DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office Action is responsive to the communications filed on 6 September 2023. Claims 1-4, 6-11 and 13-17 are pending.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Saha et al. (Hereinafter, Saha, US 2020/0285988 A1) in view of Fisher et al. (Hereinafter, Fisher, US 2015/0081379 A1).
Per claim 1, Saha discloses a processing method for maintenance management information of a plant for processing fluid (e.g., flowchart 500 as shown in Fig. 5; paragraph [0037], “FIG. 5 shows a flowchart 500 that details a method of monitoring risk of failure in the assets 192a-192c of the process plant 190 in accordance with the examples disclosed herein …”), the processing method comprising the steps of:
storing, in a non-transitory storage [[unit]] medium (e.g., data repository 166 as shown in Fig. 1; paragraph [0166]), data about a result of calculating a risk for each one of a plurality of accidents assumed to have a possibility of occurring in a plurality of pieces of equipment included in the plant(paragraph [0026]; paragraph [0038]; Examiner’s Note: Saha discloses an asset performance management (APM) system storing calculated risk regarding plant assets.), a plan view(e.g., 2D visualization as shown in Fig. 9; paragraph [0044]), and a three-dimensional image(e.g., 3D visualization 904 as shown in Fig. 9; paragraph [0044]), the plan view displaying the plurality of pieces of equipment included in the plant with the plurality of pieces of equipment divided into a plurality of blocks(e.g., corrosion loops (CLs) 122 as shown in Fig. 1; Abstract, “ … Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system ... “; paragraph [0015], “ … A corrosion loop can include a section of one or more assets that share common or similar attributes including functioning under similar process conditions …. “ ; paragraph [0042]; Examiner’s Note: Fig. 7 illustrates a plurality of pieces of equipment included in a plant. The plurality of pieces of equipment included in a plant are divided into corrosion loops (CLs) or blocks.), the three-dimensional image indicating placement, in the plant, of each of the plurality of pieces of equipment for which the risk is calculated (paragraph [0018]; paragraph [0028], “…The 3D model 184 therefore provides a visual representation of those locations where the rate of damage is high or critical…. “),
acquiring, by a computer(e.g., tablet computer 184 as shown in Fig. 1), the data of the plan view and the risk which are stored in the non-transitory storage [[unit]] medium (paragraph [0029], “In an example, the multi-dimensional visualizations 182 can be provided at remote user devices such as the tablet computer 184 using a browser, an ‘app’ or other widgets based on the information received at the asset monitoring system 100 ….”; paragraph [0047] ), performing image processing of attaching, to each one of the plurality of blocks in the plan view (e.g., palette 912 as shown in Fig. 9; Abstract; paragraph [0015], “… PE system also provides the ability to visualize the plant assets based on the corrosion loops or by damage mechanisms using a specified color coding. “; paragraph [0019]; paragraph [0028]; paragraph [0032]; paragraphs [0041-0042]; paragraph [0045], “… For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; Examiner’s Note: Examiner is interpreting the corrosion loops (CLs) in Saha to be blocks since paragraph [0015] of Saha describes a CL as comprising assets made of similar materials exposed to similar processes/environmental conditions and asset characteristics ), assessment information indicating a result of assessing a level of the risk calculated for at least one of the plurality of pieces of equipment that is included in the each one of the plurality of blocks(paragraph[0033], “In an example, the PE system 102 can also be configured to display the status information of the assets such as the asset's criticality based on the equipment risk ranking 126 generated by the APM system 112 ….” ), and displaying the plan view with the assessment information attached thereto on a monitor (e.g., ) connected to the computer(paragraph [0019], “ … As the visualizations are communicatively coupled to other elements of the asset monitoring system, the information from other elements such as the CLs, the DMs or the equipment risk ranking can be displayed within the visualizations…. “; paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202. The CLs which were auto-generated at 662 can be further submitted for user validation at 666 ... “; paragraph [0045]; Examiner’s Note: Saha discloses a multi-dimensional visualizations display the assets with a color coding representative of the equipment risk ranking of the asset. ); and
