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
This is a non-final, first office action on the merits.
Claims 1-20 are pending.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 12/07/2023 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Claim Rejections 35 USC §101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-11 are directed to a method (process), claims 12-19 are directed to a computer program and claim 20 is directed to a system (machine). Examiner note that the computer program product is not to be construed as being transitory signals per se in view of applicant specification ¶[0041]. Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-20 are directed toward the judicial exception of an abstract idea.
Independent claims 12, and 20 recites essentially the same abstract features as claim 1, thus are abstract for the same reason as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A computer-implemented method, comprising:
sensing, by a processor set, an abnormal event in an equipment topology;
locating, by the processor set, an abnormal asset within a context awareness map;
creating, by the processor set, an asset contextual reactive model for the located abnormal asset based on a work order history and domain knowledge;
building, by the processor set, a comprehensive problem statement based on outputs from the asset contextual reactive model and a correlation and context machine learning model, the context awareness map, and data from sensors in the equipment topology;
searching for at least one candidate solution based on the comprehensive problem statement; (claim 20 only)
running, by the processor set, a plurality of remedies for at least one candidate solution based on the asset contextual reactive model and the comprehensive problem statement; and providing, by the processor set, a recommended solution based on running the plurality of remedies for the at least one candidate solution.
The Applicant's Specification titled "DYNAMIC ASSET MAINTENANCE MANAGEMENT" emphasizes the business need for data analysis, "In summary, the present disclosure relates to provide a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data " (Spec. fig. 2).
As the bolded claim limitations above demonstrate, independent claims 1, 12 and 20 are recites the abstract idea of provide a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution tagging data to provide a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data. Applicant's claimed invention pertains to Certain methods of organizing human activity –commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and fundamental economic principles or practices (including hedging, insurance, mitigating risk);” See MPEP §2106.04(a)(2)(II).
Dependent claims 2-11, and 13-19 further reiterate the same abstract ideas with further embellishments (the bolded limitations), such as
claim 2 (Similarly claim 13) finding a location of the abnormal asset corresponding with the abnormal event; and
identifying a correlation and context of a failure related to the abnormal event based on the asset contextual reactive model and the correlation and context machine learning model.
claim 3 (Similarly claim 14) further comprising collecting a running situation for the located abnormal asset.
claim 4 (Similarly claim 15) further comprising searching for the at least one candidate solution based on the comprehensive problem statement.
claim 5 (Similarly claim 16) wherein the equipment topology comprises an Internet of Things (IoT) equipment topology.
claim 6 (Similarly claim 17) creating the context awareness map to model the equipment topology;
simulating a target asset based on the contextual reactive model and the comprehensive problem statement; and
validating the target asset based on the contextual reactive model and the comprehensive problem statement.
claim 7 (Similarly claim 18) further comprising filtering out at least one incorrect solution from the at least one candidate solution.
claim 8 comprising storing the recommended solution as feedback data for the work order history.
claim 9 (Similarly claim 19) creating the correlation and context machine learning model based on the context awareness map, the work order history, an operation troubleshooting guide, and feedback;
storing the recommended solution as training data for the asset contextual reactive model and the correlation and context machine learning model.
claim 10 wherein the operation troubleshooting guide comprises a symptom of the failure, a cause of the failure, and a potential remedy of the failure.
claim 11 wherein the comprehensive problem statement comprises a plurality of assets and a description of the abnormal values of the assets.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 12, and 20.
Regarding Step 2A [prong 2]
Claims 1-20 fail to integrate the abstract idea into a practical application. Independent claims 1, 12, and 20 include the following additional elements which do not amount to a practical application:
Claim 1. A processor set, model, and a machine learning model
Claim 7. A computer program product, model, and a machine learning model
Claim 13. A system, model, and a machine learning model
The bolded limitations recited above in independent claims 1, 12 and 20 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of A system, computer program product, model, and a machine learning model which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, (fig. 1). Nothing in the Specification describes the specific operations recited in claims 1, 12 and 20 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for providing a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention provide a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-11, and 13-19 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 12, and 20 but, these features only serve to further limit the abstract idea of independent claims 1, 12, and 20. furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claims 1, 12, and 20 include a system, computer program product, model, and a machine learning model. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to provide a recommended solution based on running a plurality of remedies for at least one solution the system identify based on different data.
