CTNF 19/019,708 CTNF 93625 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority The instant application claims priority to European Patent Application No. 24152269.7, filed January 16, 2024. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Regarding Claims 1-14, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-14 are directed to the abstract idea of reporting the status of a plant. Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites receiving a query to generate a plant status report; determining at least one plant parameter status information of at least one plant parameter of interest based on the query, wherein the plant parameter status information comprises operational information of the plant parameter of interest; and generating the plant status report including the determined plant parameter status information. These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)). Accordingly, the claim recites an abstract idea and dependent claims 4-8, 10-11 and 13 further recite the abstract idea. Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an artificial intelligence module. The artificial intelligence module is merely a generic computing device and does not integrate the judicial exception into a practical application. With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1-3, 9, 12 and 14 include various elements that are not directed to the abstract idea under 2A. These elements include an artificial intelligence module, user interface, sensors, a large language model and the generic computing elements described in the Applicant's specification in at least Para 0075. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. Therefore, Claims 1-3, 9, 12 and 14, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more. Regarding Claims 15-17, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 15-17 are directed to the abstract idea of reporting the status of a plant. Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 15, claim 15 recites receiving a query to generate a plant status report; determining at least one plant parameter status information of at least one plant parameter of interest based on the query, wherein the plant parameter status information comprises operational information of the plant parameter of interest; and generating the plant status report including the determined plant parameter status information. These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)). Accordingly, the claim recites an abstract idea. Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an artificial intelligence module. The artificial intelligence module is merely a generic computing device and does not integrate the judicial exception into a practical application. With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 15-17 include various elements that are not directed to the abstract idea under 2A. These elements include an artificial intelligence module, one or more sensors, at least one user interface and the generic computing elements described in the Applicant's specification in at least Para 0075. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions Therefore, Claims 15-17, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more. Regarding Claim 18, it is directed to a computer readable medium, however the claims are directed to a judicial exception without significantly more. Claim 18 is directed to the abstract idea of reporting the status of a plant. Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 18, claim 18 recites receiving a query to generate a plant status report; determining at least one plant parameter status information of at least one plant parameter of interest based on the query, wherein the plant parameter status information comprises operational information of the plant parameter of interest; and generating the plant status report including the determined plant parameter status information. These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)). Accordingly, the claim recites an abstract idea. Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an artificial intelligence module. The artificial intelligence module is merely a generic computing device and does not integrate the judicial exception into a practical application. With respect to 2B, the claim does not include additional elements amounting to significantly more than the abstract idea. Claim 18 includes various elements that are not directed to the abstract idea under 2A. These elements include an artificial intelligence module and the generic computing elements described in the Applicant's specification in at least Para 0075. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. Therefore, Claim 18 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more. Furthermore, the language used by the applicant(s) does not exclude non-statutory forms of computer program products such as signals or carrier waves. Therefore, these claims are non-statutory. The Office recommends amending these claims to recite the term "non-transitory" in the preamble so that the scope of the claim is only limited to non-transitory computer readable media. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-3, 6, 8-10, and 15-18 is/are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Nalala Pochaiah (US 2023/0169071 A1), here by known as Nalala . Regarding Claim 1, Nalala teaches the limitations of Claim 1 which state receiving by an artificial intelligence (AI) module a query to generate a plant status report (Nalala: Para 0019 via The custom query engine 112 further comprises a (ML/AI) algorithm for auto-generating custom queries and contextual information, as described in further detail below. The display 116 (i.e., visualization software application and/or an interactive bot) is configured to accept input data from a user 118 that is representative of system data that the user intends to access from a set of data tables, as described in further detail below); determining by the AI module at least one plant parameter status information of at least one plant parameter of interest based on the query, wherein the plant parameter status information comprises operational information of the plant parameter of interest (Nalala: Para 0019, 0024 via The memory 108 is configured to store one more processor executable instructions 110, the processor-executable instructions further including a machine learning/artificial intelligence (ML/AI) custom query engine 112. The custom query engine 112 further comprises a (ML/AI) algorithm for auto-generating custom queries and contextual information, as described in further detail below. The display 116 (i.e., visualization software application and/or an interactive bot) is configured to accept input data from a user 118 that is representative of system data that the user intends to access from a set of data tables, as described in further detail below…The sources 104 are in communication with database 102, and are configured to transmit the system data to the database. In various embodiments of the current invention, the industrial parameters measured by sources 104 could comprise one or more of the following industrial automation control system data: key performance indicator templates, alarms, input/output, local variables, tag names, disabled points, hardware, library code, memory allocations, user privileges, audit management, timers, temperatures, molding parameters, work-in-process, inventory, backlog, through-put, cycle times, material properties, safety data, quality assurance, tracking data, customer data, or asset information, among other things…The situations in which the custom query engine with contextual analytics can be effective include, but are not limited to: 1) for weekly report on “Alarm floods>20 alarm rate in 10 minutes”, 2) weekly reports for “Most Frequent Alarms with Threshold Limit Alarm 50 Count”, 3) percentage contribution of top ten most frequent alarms to the overall alarm load, 4) priority information to give low priority alarms KPI, 5) update Master Alarm Database (MADB) for each control system block or compound, 6) comparison of the MADB set points limits against the block ranges, 7) for monthly “alarms per week period, with threshold limit 50”, or 8) for a controller modes report along with the tag details for <7 days and >7 days); and generating the plant status report including the determined plant parameter status information (Nalala: Para 0024 via The situations in which the custom query engine with contextual analytics can be effective include, but are not limited to: 1) for weekly report on “Alarm floods>20 alarm rate in 10 minutes”, 2) weekly reports for “Most Frequent Alarms with Threshold Limit Alarm 50 Count”, 3) percentage contribution of top ten most frequent alarms to the overall alarm load, 4) priority information to give low priority alarms KPI, 5) update Master Alarm Database (MADB) for each control system block or compound, 6) comparison of the MADB set points limits against the block ranges, 7) for monthly “alarms per week period, with threshold limit 50”, or 8) for a controller modes report along with the tag details for <7 days and >7 days). Regarding Claim 2, Nalala teaches the limitations of Claim 2 which state wherein the query is generated by a user via a user interface that is adapted to receive user input (Nalala: Para 0020-0021 via the custom query engine receives input data representative of the system data that the user intends to access at display 116 in order to generate a custom query. The user input would be trained or retrained on the custom query engine based on the contextual information (e.g., analytics) to, for example, make the custom query engine more generalized. The custom query engine 112, using the training dataset, parses the input data for one or more keywords and identifies one or more tables associated with the keywords… The method then comprises receiving, from user 118, input data representative of the system data that the user intends to access in the data tables for generating a custom query…). Regarding Claim 3, Nalala teaches the limitations of Claim 3 which state further comprising sending the query from the user interface to the Al module; and after generating the plant status report, sending the generated plant status report to the user (Nalala: Para 0020-0021 via the custom query engine receives input data representative of the system data that the user intends to access at display 116 in order to generate a custom query. The user input would be trained or retrained on the custom query engine based on the contextual information (e.g., analytics) to, for example, make the custom query engine more generalized. The custom query engine 112, using the training dataset, parses the input data for one or more keywords and identifies one or more tables associated with the keywords… The method then comprises receiving, from user 118, input data representative of the system data that the user intends to access in the data tables for generating a custom query…Next, the method comprises outputting an output of best fit multiple custom queries executed, the output retrieving selected system data from the one or more data tables in response to the custom queries. Next, the