receiving,In an example, various GUIs 180 can be associated with the asset monitoring system 100 to receive input from users and to provide output to the users. For example, input identifying the CLs can be received via one or more of the GUIs 180. Similarly, the CLs marked up by the PE system 102, the various visualizations that are generated and other output can be provided by the GUIs 180. The GUIs 180 can include markup documents that can be displayed via browsers or UIs to be presented in mobile ‘apps’, etc. The two-dimensional visualizations can be generated using SmartPlant P&ID while the 3D visualizations can be generated using SmartPlant 3D in accordance with some examples. “; paragraph [0034]; paragraph [0041], “…The method begins at 652 wherein the training data from the data sources 170 pertaining to the plant assets 192a-192c that include paper documents with P&IDs having CLs marked out, data logs from legacy systems or manual CL inputs received at the PE system 102 etc. is accessed. “; paragraph [0044]; paragraph [0047]; Examiner’s Note: Saha discloses receiving input identifying the CLs or blocks can be received via one or more of the GUIs 180 ), acquiring, by the computer, the data of the three-dimensional image about at least one of the plurality of pieces of equipment that is included in the selected one of the plurality of blocks from the non-transitory storage [[unit]] medium (Abstract; paragraph [0045]), and displaying the three-dimensional image on the monitor(e.g., asset monitoring system 100 as shown in Fig. 1; paragraph [0015]; paragraph [0018]; paragraph [0029]; paragraph [0042]; paragraph [0044]; Examiner’s Note: Saha discloses acquiring status information regarding plant assets and generating multi-dimensional visualizations.), wherein the step of displaying the three-dimensional image on the monitor includes performing image processing of attaching, to one of the plurality of pieces of equipment that is displayed in the three-dimensional image (e.g., step 514; paragraph [0038], “… At 514, the risk ranking can also be used along with the CLS 122 and the DMs 144 for generating one or more multi-dimensional visualizations 182 such as two-dimensional diagrams or three-dimensional visualizations via mobile apps. “), equipment assessment information(paragraph [0015]; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”) indicating a result of assessing highness of the occurrence frequency when the one of the plurality of pieces of equipment is equipment related to the accident-causing event, and a result of assessing highness of the failure probability of normal operation when the one of the plurality of pieces of equipment is the safety device (paragraph [0026], “…For example, if the plant 190 pertains to processing oil, the equipment risk ranking 126 can be generated on conducting risk based inspection (RBI) analysis of the assets 192a-192c. The equipment risk ranking 126 can be a combination of the probability of failure (POF) and Consequence of Failure (COF) …. “; paragraph [0028]; paragraph [0045], “…The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; paragraph [0046];paragraph [0047]; Examiner’s Note: Saha discloses using different colors to indicate a probability of failure.); but does not expressly disclose:
the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents,
Fisher discloses:
the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents (e.g., Initiating Event Frequency (IEF) as shown in Equation (5); paragraph [0084], “ … Each cause is given a certain Initiating Event Frequency (IEF) based on known industry failure rates… “) and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents(e.g., Probability of Failure on Demand (PFD) as shown in Equation (5); paragraph [0084], “ … layers of protection are given a Probability of Failure on Demand (PFD), which represents the fact that layers will not always be effective, due to a variety of possible failures... “; Examiner’s Note: Examiner is broadly and reasonably interpreting a safety device to be a layer of protection.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the safety calculation module of Fisher with Saha’s site machine learning (ML) based asset monitoring system for monitoring the impact of lacking of safeguards and procedures in order to uncover the hidden consequences of failed safeguards as suggested by Fisher(paragraph [0006]).
Per claim 2, Saha and Fisher disclose the processing method for maintenance management information of a plant according to claim 1, wherein the assessment information is information indicating in which one of a plurality of risk assessment ranges an indicator calculated for the at least one of the plurality of pieces of equipment is included (Saha, paragraph [0019]; paragraph [0026], “In an example, the asset monitoring system 100 may also be communicatively coupled to an asset performance management (APM) system 112. In an example, the APM system 112 can be an external asset management system such as Meredium which can be used for calculating an equipment risk ranking 126 for the assets using proprietary formulae peculiar to the applications being used…”; paragraph [0045]; Examiner’s Note: Saha disclose ranking the varied levels of corrosion and degradation based on the dynamic nature of the production process. ), the indicator being used to assess the level of the risk, the plurality of risk assessment ranges being created by dividing a possible range in which the indicator fluctuates (paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “).