Claims 1-20 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will be followed/begin by an “EN” that will denote an examiner note. This will be place to further explain a rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bromfield et al. US 2025/0103039 (hereinafter Bromfield).
Regarding Claim 1:
A computer-implemented method, comprising:
sensing, by a processor set, an abnormal event in an equipment topology; (Bromfield [0005], “monitoring and controlling industrial assets to mitigate predicted future faults … a future fault for the asset”. [0064-0066], “multiple faults … considered unhealth … a sensor fault which provides …. For each parameter when an advisory or warning is generated by the asset model”. [0129], “batch events module … receive batch events”. [0154], “event logs, files, and contextual data”. Also, see [0182])
locating, by the processor set, an abnormal asset within a context awareness map; (Bromfield [0050], “a location where the data was collected … a piece of equipment it is associated with (e.g., an asset) …. From a sensor assorted with a piece of equipment”. [0072], “the asset advisory 1202 may include a unique identification, location, asset identification …. Description, and status of the advisory (fig. 14)”. EN: Figure 14 disclose a context awareness for the abnormal asset”. Also, see fig. 29A-29B”. )
creating, by the processor set, an asset contextual reactive model for the located abnormal asset based on a work order history and domain knowledge; (Bromfield [0060-0072], “contextualizes data by using pre-built industry specific asset models … collected by one or more measurement devices ( e.g., sensors, monitors, meters, etc.) and sent to the model inputs 504. The model inputs 504 include data that is fed into the asset models 506. In some embodiments, the model inputs 504 can include a data historian 510, site system data 512. and plant system data 514. The data historian 510 may be a database which stores timeseries data associated with the assets 502” [0068], “specific type of asset where details of a given type of asset … AI/ML models are created for both individual assets and for entire asset class”. Also, see [0049], [0081], and [0155])
building, by the processor set, a comprehensive problem statement based on outputs from the asset contextual reactive model and a correlation and context machine learning model, the context awareness map, and data from sensors in the equipment topology; (Bromfield figures 10-15 [0068-0078], “The artificial intelligence/machine learning model is a model which is defined for a specific type of asset where details of a given type of asset like parts, performance behavior, lifetime, and use are stored and used. An asset class is a group of similar assets, like all heat exchangers, conveyors, ball mills, etc. In some embodiments, AI/ML models are created for both individual assets and for entire asset classes … dashboard to describe the health status … relative number of active advisories based on their severity level … details about the asset”. [0082], “associated with sensors … the asset sensor …. Generate asset context 722data contextualized refers to tagging or mapping the data … the contextualized data … generating predictions regarding the assets using asset models”. [0094], “sensors … the data contextualize may configure, connect, map, and contextualize the data”. Also, see [0109], [0132], [0155], [0162], [0167], figure 14 disclose the context awareness. Also, figure 23-27)
running, by the processor set, a plurality of remedies for at least one candidate solution based on the asset contextual reactive model and the comprehensive problem statement; and providing, by the processor set, a recommended solution based on running the plurality of remedies for the at least one candidate solution. (Bromfield [0067], “a recommendation for addressing any issues”. [0072], “diagnostic details 1406 which describes the determined fault or problem with the asset. The user interface 1400 may include any previous diagnostic details 1408 for the asset. The user interface 1400 may also include recommendation details 1410 which describes any recommended actions the asset intelligence management system 500 suggests taking to resolve the fault with the asset. The user interface 1400 may also include a current health score 1412 and predicted future health score 1414 which may be determined as described above”. [0122], “identified one or more corrective action can be taken”. Also, see [0150], [0085], figure 14 element 1410.)