method comprises generating a visualization, the visualization displaying information representative of the results of the executed custom query). Regarding Claim 6, Nalala teaches the limitations of Claim 6 which state wherein the plant status report comprises at least one of text information, image information, video information, audio information, statistical information, numerical values, tables, visualizations and/or figures (Nalala: Para 0021 via Next, the method comprises outputting an output of best fit multiple custom queries executed, the output retrieving selected system data from the one or more data tables in response to the custom queries. Next, the method comprises generating a visualization, the visualization displaying information representative of the results of the executed custom query). Regarding Claim 8, Nalala teaches the limitations of Claim 8 which state wherein the at least one plant parameter status information includes past status information comprising data of the at least one plant parameter of interest acquired at a past time instance or range (Nalala: Para 0019 via The sources 104 could be comprised of sensors, field equipment, or other system parameter measurement devices. The sources 104 can further include system configurations, engineering parameters, assets/inventory data, and historical or other relevant data from the entire control system employed in an industry or plant). Regarding Claim 9, Nalala teaches the limitations of Claim 9 which state wherein one or more of the data of the current status information or the past status information is detected by sensors adapted to acquire operational information of the at least one plant parameter of interest (Nalala: Para 0019 via The sources 104 could be comprised of sensors, field equipment, or other system parameter measurement devices. The sources 104 can further include system configurations, engineering parameters, assets/inventory data, and historical or other relevant data from the entire control system employed in an industry or plant). Regarding Claim 10, Nalala teaches the limitations of Claim 10 which state wherein the plant parameter status information includes or is based on information of historical documents (Nalala: Para 0019 via The sources 104 could be comprised of sensors, field equipment, or other system parameter measurement devices. The sources 104 can further include system configurations, engineering parameters, assets/inventory data, and historical or other relevant data from the entire control system employed in an industry or plant). Regarding Claim 14, Nalala teaches the limitations of Claim 14 which state wherein the AI module comprises at least one of an intent identifying module adapted to identify an intent of the query, a large language model (LLM) module adapted to create an LLM output based on current, past or forecasted status information of the plant parameter of interest, and a generative Al module adapted to generate the plant status report (Nalala: Para 0019-0020 via The memory 108 is configured to store one more processor executable instructions 110, the processor-executable instructions further including a machine learning/artificial intelligence (ML/AI) custom query engine 112. The custom query engine 112 further comprises a (ML/AI) algorithm for auto-generating custom queries and contextual information, as described in further detail below. The display 116 (i.e., visualization software application and/or an interactive bot) is configured to accept input data from a user 118 that is representative of system data that the user intends to access from a set of data tables, as described in further detail below. The processor 106 is in communication with the display 116, the memory 108, and the database 102 in order to facilitate communication between the various components. In addition, the processor 106 is configured to execute custom query engine 112 such that the custom query engine performs specific operations… The user input would be trained or retrained on the custom query engine based on the contextual information (e.g., analytics) to, for example, make the custom query engine more generalized. The custom query engine 112, using the training dataset, parses the input data for one or more keywords and identifies one or more tables associated with the keywords). Regarding Claim 15, it is analogous to Claim 1 and is rejected for the same reasons (Nalala: Para 0029). Regarding Claim 16, it is analogous in nature to Claim 9 and is rejected for the same reasons. Regarding Claim 17, it is analogous in nature to Claim 2 and is rejected for the same reasons. Regarding Claim 18, it is analogous to Claim 1 and is rejected for the same reasons (Nalala: Para 0029) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 4 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalala Pochaiah (US 2023/0169071 A1) in view of Narayan et al. (US 2025/0078007 A1) . Regarding Claim 4, while Nalala teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 4 which states further comprising determining control instructions for controlling one or more plant components based on the determined plant parameter status information. Narayan though, with the teachings of Nalala, teaches of further comprising determining control instructions for controlling one or more plant components based on the determined plant parameter status information (Narayan: Para 0023 via The controller 18 is configured to train a KPI forecast model for each of the KPIs 22, 24 that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs 22, 24 that are identified by the plurality