Per claim 3, Saha and Fisher disclose the processing method for maintenance management information of a plant according to claim 2,
wherein the assessment information is colors set in association with the plurality of risk assessment ranges and displayable on the monitor (Saha, paragraph [0015], “ “; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”), and
wherein the image processing is processing of rendering one of the colors that indicates one of the plurality of risk assessment ranges in which the indicator calculated for the at least one of the plurality of pieces of equipment is included to the each one of the plurality of blocks (Saha, paragraph [0028], “…Any changes in the equipment risk ranking of the assets can be transmitted to the visualization generator 108 and reflected in the visualizations generated for those assets in real-time. For example, upon completion of the suggested maintenance tasks, the color of an asset may be changed from red/orange to green automatically in real-time in the multi-dimensional visualizations …“).
Per claim 4, Saha and Fisher disclose the processing method for maintenance management information of a plant according to claim 1, wherein, when one of the plurality of blocks includes a plurality of pieces of equipment out of the plurality of pieces of equipment included in the plant, the one of the plurality of blocks is displayed with the assessment information based on an integrated value of the risk that is calculated for each one of the plurality of pieces of equipment attached thereto (Saha, paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202 …. “; paragraph [0042]).
Per claim 7, Saha discloses a processing system for maintenance management information of a plant for processing fluid, the processing system comprising:
a non-transitory storage [[unit]] medium (e.g., data repository 166 as shown in Fig. 1; paragraph [0166]) configured to store data about a result of calculating a risk for each one of a plurality of accidents assumed to have a possibility of occurring in a plurality of pieces of equipment included in the plant(paragraph [0026]; paragraph [0038]; Examiner’s Note: Saha discloses an asset performance management (APM) system storing calculated risk regarding plant assets.), a plan view(e.g., 2D visualization as shown in Fig. 9; paragraph [0044]), and a three-dimensional image(e.g., 3D visualization 904 as shown in Fig. 9; paragraph [0044]), the plan view displaying the plurality of pieces of equipment included in the plant with the plurality of pieces of equipment divided into a plurality of blocks(e.g., corrosion loops (CLs) 122 as shown in Fig. 1; Abstract, “ … Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system ... “; paragraph [0015], “ … A corrosion loop can include a section of one or more assets that share common or similar attributes including functioning under similar process conditions …. “ ; paragraph [0042]; Examiner’s Note: Fig. 7 illustrates a plurality of pieces of equipment included in a plant. The plurality of pieces of equipment included in a plant are divided into corrosion loops (CLs) or blocks.), the three-dimensional image indicating placement, in the plant, of each of the plurality of pieces of equipment for which the risk is calculated(paragraph [0018]; paragraph [0028], “…The 3D model 184 therefore provides a visual representation of those locations where the rate of damage is high or critical…. “);
a hardware processor (e.g., tablet computer 184 as shown in Fig. 1) configured to acquire the data of the plan view and the risk from the non-transitory storage [[unit]] medium, performing image processing of attaching, to each one of the plurality of blocks in the plan view (e.g., palette 912 as shown in Fig. 9; Abstract; paragraph [0015], “… PE system also provides the ability to visualize the plant assets based on the corrosion loops or by damage mechanisms using a specified color coding. “; paragraph [0019]; paragraph [0028]; paragraph [0032]; paragraphs [0041-0042]; paragraph [0045], “… For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; Examiner’s Note: Examiner is interpreting the corrosion loops (CLs) in Saha to be blocks since paragraph [0015] of Saha describes a CL as comprising assets made of similar materials exposed to similar processes/environmental conditions and asset characteristics ), assessment information indicating a result of assessing a level of the risk calculated for at least one of the plurality of pieces of equipment that is included in the each one of the plurality of blocks(paragraph[0033], “In an example, the PE system 102 can also be configured to display the status information of the assets such as the asset's criticality based on the equipment risk ranking 126 generated by the APM system 112 ….” ), and displaying the plan view with the assessment information attached thereto on a monitor (e.g., ) connected to the computer(paragraph [0019], “ … As the visualizations are communicatively coupled to other elements of the asset monitoring system, the information from other elements such as the CLs, the DMs or the equipment risk ranking can be displayed within the visualizations…. “; paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202. The CLs which were auto-generated at 662 can be further submitted for user validation at 666 ... “; paragraph [0045]; Examiner’s Note: Saha discloses a multi-dimensional visualizations display the assets with a color coding representative of the equipment risk ranking of the asset. );