Regarding Claim 2:
Bromfield disclose the computer-implemented method of claim 1, further comprising:
Bromfield further teach finding a location of the abnormal asset corresponding with the abnormal event; and (Bromfield [0050], “a location where the data was collected … a piece of equipment it is associated with (e.g., an asset) …. From a sensor assorted with a piece of equipment”. [0072], “the asset advisory 1202 may include a unique identification, location, asset identification …. Description, and status of the advisory (fig. 14)”. EN: Figure 14 disclose a context awareness for the abnormal asset”. Also, see fig. 29A-29B”. )
identifying a correlation and context of a failure related to the abnormal event based on the asset contextual reactive model and the correlation and context machine learning model. (Bromfield figures 10-15 [0068-0078], “The artificial intelligence/machine learning model is a model which is defined for a specific type of asset where details of a given type of asset like parts, performance behavior, lifetime, and use are stored and used. An asset class is a group of similar assets, like all heat exchangers, conveyors, ball mills, etc. In some embodiments, AI/ML models are created for both individual assets and for entire asset classes … dashboard to describe the health status … relative number of active advisories based on their severity level … details about the asset”. [0082], “associated with sensors … the asset sensor …. Generate asset context 722data contextualized refers to tagging or mapping the data … the contextualized data … generating predictions regarding the assets using asset models”. [0094], “sensors … the data contextualize may configure, connect, map, and contextualize the data”. Also, see [0109], [0132], [0155], [0162], [0167], figure 14 disclose the context awareness. Also, figure 23-27)
Regarding Claim 3:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach further comprising collecting a running situation for the located abnormal asset. (Bromfield [0050], “all the timeseries data collected at a certain site and from a sensor associated with a piece of equipment will be tagged as being associated with that site and with that piece of equipment”. [0063-0064], “data about the assets may be collected by one or more measurement devices … described as a measurement of how well an asset is operating”. [0078], “provide additional information about the asset that data is being collected about in order to provide context regarding that sensor data”. Also, see [0124], [0167])
Regarding Claim 4:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach further comprising searching for the at least one candidate solution based on the comprehensive problem statement. (Bromfield [0067], “a recommendation for addressing any issues”. [0072], “diagnostic details 1406 which describes the determined fault or problem with the asset. The user interface 1400 may include any previous diagnostic details 1408 for the asset. The user interface 1400 may also include recommendation details 1410 which describes any recommended actions the asset intelligence management system 500 suggests taking to resolve the fault with the asset. The user interface 1400 may also include a current health score 1412 and predicted future health score 1414 which may be determined as described above”. [0122], “identified one or more corrective action can be taken”. Also, see [0150], [0085], figure 14 element 1410)
Regarding Claim 5:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach wherein the equipment topology comprises an Internet of Things (IoT) equipment topology. (Bromfield [0108], “The batch performance analytics system 1800 is an industry solution that acts to automatically monitor the batch production process by the enterprise 1600 to improve the batch performance in such manufacturing processes. The batch performance analytics system 1800 is an industrial internet of things (IIoT) solution which provides KPI dashboarding and analytics capabilities from disparate data sources. Specifically, the batch performance analytics system 1800 automates the aggregation and contextualization of batch data from the disparate data sources, correlates key variables that can impact the KPis, and provides KPI roll up capabilities for supervisory visualization from the unit level to the enterprise level”.)