of tags. Each of the KPI forecast models may be an Artificial Intelligence (AI) based model. In some cases, one or more of the KPI forecast models may be trained continuously using updated current and historical values for at least some of the KPIs 22, 24, and thus may continuously learn and track the characteristics of the industrial process. In other cases, one or more of the KPI forecast models may be trained during a training phase, and then the trained model is released for use during an operational phase. In either case, the controller 18 may be configured to generate a forecasted KPI value for each of the KPIs 22, 24 identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI 22, 24. In some instances, the controller 18 may be configured to automatically adjust one or more parameters of the industrial process 12 based at least in part on one or more of the forecasted KPI values). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala with the teachings of Narayan in order to have further comprising determining control instructions for controlling one or more plant components based on the determined plant parameter status information. The motivations behind this being to incorporate the teachings of predicting KPI values, plant states and controlling processes in an industrial process as taught by Narayan. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. Regarding Claim 11, while Nalala teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 11 which states wherein the plant parameter status information includes information about at least one of a plant component, a key performance indicator (KPI) of the plant component and/or a process variable (29). Narayan though, with the teachings of Nalala, teaches of wherein the plant parameter status information includes information about at least one of a plant component, a key performance indicator (KPI) of the plant component and/or a process variable (29) (Narayan: Para 0023 via The controller 18 is configured to receive via the I/O port 14 a plurality of tags that each identify a corresponding KPI 22, 24 of the industrial process 12. In some cases, the plurality of tags only tag certain KPIs of the industrial process 12 that are deemed critical to the operation and control of the industrial process 12. The controller 18 is configured to receive via the I/O port 14 historical values for the KPIs 22, 24 that are identified by the plurality of tags. The controller 18 is configured to train a KPI forecast model for each of the KPIs 22, 24 that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs 22, 24 that are identified by the plurality of tags. Each of the KPI forecast models may be an Artificial Intelligence (AI) based model. In some cases, one or more of the KPI forecast models may be trained continuously using updated current and historical values for at least some of the KPIs 22, 24, and thus may continuously learn and track the characteristics of the industrial process). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala with the teachings of Narayan in order to have wherein the plant parameter status information includes information about at least one of a plant component, a key performance indicator (KPI) of the plant component and/or a process variable (29). The motivations behind this being to incorporate the teachings of predicting KPI values, plant states and controlling processes in an industrial process as taught by Narayan. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention . 07-21-aia AIA Claim (s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalala Pochaiah (US 2023/0169071 A1) in view of Narayan et al. (US 2025/0078007 A1) further in view of Bhat (US 2022/0066425 A1) . Regarding Claim 5, while Nalala/Narayan teaches the limitations of Claim 4, it does not explicitly disclose the limitation of Claim 5 which states generating the plant status report including the determined control instructions. Bhat though, with the teachings of Nalala/Narayan, teaches of generating the plant status report including the determined control instructions (Bhat: Para 0017, 0032 via FIG. 1 shows a simplified diagram of a control room (100) in a process plant, in an embodiment an industrial plant or a power plant can be considered in place of the process plant. The foregoing disclosure is described with respect to the process plant. However, it should not be construed as a limitation. A person of ordinary skill in the art will appreciate that aspects which are applicable to the industrial plant or power plant falls within the scope of this invention. The process plant can comprise a plurality of control rooms. The present disclosure is described with reference to one control room (100). The control room (100) comprises a plurality of display units (102A . . . 102N) to monitor plant parameters related to at least one process in the process plant. The display units (102A . . . 102N) can also display parameters related to a plurality of equipment in the process plant. For example, the display units (102A . . . 102N) can display a condition of the plurality of equipment. The control room (100) further comprises one or more imaging units (101A . . . 101N) for monitoring the control room (100), such as the display units (102A . . . 