In an example, various GUIs 180 can be associated with the asset monitoring system 100 to receive input from users and to provide output to the users. For example, input identifying the CLs can be received via one or more of the GUIs 180. Similarly, the CLs marked up by the PE system 102, the various visualizations that are generated and other output can be provided by the GUIs 180. The GUIs 180 can include markup documents that can be displayed via browsers or UIs to be presented in mobile ‘apps’, etc. The two-dimensional visualizations can be generated using SmartPlant P&ID while the 3D visualizations can be generated using SmartPlant 3D in accordance with some examples. “; paragraph [0034]; paragraph [0041], “…The method begins at 652 wherein the training data from the data sources 170 pertaining to the plant assets 192a-192c that include paper documents with P&IDs having CLs marked out, data logs from legacy systems or manual CL inputs received at the PE system 102 etc. is accessed. “; paragraph [0044]; paragraph [0047]; Examiner’s Note: Saha discloses receiving input identifying the CLs or blocks can be received via one or more of the GUIs 180 ); and
Examiner’s Note: Saha discloses acquiring status information regarding plant assets and generating multi-dimensional visualizations.) wherein the hardware processor is configured to perform image processing of attaching, to one of the plurality of pieces of equipment that is displayed in the three-dimensional image (e.g., step 514; paragraph [0038], “… At 514, the risk ranking can also be used along with the CLS 122 and the DMs 144 for generating one or more multi-dimensional visualizations 182 such as two-dimensional diagrams or three-dimensional visualizations via mobile apps. “), equipment assessment information(paragraph [0015]; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”) indicating a result of assessing highness of the occurrence frequency when the one of the plurality of pieces of accident-causing equipment, and a result of assessing highness of the failure probability of normal operation when the one of the plurality of pieces of equipment is the safety device (paragraph [0026], “…For example, if the plant 190 pertains to processing oil, the equipment risk ranking 126 can be generated on conducting risk based inspection (RBI) analysis of the assets 192a-192c. The equipment risk ranking 126 can be a combination of the probability of failure (POF) and Consequence of Failure (COF) …. “; paragraph [0028]; paragraph [0045], “…The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; paragraph [0046];paragraph [0047]; Examiner’s Note: Saha discloses using different colors to indicate a probability of failure.); but does not expressly disclose:
the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents.
Fisher discloses the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents (e.g., Initiating Event Frequency (IEF) as shown in Equation (5); paragraph [0084], “ … Each cause is given a certain Initiating Event Frequency (IEF) based on known industry failure rates… “) and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents(e.g., Probability of Failure on Demand (PFD) as shown in Equation (5); paragraph [0084], “ … layers of protection are given a Probability of Failure on Demand (PFD), which represents the fact that layers will not always be effective, due to a variety of possible failures... “; Examiner’s Note: Examiner is broadly and reasonably interpreting a safety device to be a layer of protection. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the safety calculation module of Fisher with Saha’s site machine learning (ML) based asset monitoring system for monitoring the impact of lacking of safeguards and procedures in order to uncover the hidden consequences of failed safeguards as suggested by Fisher(paragraph [0006]).
Per claim 8, Saha discloses a processing system for maintenance management information of a plant for processing fluid, the processing system comprising:
a hardware processor (e.g., asset monitoring system 100 as shown in Fig. 1; paragraph [0022], “FIG. 1 is a block diagram that shows the details of the ML-based asset monitoring system 100 in accordance with the examples disclosed herein. The asset monitoring system 100 monitors assets 192a, 192b, 192c, etc. such as the various equipment in a process plant 190 ... “) configured to acquire, through communication to and from an outside (paragraph [0022], “ …The asset monitoring system 100 can be physically located within the process plant 190 or can be located remotely from the process plant 190 and connected to the process plant 190 via a wireless network such as the internet ... “), data about a result of calculating a risk for each one of a plurality of accidents assumed to have a possibility of occurring in a plurality of pieces of equipment included in the plant(paragraph [0026]; paragraph [0038]; Examiner’s Note: Saha discloses an asset performance management (APM) system storing calculated risk regarding plant assets.), a plan view(e.g., 2D visualization as shown in Fig. 9; paragraph [0044]), and a three-dimensional image(e.g., 3D visualization 904 as shown in Fig. 9; paragraph [0044]), the plan view displaying the plurality of pieces of equipment included in the plant with the plurality of pieces of equipment divided into a plurality of blocks(e.g., corrosion loops (CLs) 122 as shown in Fig. 1; Abstract, “ … Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system ... “; paragraph [0015], “ … A corrosion loop can include a section of one or more assets that share common or similar attributes including functioning under similar process conditions …. “ ; paragraph [0042]; Examiner’s Note: Fig. 7 illustrates a plurality of pieces of equipment included in a plant. The plurality of pieces of equipment included in a plant are divided into corrosion loops (CLs) or blocks.), the three-dimensional image indicating placement, in the plant, of each of the plurality of pieces of equipment for which the risk is calculated (paragraph [0018]; paragraph [0028], “…The 3D model 184 therefore provides a visual representation of those locations where the rate of damage is high or critical…. “),