Regarding Claim 8:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach further comprising storing the recommended solution as feedback data for the work order history. (Bromfield [0085], “Advisory KPI may include a past diagnosis for an asset, a present diagnosis for an asset, and a recommendation for addressing any issues for each sub-component within an asset”. Also, see [0067])
Regarding Claim 9:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach further comprising:
creating the correlation and context machine learning model based on the context awareness map, the work order history, an operation troubleshooting guide, and feedback; (Bromfield [0060-0072], “contextualizes data by using pre-built industry specific asset models … collected by one or more measurement devices ( e.g., sensors, monitors, meters, etc.) and sent to the model inputs 504. The model inputs 504 include data that is fed into the asset models 506. In some embodiments, the model inputs 504 can include a data historian 510, site system data 512. and plant system data 514. The data historian 510 may be a database which stores timeseries data associated with the assets 502” [0068], “specific type of asset where details of a given type of asset … AI/ML models are created for both individual assets and for entire asset class”. Also, see [0049], [0081], and [0155])
storing the recommended solution as training data for the asset contextual reactive model and the correlation and context machine learning model. (Bromfield [0067], “a recommendation for addressing any issues”. Bromfield [0068], “The development and training of the asset models 506 is done leveraging historical sensor and PLC data in conjunction with the known downtime history of the asset which are received from the model inputs 504 … The artificial intelligence/machine learning model is a model which is defined for a specific type of asset where details of a given type of asset like parts, performance behavior, lifetime, and use are stored and used. An asset class is a group of similar assets”. [0072], “diagnostic details 1406 which describes the determined fault or problem with the asset. The user interface 1400 may include any previous diagnostic details 1408 for the asset. The user interface 1400 may also include recommendation details 1410 which describes any recommended actions the asset intelligence management system 500 suggests taking to resolve the fault with the asset. The user interface 1400 may also include a current health score 1412 and predicted future health score 1414 which may be determined as described above”. [0122], “identified one or more corrective action can be taken”. Also, see [0150], [0085], figure 14 element 1410”.)
Regarding Claim 10:
Bromfield disclose the computer-implemented method of claim 9,
Bromfield further teach wherein the operation troubleshooting guide comprises a symptom of the failure, a cause of the failure, and a potential remedy of the failure. (Bromfield figure 6 and figures 10-15 disclose the claim limitations [0013], “displaying the key performance indicators to a user; determining if the key performance indicators are above one or more pre-determined thresholds; and taking a corrective action in response to the key performance indicators being above the one or more pre-determined thresholds. Also, see [0064], [0066-0070])
Regarding Claim 11:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach wherein the comprehensive problem statement comprises a plurality of assets and a description of the abnormal values of the assets. (Bromfield [0060-0072], “contextualizes data by using pre-built industry specific asset models … collected by one or more measurement devices ( e.g., sensors, monitors, meters, etc.) and sent to the model inputs 504. The model inputs 504 include data that is fed into the asset models 506. In some embodiments, the model inputs 504 can include a data historian 510, site system data 512. and plant system data 514. The data historian 510 may be a database which stores timeseries data associated with the assets 502”. figures 10-15 [0068-0078], “The artificial intelligence/machine learning model is a model which is defined for a specific type of asset where details of a given type of asset like parts, performance behavior, lifetime, and use are stored and used. An asset class is a group of similar assets, like all heat exchangers, conveyors, ball mills, etc. In some embodiments, AI/ML models are created for both individual assets and for entire asset classes … dashboard to describe the health status … relative number of active advisories based on their severity level … details about the asset”. [0082], “associated with sensors … the asset sensor …. Generate asset context 722data contextualized refers to tagging or mapping the data … the contextualized data … generating predictions regarding the assets using asset models”. [0094], “sensors … the data contextualize may configure, connect, map, and contextualize the data”. Also, see [0109], [0132], [0155], [0162], [0167], figure 14 disclose the context awareness. Also, figure 23-27)
Regarding Claim 12:
Claim 12 is the computer program product claim corresponding to the method claim 1 rejected above. Therefore, Claim 12 is rejected under the same rational as claim 1.
Regarding Claim 13:
Claim 13 is the computer program product claim corresponding to the method claim 2 rejected above. Therefore, Claim 13 is rejected under the same rational as claim 2.
Regarding Claim 14:
Claim 14 is the computer program product claim corresponding to the method claim 3 rejected above. Therefore, Claim 14 is rejected under the same rational as claim 3.
Regarding Claim 15:
Claim 15 is the computer program product claim corresponding to the method claim 4 rejected above. Therefore, Claim 15 is rejected under the same rational as claim 4.
Regarding Claim 16:
Claim 16 is the computer program product claim corresponding to the method claim 5 rejected above. Therefore, Claim 16 is rejected under the same rational as claim 5.