102N) and one or more control room operators (103). The one or more control room operators (103) can be experienced operators. The one or more control room operators (103) perform one or more control operations to control the at least one process. For example, the one or more control room operators (103) provide key inputs to maintain temperature of an ongoing process… Referring to FIG. 4, an example is provided to illustrate the working of the invention. As seen in FIG. 4, the display unit (102B) indicates a temperature warning in a process. The temperature warning could occur due to various reasons. The plurality of process parameters associated with the process are displayed on the display units (102A . . . 102N). Upon noticing the temperature warning, the control room operator may take various measures to contain the warning. For example, the control room operator may observe a first set of parameters in the display unit (102A) and vary a second set of parameters in the display unit (102N). The one or more imaging units (101) are configured to capture images/video of the control room operator as the operator performs the control operations to control the process). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala/Narayan with the teachings of Bhat, in order to have generating the plant status report including the determined control instructions. The motivations behind this being to incorporate the teachings of controlling processes in a process plant as taught by Bhat. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention . 07-21-aia AIA Claim (s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalala Pochaiah (US 2023/0169071 A1) in view of Bhat (US 2022/0066425 A1) . Regarding Claim 7, while Nalala teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 7 which state wherein the plant parameter status information includes current status information comprising data of the plant parameter of interest acquired at a current time instance. Bhat though, with the teachings of Nalala, teaches of wherein the plant parameter status information includes current status information comprising data of the plant parameter of interest acquired at a current time instance (Bhat: Para 0028 via the availability is determined further based on one or more sensory parameters of the one or more control room operators (103). For example, if the one or more control room operators (103) are involved in controlling the at least one process, the computing unit (202) determines that the one or more control room operators (103) are unavailable. Further, the computing unit (202) obtains a status associated with the at least one process from the database (204) or can obtain the status in real-time using sensors (not shown) of the DCS (200)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala with the teachings of Bhat in order to have wherein the plant parameter status information includes current status information comprising data of the plant parameter of interest acquired at a current time instance. The motivations behind this being to incorporate the teachings of controlling processes in a process plant and obtaining status information as taught by Bhat. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention . 07-21-aia AIA Claim (s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalala Pochaiah (US 2023/0169071 A1) in view of Entzminger et al. (US 2021/0080941 A1) . Regarding Claim 12, while Nalala teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 12 which states further comprising determining by the AI module a forecast information about a forecasted future state of the plant parameter of interest based on the operational information of the plant parameter of interest, and generating the plant status report including the forecast information. Entzminger though, with the teachings of Nalala, teaches of further comprising determining by the AI module a forecast information about a forecasted future state of the plant parameter of interest based on the operational information of the plant parameter of interest, and generating the plant status report including the forecast information (Entzminger: Para 0035-0036 via This bidirectional communication that may exist between the PLC and the models enables dynamic updates to component performance curve models, allowing for the machine learning aspects of the predictive and preventative models to make adjustments to the performance curves empirically and compensate for changes or declines in performance of various components over time. These adjustments to the performance curves and corresponding updates to the physical models of various industrial assets enables the machine learning models to be dynamically retrained over time to allow for new or changed behavior of the assets to be accounted for in the predictive analysis and preventative action recommendations. Additionally, the continual adjustments to the models to compensate for changes in performance of the various industrial assets over time may allow for the confidence level in the maintenance event predictions generated by the predictive and preventative models to be increased, since these determinations would be based on more accurate representations of the underlying assets. In some implementations, the maintenance event predictions may be presented along with their associated confidence levels to better enable a user to make a decision as to whether or not to take preventative action… FIG. 3 is a block diagram that illustrates an exemplary graphical display of computing system 300 in an exemplary implementation. Computing system 300 provides an example of computing system 101 of FIG. 1, although computing system 101 could use alternative configurations. In this example, computing system 300 includes display system 301 which provides a graphical user interface for an industrial automation application, which could comprise human-machine interface (HMI) software in some implementations). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala with the teachings of Entzminger in order to have further comprising determining by the AI module a forecast information about a forecasted future state of the plant parameter of interest based on the operational information of the plant parameter of interest, and generating the plant status report including the forecast information. The motivations behind this being to incorporate the teachings of predictive maintenance for industrial assets in an industrial automation environment as taught by Entzminger. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention . 07-21-aia AIA Claim (s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalala Pochaiah (US 2023/0169071 A1) in view of Qian et al. (US 2022/0058589 A1) . Regarding Claim 13, while Nalala teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 13 which state wherein the plant status report comprises a shift handover report comprising information for a handover from a first shift of plant operators to a second shift of plant operators, wherein the shift handover report includes a summary of one or more plant parameters of interest for the second shift of plant operators. Qian though, with the teachings of Nalala, teaches of wherein the plant status report comprises a shift handover report comprising information for a handover from a first shift of plant operators to a second shift of plant operators, wherein the shift handover report includes a summary of one or more plant parameters of interest for the second shift of plant operators (Qian: Para 0061, 0065 via System environment 900 includes a plant server platform 902 configured to interface with one or more operator devices/operator consoles 904a, 904b for receiving operator shift reports from outgoing and/or incoming shift operators 906a, 906b and for enabling editing/viewing/consolidating of such operator shift reports (for example in the form of work shift handover reports) by a shift supervisor or other incoming/outgoing shift operators 906a/906b. The plant server platform 902 may be configured to generate one or more work shift handover report(s) 908 based on data (in the form of operator shift reports) recorded and submitted by operators from an outgoing/concluding shift. The generated work shift handover report(s) 908 is generated based on selection of specific data records/data logs from one or more operator shift reports, supervisor logs and/or database records, and may be viewed by operators within one or more subsequent shifts for the purposes of executing shift related tasks…Step 1002 comprises receiving a plurality of operator shift reports. The plurality of operator shift reports may be received from one or more operator devices, client devices, or remote devices from which shift operators input or submit their individual operator shift reports. One or more of the received operator shift reports may include any of text based, image based, video based or audio based logs or notes or readings, or data records generated by an individual shift operator. In an embodiment, the plurality of operator shift reports may be stored in a central repository and receiving them at step 1002 may comprise receiving them from the central repository. In a particular embodiment, the plurality of operator shift reports are received at operations management server 9022). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nalala with the teachings of Qian in order to have wherein the plant status report comprises a shift handover report comprising information for a handover from a first shift of plant operators to a second shift of plant operators, wherein the shift handover report includes a summary of one or more plant parameters of interest for the second shift of plant operators. The motivations behind this being to incorporate the teachings of management of work shift handover reports within industrial plants. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Demaagd et al. (US 2024/0338529 A1) Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at 571-272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /T.E.S./Examiner, Art Unit 3625 /BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625 Application/Control Number: 19/019,708 Page 2 Art Unit: 3625 Application/Control Number: 19/019,708 Page 3 Art Unit: 3625 Application/Control Number: 19/019,708 Page 4 Art Unit: 3625 Application/Control Number: 19/019,708 Page 5 Art Unit: 3625 Application/Control Number: 19/019,708 Page 6 Art Unit: 3625 Application/Control Number: 19/019,708 Page 7 Art Unit: 3625 Application/Control Number: 19/019,708 Page 8 Art Unit: 3625 Application/Control Number: 19/019,708 Page 9 Art Unit: 3625 Application/Control Number: 19/019,708 Page 10 Art Unit: 3625 Application/Control Number: 19/019,708 Page 11 Art Unit: 3625 Application/Control Number: 19/019,708 Page 12 Art Unit: 3625 Application/Control Number: 19/019,708 Page 13 Art Unit: 3625 Application/Control Number: 19/019,708 Page 14 Art Unit: 3625 Application/Control Number: 19/019,708 Page 15 Art Unit: 3625 Application/Control Number: 19/019,708 Page 16 Art Unit: 3625 Application/Control Number: 19/019,708 Page 17 Art Unit: 3625 Application/Control Number: 19/019,708 Page 18 Art Unit: 3625 Application/Control Number: 19/019,708 Page 19 Art Unit: 3625 Application/Control Number: 19/019,708 Page 20 Art Unit: 3625 Application/Control Number: 19/019,708 Page 21 Art Unit: 3625 Application/Control Number: 19/019,708 Page 22 Art Unit: 3625 Application/Control Number: 19/019,708 Page 23 Art Unit: 3625
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