In an example, the multi-dimensional visualizations 182 can be provided at remote user devices such as the tablet computer 184 using a browser, an ‘app’ or other widgets based on the information received at the asset monitoring system 100 ….”; paragraph [0047] ), performing image processing of attaching, to each one of the plurality of blocks in the plan view (e.g., palette 912 as shown in Fig. 9; Abstract; paragraph [0015], “… PE system also provides the ability to visualize the plant assets based on the corrosion loops or by damage mechanisms using a specified color coding. “; paragraph [0019]; paragraph [0028]; paragraph [0032]; paragraphs [0041-0042]; paragraph [0045], “… For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; Examiner’s Note: Examiner is interpreting the corrosion loops (CLs) in Saha to be blocks since paragraph [0015] of Saha describes a CL as comprising assets made of similar materials exposed to similar processes/environmental conditions and asset characteristics ), assessment information indicating a result of assessing a level of the risk calculated for at least one of the plurality of pieces of equipment that is included in the each one of the plurality of blocks(paragraph[0033], “In an example, the PE system 102 can also be configured to display the status information of the assets such as the asset's criticality based on the equipment risk ranking 126 generated by the APM system 112 ….” ), and displaying the plan view with the assessment information attached thereto on a monitorr(paragraph [0019], “ … As the visualizations are communicatively coupled to other elements of the asset monitoring system, the information from other elements such as the CLs, the DMs or the equipment risk ranking can be displayed within the visualizations…. “; paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202. The CLs which were auto-generated at 662 can be further submitted for user validation at 666 ... “; paragraph [0045]; Examiner’s Note: Saha discloses a multi-dimensional visualizations display the assets with a color coding representative of the equipment risk ranking of the asset. ); and
In an example, various GUIs 180 can be associated with the asset monitoring system 100 to receive input from users and to provide output to the users. For example, input identifying the CLs can be received via one or more of the GUIs 180. Similarly, the CLs marked up by the PE system 102, the various visualizations that are generated and other output can be provided by the GUIs 180. The GUIs 180 can include markup documents that can be displayed via browsers or UIs to be presented in mobile ‘apps’, etc. The two-dimensional visualizations can be generated using SmartPlant P&ID while the 3D visualizations can be generated using SmartPlant 3D in accordance with some examples. “; paragraph [0034]; paragraph [0041], “…The method begins at 652 wherein the training data from the data sources 170 pertaining to the plant assets 192a-192c that include paper documents with P&IDs having CLs marked out, data logs from legacy systems or manual CL inputs received at the PE system 102 etc. is accessed. “; paragraph [0044]; paragraph [0047]; Examiner’s Note: Saha discloses receiving input identifying the CLs or blocks can be received via one or more of the GUIs 180 );
Examiner’s Note: Saha discloses acquiring status information regarding plant assets and generating multi-dimensional visualizations.), wherein the hardware processor is configured to perform image processing of attaching, to one of the plurality of pieces of equipment that is displayed in the three-dimensional image (e.g., step 514; paragraph [0038], “… At 514, the risk ranking can also be used along with the CLS 122 and the DMs 144 for generating one or more multi-dimensional visualizations 182 such as two-dimensional diagrams or three-dimensional visualizations via mobile apps. “), equipment assessment information(paragraph [0015]; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”) indicating a result of assessing highness of the occurrence frequency when the one of the plurality of pieces of equipment is equipment related to the accident-causing event, and a result of assessing highness of the failure probability of normal operation when the one of the plurality of pieces of equipment is the safety device (paragraph [0026], “…For example, if the plant 190 pertains to processing oil, the equipment risk ranking 126 can be generated on conducting risk based inspection (RBI) analysis of the assets 192a-192c. The equipment risk ranking 126 can be a combination of the probability of failure (POF) and Consequence of Failure (COF) …. “; paragraph [0028]; paragraph [0045], “…The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “; paragraph [0046];paragraph [0047]; Examiner’s Note: Saha discloses using different colors to indicate a probability of failure.); but does not expressly disclose:
the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents.
