedback;
However, KUMAR et al. teaches:
wherein the operations comprise updating the knowledge graph based on the feedback;
(at least figs. 3-4 [0073]-[0102] discussed computer system 302, dashboard visualization component 348, in particular [0096]-[0097]; [0092]-[0097] discussing adjusting operation limits, settings, discussed “updates the knowledge graph data structure based on the one or more insights”) to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management ([0073]-[0102]);
It would have been obvious to one of ordinary skill in the art at the time of filing and at the time of the invention to modify the system and method of Yin et al. with wherein the operations comprise updating the knowledge graph based on the feedback; as taught by KUMAR et al. to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management.
Regarding claim 19, Yin et al. does not explicitly teach:
generating, via the processing system, one or more alarms based on the one or more events;
However, KUMAR et al. teaches:
generating, via the processing system, one or more alarms based on the one or more events;
(at least figs. 3-4 [0073]-[0102] discussed computer system 302, dashboard visualization component 348, in particular [0096]-[0097]; [0033]-[0038] discussed alerts) to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management and to present information associated with the knowledge graph and/or the one or more insights associated with the one or more assets ([0073]-[0102] [0033]-[0038]);
It would have been obvious to one of ordinary skill in the art at the time of filing and at the time of the invention to modify the system and method of Yin et al. with generating, via the processing system, one or more alarms based on the one or more events; as taught by KUMAR et al. to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management and to present information associated with the knowledge graph and/or the one or more insights associated with the one or more assets
Regarding claim 20, Yin et al. does not explicitly teach:
presenting, via the GUI, a visualization of the one or more alarms to a user;
However, KUMAR et al. teaches:
presenting, via the GUI, a visualization of the one or more alarms to a user;
(at least figs. 3-4 [0073]-[0102] discussed computer system 302, dashboard visualization component 348, in particular [0096]-[0097]; [0033]-[0038] discussed alerts) to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management and to present information associated with the knowledge graph and/or the one or more insights associated with the one or more assets ([0073]-[0102] [0033]-[0038]);
It would have been obvious to one of ordinary skill in the art at the time of filing and at the time of the invention to modify the system and method of Yin et al. with presenting, via the GUI, a visualization of the one or more alarms to a user; as taught by KUMAR et al. to facilitate a practical application of knowledge graph generation and/or contextualization of data for a knowledge graph to provide optimization related to enterprise data management and to present information associated with the knowledge graph and/or the one or more insights associated with the one or more assets
Conclusion
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BAO LONG T. NGUYEN
Examiner
Art Unit 3664
/BAO LONG T NGUYEN/Primary Examiner, Art Unit 3656