Regarding Claim 19:
Claim 19 is the computer program product claim corresponding to the method claim 9 rejected above. Therefore, Claim 19 is rejected under the same rational as claim 9.
Regarding Claim 20:
A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
sense an abnormal event in an equipment topology; (Bromfield [0005], “monitoring and controlling industrial assets to mitigate predicted future faults … a future fault for the asset”. [0064-0066], “multiple faults … considered unhealth … a sensor fault which provides …. For each parameter when an advisory or warning is generated by the asset model”. [0129], “batch events module … receive batch events”. [0154], “event logs, files, and contextual data”. Also, see [0182])
locate an abnormal asset within a context awareness map; (Bromfield [0050], “a location where the data was collected … a piece of equipment it is associated with (e.g., an asset) …. From a sensor assorted with a piece of equipment”. [0072], “the asset advisory 1202 may include a unique identification, location, asset identification …. Description, and status of the advisory (fig. 14)”. EN: Figure 14 disclose a context awareness for the abnormal asset”. Also, see fig. 29A-29B”. )
create an asset contextual reactive model for the located abnormal asset based on a work order history and domain knowledge; (Bromfield [0060-0072], “contextualizes data by using pre-built industry specific asset models … collected by one or more measurement devices ( e.g., sensors, monitors, meters, etc.) and sent to the model inputs 504. The model inputs 504 include data that is fed into the asset models 506. In some embodiments, the model inputs 504 can include a data historian 510, site system data 512. and plant system data 514. The data historian 510 may be a database which stores timeseries data associated with the assets 502” [0068], “specific type of asset where details of a given type of asset … AI/ML models are created for both individual assets and for entire asset class”. Also, see [0049], [0081], and [0155])
build a comprehensive problem statement based on outputs from the asset contextual reactive model and a correlation and context machine learning model, the context awareness map, and data from sensors in the equipment topology; (Bromfield figures 10-15 [0068-0078], “The artificial intelligence/machine learning model is a model which is defined for a specific type of asset where details of a given type of asset like parts, performance behavior, lifetime, and use are stored and used. An asset class is a group of similar assets, like all heat exchangers, conveyors, ball mills, etc. In some embodiments, AI/ML models are created for both individual assets and for entire asset classes … dashboard to describe the health status … relative number of active advisories based on their severity level … details about the asset”. [0082], “associated with sensors … the asset sensor …. Generate asset context 722data contextualized refers to tagging or mapping the data … the contextualized data … generating predictions regarding the assets using asset models”. [0094], “sensors … the data contextualize may configure, connect, map, and contextualize the data”. Also, see [0109], [0132], [0155], [0162], [0167], figure 14 disclose the context awareness. Also, figure 23-27)
searching for at least one candidate solution based on the comprehensive problem statement; (Bromfield [0067], “a recommendation for addressing any issues”. [0072], “diagnostic details 1406 which describes the determined fault or problem with the asset. The user interface 1400 may include any previous diagnostic details 1408 for the asset. The user interface 1400 may also include recommendation details 1410 which describes any recommended actions the asset intelligence management system 500 suggests taking to resolve the fault with the asset. The user interface 1400 may also include a current health score 1412 and predicted future health score 1414 which may be determined as described above”. [0122], “identified one or more corrective action can be taken”. Also, see [0150], [0085], figure 14 element 1410)
run a plurality of remedies for the at least one candidate solution based on the asset contextual reactive model and the comprehensive problem statement; and provide a recommended solution based on running the plurality of remedies for the at least one candidate solution. (Bromfield [0067], “a recommendation for addressing any issues”. [0072], “diagnostic details 1406 which describes the determined fault or problem with the asset. The user interface 1400 may include any previous diagnostic details 1408 for the asset. The user interface 1400 may also include recommendation details 1410 which describes any recommended actions the asset intelligence management system 500 suggests taking to resolve the fault with the asset. The user interface 1400 may also include a current health score 1412 and predicted future health score 1414 which may be determined as described above”. [0122], “identified one or more corrective action can be taken”. Also, see [0150], [0085], figure 14 element 1410)
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.