Fisher discloses the risk being a value obtained by multiplying an occurrence frequency of an accident-causing event which is likely to cause the each one of the plurality of accidents (e.g., Initiating Event Frequency (IEF) as shown in Equation (5); paragraph [0084], “ … Each cause is given a certain Initiating Event Frequency (IEF) based on known industry failure rates… “) and a failure probability of normal operation of a safety device for preventing occurrence of the each one of the plurality of accidents(e.g., Probability of Failure on Demand (PFD) as shown in Equation (5); paragraph [0084], “ … layers of protection are given a Probability of Failure on Demand (PFD), which represents the fact that layers will not always be effective, due to a variety of possible failures... “; Examiner’s Note: Examiner is broadly and reasonably interpreting a safety device to be a layer of protection. ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the safety calculation module of Fisher with Saha’s site machine learning (ML) based asset monitoring system for monitoring the impact of lacking of safeguards and procedures in order to uncover the hidden consequences of failed safeguards as suggested by Fisher(paragraph [0006]).
Per claim 9, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 7, wherein the assessment information is information indicating in which one of a plurality of risk assessment ranges an indicator calculated for the at least one of the plurality of pieces of equipment is included (Saha, paragraph [0019]; paragraph [0026], “In an example, the asset monitoring system 100 may also be communicatively coupled to an asset performance management (APM) system 112. In an example, the APM system 112 can be an external asset management system such as Meredium which can be used for calculating an equipment risk ranking 126 for the assets using proprietary formulae peculiar to the applications being used…”; paragraph [0045]; Examiner’s Note: Saha disclose ranking the varied levels of corrosion and degradation based on the dynamic nature of the production process. ), the indicator being used to assess the level of the risk, the plurality of risk assessment ranges being created by dividing a possible range in which the indicator fluctuates (paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “).
Per claim 10, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 9,
wherein the assessment information is colors set in association with the plurality of risk assessment ranges and displayable on the monitor (Saha, paragraph [0015], “ “; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”), and
wherein the hardware processor is configured to execute processing of rendering one of the colors that indicates one of the plurality of risk assessment ranges in which the indicator calculated for the at least one of the plurality of pieces of equipment is included to the each one of the plurality of blocks (Saha, paragraph [0028], “…Any changes in the equipment risk ranking of the assets can be transmitted to the visualization generator 108 and reflected in the visualizations generated for those assets in real-time. For example, upon completion of the suggested maintenance tasks, the color of an asset may be changed from red/orange to green automatically in real-time in the multi-dimensional visualizations …“).
Per claim 11, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 7, wherein, when one of the plurality of blocks includes a plurality of pieces of equipment out of the plurality of pieces of equipment included in the plant, thehardware processor is configured to execute image processing so that the one of the plurality of blocks is displayed with the assessment information based on an integrated value of the risk that is calculated for each one of the plurality of pieces of equipment attached thereto(Saha, paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202 …. “; paragraph [0042]).
Per claim 15, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 8, wherein the assessment information is information indicating in which one of a plurality of risk assessment ranges an indicator calculated for the at least one of the plurality of pieces of equipment is included (Saha, paragraph [0019]; paragraph [0026], “In an example, the asset monitoring system 100 may also be communicatively coupled to an asset performance management (APM) system 112. In an example, the APM system 112 can be an external asset management system such as Meredium which can be used for calculating an equipment risk ranking 126 for the assets using proprietary formulae peculiar to the applications being used…”; paragraph [0045]; Examiner’s Note: Saha disclose ranking the varied levels of corrosion and degradation based on the dynamic nature of the production process. ), the indicator being used to assess the level of the risk, the plurality of risk assessment ranges being created by dividing a possible range in which the indicator fluctuates (paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes. For example, a palette 912 including a 5×5 matrix shows the color coding of red, green and orange indicating the criticality of various assets. The visualizations use the color coding data to show the asset within the visualization 904 in the corresponding color. “).
Per claim 16, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 15,
wherein the assessment information is colors set in association with the plurality of risk assessment ranges and displayable on the monitor (Saha, paragraph [0015], “ “; paragraph [0028], “ … The multi-dimensional representations or the multi-dimensional visualizations of the assets can include color coding such as red, orange and green to indicate if the asset is in a critical state, an acceptable state or a good state respectively. An asset in a critical state may be shown in red in the multi-dimensional visualizations …. “; paragraph [0045], “ …The visualizations can be configured to display the risk ranking, thickness or other asset attributes indicating the criticality of the assets via color codes…”), and
wherein thehardware processor is configured to execute processing of rendering one of the colors that indicates one of the plurality of risk assessment ranges in which the indicator calculated for the at least one of the plurality of pieces of equipment is included to the each one of the plurality of blocks (Saha, paragraph [0028], “…Any changes in the equipment risk ranking of the assets can be transmitted to the visualization generator 108 and reflected in the visualizations generated for those assets in real-time. For example, upon completion of the suggested maintenance tasks, the color of an asset may be changed from red/orange to green automatically in real-time in the multi-dimensional visualizations …“).
Per claim 17, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 8, wherein, when one of the plurality of blocks includes a plurality of pieces of equipment out of the plurality of pieces of equipment included in the plant, the hardware processor is configured to execute image processing so that the one of the plurality of blocks is displayed with the assessment information based on an integrated value of the risk that is calculated for each one of the plurality of pieces of equipment attached thereto (Saha, paragraph [0041], “… The assets that make up each of the CLs can be marked via overlays generated in specific colors corresponding to the CLs to outline the assets in the 2D representations 202 …. “; paragraph [0042]).
Claims 6, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Saha et al. (Hereinafter, Saha, US 2020/0285988 A1) in view of Fisher et al. (Hereinafter, Fisher, US 2015/0081379 A1), and in further view of Kitamaki et al. (Hereinafter, Kitamaki, US 2020/0288086 A1).
Per claim 6, Saha and Fisher disclose the processing method for maintenance management information of a plant according to claim 1, but do not expressly disclose:
wherein the step of storing in the non-transitory storage [[unit]] medium includes further storing data of a video indicating operation steps of the plant in the storage unit, the operation steps being for a case in which the highness of the occurrence frequency exceeds a level set in advance when the each one of the plurality of pieces accident-causing
wherein the processing method further comprises a step of receiving, non-transitory storage [[unit]] medium, and displaying the video on the monitor in a replayable manner.
Kitamaki discloses:
wherein the step of storing in the storage unit includes further storing data of a video indicating operation steps of the plant in the storage unit, the operation steps being for a case in which the highness of the occurrence frequency exceeds a level set in advance when the each one of the plurality of pieces of equipment is equipment related to the accident-causing event, or a case in which the highness of the failure probability of normal operation exceeds a level set in advance when the each one of the plurality of pieces of equipment is the safety device, or a case in which an accident related to the pieces of equipment is assumed to occur(e.g., first monitor 27 as shown in Fig. 10; ‘Storage Device’ – paragraphs [0042-0050]; paragraph [0078]; paragraph [0108]; Kitamaki discloses capturing an abnormality recorded by a camera monitoring a target device.), and
wherein the processing method further comprises a step of receiving, via the input unit, selection of one of the plurality of pieces of equipment displayed in the step of displaying the three-dimensional image on the monitor(e.g., seventh area A7 as shown in Fig. 6), acquiring, by the computer, data of the video about the selected one of the plurality of pieces of equipment from the storage unit(e.g., ninth area A9 as shown in Fig. 6; paragraph [0066-0067]), and displaying the video on the monitor in a replayable manner (‘Monitor Display’ – paragraphs [0051-0071]; Examiner’s Note: As shown in Fig. 6, Kitamaki displays a video of the abnormality and other information such as the location and time of the abnormality’s occurrence.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the monitoring system of Kitamaki with Saha and Fisher’s site machine learning (ML) based asset monitoring system to make it easier to later confirm under what circumstances and how an abnormality occurred in a monitored target device as suggested by Kitamaki (paragraph [0004]).