Claims 6, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bromfield et al. US 2025/0103039 (hereinafter Bromfield) in view of Amaru et al. US 7,673,282 (hereinafter Amaru).
Regarding Claim 6:
Bromfield disclose the computer-implemented method of claim 1,
Bromfield further teach further comprising:
creating the context awareness map to model the equipment topology; (Bromfield [0060-0072], “contextualizes data by using pre-built industry specific asset models … collected by one or more measurement devices ( e.g., sensors, monitors, meters, etc.) and sent to the model inputs 504. The model inputs 504 include data that is fed into the asset models 506. In some embodiments, the model inputs 504 can include a data historian 510, site system data 512. and plant system data 514. The data historian 510 may be a database which stores timeseries data associated with the assets 502” [0068], “specific type of asset where details of a given type of asset … AI/ML models are created for both individual assets and for entire asset class”. Also, see [0049], [0081], and [0155]) but, specifically fails to disclose simulating a target asset based on the contextual reactive model and the comprehensive problem statement; and validating the target asset based on the contextual reactive model and the comprehensive problem statement.
However, Amaru teaches the following limitation:
simulating a target asset based on the contextual reactive model and the comprehensive problem statement; and validating the target asset based on the contextual reactive model and the comprehensive problem statement. (Amaru fig. 3 claim 1, a model simulator for simulating the information model, wherein a test instance generator populates data that simulates actual enterprise data; and a model validator for validating a simulation of the information model; a data rationalizer for generating mappings from the plurality of physical data assets to the information model, the plurality of physical data assets comprising data asset constructs, wherein the data rationalizer describes to which business constructs the data asset constructs relate, and wherein the data rationalizer further comprises: a synchronizer for synchronizing updates to at least one physical data asset of the plurality of physical data assets with the information model; and a data thesaurus for automating tasks of data management based on the mappings, wherein the data thesaurus translates business instructions for the information model into corresponding data processing instructions for the plurality of physical data assets, and wherein the data thesaurus translates the business instructions based on the mappings”.)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Bromfield, to include the feature as taught by Amaru, in order to simulate and validate a data received from the system/asset (Amaru fig 3 claim 1). Also, it will improve the functioning of computer systems within an enterprise. Further, the claimed invention is merely a combination of old elements in a similar field of endeavor and, in the combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Bromfield and Amaru, the results of the combination were predictable (see MPEP 2143 A).
Regarding Claim 17:
Claim 17 is the computer program product claim corresponding to the method claim 6 rejected above. Therefore, Claim 17 is rejected under the same rational as claim 6.
Claims 7, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bromfield et al. US 2025/0103039 (hereinafter Bromfield) in view of Sexton et al. US 2020/0366706 (hereinafter Sexton).
Regarding Claim 7:
Bromfield disclose the computer-implemented method of claim 1 but, does not specifically teach or disclose, however, Sexton, in the same field of endeavor teaches further comprising filtering out at least one incorrect solution from the at least one candidate solution. (Sexton [0045-0050], “de-duplicate them and remove solutions that have been superseded by other solutions. As used herein, a ruleset is a set of rules that govern when a solution is to be removed or merged with another and how that merge is to be accomplished. In an example, when solution texts not matching a given ruleset are discovered they are flagged for manual review”. [0057], “filtering rule remove one or more solutions”. [0132], “remove the invalid findings”.)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Bromfield, to include the feature as taught by Sexton, in order to filter solutions by removing incorrect solution from a list of solutions (Sexton [0045-0050]). Also, it will improve the functioning of computer systems within an enterprise. Further, the claimed invention is merely a combination of old elements in a similar field of endeavor and, in the combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Bromfield and Sexton, the results of the combination were predictable (see MPEP 2143 A).
Regarding Claim 18:
Claim 18 is the computer program product claim corresponding to the method claim 7 rejected above. Therefore, Claim 18 is rejected under the same rational as claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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