Per claim 13, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 7, but do not expressly disclose:
wherein the non-transitory storage [[unit]] medium is further configured to store a video indicating operation steps of the plant, the operation steps being for a case in which the highness of the occurrence frequency exceeds a level set in advance when the each one of the plurality of pieces of equipment is accident-causing equipment
wherein the hardware processor is configured to receive selection of one of the plurality of pieces of equipment displayed in the step of displaying the three-dimensional image on the monitor, and the
Kitamaki discloses:
wherein the non-transitory storage [[unit]] medium is further configured to store a video indicating operation steps of the plant, the operation steps being for a case in which the highness of the occurrence frequency exceeds a level set in advance when the each one of the plurality of pieces of equipment is accident-causing equipment discloses capturing an abnormality recorded by a camera monitoring a target device.), and
wherein the hardware processor is configured to receive selection of one of the plurality of pieces of equipment displayed in the step of displaying the three-dimensional image on the monitor, and the Examiner’s Note: As shown in Fig. 6, Kitamaki displays a video of the abnormality and other information such as the location and time of the abnormality’s occurrence.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the monitoring system of Kitamaki with Saha and Fisher’s site machine learning (ML) based asset monitoring system to make it easier to later confirm under what circumstances and how an abnormality occurred in a monitored target device as suggested by Kitamaki (paragraph [0004]).
Per claim 14, Saha and Fisher disclose the processing system for maintenance management information of a plant according to claim 8, but do not expressly disclose:
wherein the hardware processor is further configured to store a video indicating operation steps of the plant, the operation steps being for a case in which the highness of the occurrence frequency exceeds a level set in advance when the each one of the plurality of pieces of equipment is accident-causing equipment
Kitamaki discloses:
the plurality of pieces of equipment from the storage unit(e.g., ninth area A9 as shown in Fig. 6; paragraph [0066-0067]), and display the video on the monitor in a replayable manner (‘Monitor Display’ – paragraphs [0051-0071]; Examiner’s Note: As shown in Fig. 6, Kitamaki displays a video of the abnormality and other information such as the location and time of the abnormality’s occurrence.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to use the monitoring system of Kitamaki with Saha and Fisher’s site machine learning (ML) based asset monitoring system to make it easier to later confirm under what circumstances and how an abnormality occurred in a monitored target device as suggested by Kitamaki (paragraph [0004]).
Response to Arguments
Interview Summary
Examiner acknowledges applicant’s remarks regarding the interview on 10/16/2025.
Claim Interpretations - 35 U.S.C. 112(f)
In view of applicant’s amendments to the claims, Examiner withdraws claim interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, on
claims 1-4, 4-11 and 13-17.
Claim Rejections under 35 U.S.C. 103
Applicant's arguments filed 10 November 2025 have been fully considered but they are not persuasive.
The Applicant argues that the cited references fail to disclose “the step of displaying the three-dimensional image on the monitor includes performing image processing of attaching, to one of the plurality of pieces of equipment that is displayed in the three-dimensional image, equipment assessment information indicating a result of assessing highness of the occurrence frequency when the one of the plurality of pieces of equipment is accident-causing equipment, and a result of assessing highness of the failure probability of normal operation when the one of the plurality of pieces of equipment is the safety device” as recited in claim 1 since Saha merely discloses "using different colors to indicate a probability of failure of the asset" without separating the asset into accident-causing equipment and safety device.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., separating the asset into accident-causing equipment and safety device) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Moreover, Saha disclose displaying three dimensional (3D) representations indicating asset criticality within the visualizations (paragraph [0019]; paragraph [0027]). Saha discloses "using different colors to indicate a probability of failure of the asset" on the 3D representation. Accordingly, Saha does suggest separating the asset into accident-causing equipment and safety device. Moreover, Fisher discloses separating the asset into accident-causing equipment and safety device (Abstract; paragraph [0007]; paragraph [0033]). Based on the above, Examiner respectfully submits that Saha and Fisher fail disclose at least the features as highlighted above in claim 1.
Regarding independent claims 7 and 8, for the same reasons set forth above of claim 1, claims 7 and 8 are not patentable over the cited reference. Accordingly, the rejections on claim 1 is maintained.
Regarding dependent claim 2-4, 6, 9-11 and 13-18, claims 2-4, 6, 9-11 and 13-18 do not overcome the rejections as a matter of law, for at least the reasons that claims 2-4, 6, 9-11 and 13-18 contain all features of independent claims 1, 7 and 8, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hanson (US 2008/0222102 A1) - An apparatus for providing a safety management center includes a communication element, a memory device, a reporting element and a planning element.
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DARRIN HOPE
Examiner
Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178