CTNF 18/651,915 CTNF 84745 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. DETAILED ACTION This action is in response to the application filed on 05/01/2024, in which claims 1-20 are presented for the examination. Information Disclosure Statement The Information Disclosure Statement (IDS) submitted on 05/01/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS statement is being considered by the examiner. Drawings The drawings filed on 05/01/2024 are accepted by the examiner. Claim Rejections - 35 U.S.C. § 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. Claim s 1-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. As to claim 1: Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03. Yes, the claim is to a process. Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Claim limitations include running simulation on collected IoT data, detecting an alert and notifying workers. It is characterized as analyzing information and presenting results. Such activities constitute mental processes that could practically be performed by a human using pen and paper or generic computer. Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No, the limitations “IoT devices, a data repository” is an additional element that are recited at a high level of generality and merely use generic computer components as tools to implement the abstract idea. Claim does not improve computer functioning or network communications or provide a specific technological improvement in simulation technology. Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. The claim does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The additional claim elements individually or in combination does not amount to no more than convention data collection, conventional data analysis using generic computer. The combination of these elements merely automates the abstract idea using generic computer technology and does not amount to significantly more than the abstract idea itself. Accordingly, claim 1 is directed to an abstract idea and does not include additional elements sufficient to amount to significantly more than the abstract idea. Therefore, the claim 1 is not patent eligible under 35 U.S.C. § 101. Dependent claims 2-11 are rejected for the same rationale. As to claim 13: Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03. Yes, the claim is to a machine. Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Claim limitations include running simulation on collected IoT data, detecting an alert and notifying workers. It is characterized as analyzing information and presenting results. Such activities constitute mental processes that could practically be performed by a human using pen and paper or generic computer. Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No, the limitations “IoT devices, a data repository” is an additional element that are recited at a high level of generality and merely use generic computer components as tools to implement the abstract idea. Claim does not improve computer functioning or network communications or provide a specific technological improvement in simulation technology. Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. The claim does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The additional claim elements individually or in combination does not amount to no more than convention data collection, conventional data analysis using generic computer. The combination of these elements merely automates the abstract idea using generic computer technology and does not amount to significantly more than the abstract idea itself. Accordingly, claim 13 is directed to an abstract idea and does not include additional elements sufficient to amount to significantly more than the abstract idea. Therefore, the claim 13 is not patent eligible under 35 U.S.C. § 101. Dependent claims 14-19 are rejected for the same rationale. As to claim 20: Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03. Yes, the claim is to a manufacture or composition of matter. Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Claim limitations include running simulation on collected IoT data, detecting an alert and notifying workers. It is characterized as analyzing information and presenting results. Such activities constitute mental processes that could practically be performed by a human using pen and paper or generic computer. Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No, the limitations “IoT devices, a data repository” is an additional element that are recited at a high level of generality and merely use generic computer components as tools to implement the abstract idea. Claim does not improve computer functioning or network communications or provide a specific technological improvement in simulation technology. Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. The claim does not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The additional claim elements individually or in combination does not amount to no more than convention data collection, conventional data analysis using generic computer. The combination of these elements merely automates the abstract idea using generic computer technology and does not amount to significantly more than the abstract idea itself. Accordingly, claim 20 is directed to an abstract idea and does not include additional elements sufficient to amount to significantly more than the abstract idea. Therefore, the claim 20 is not patent eligible under 35 U.S.C. § 101. Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 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 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 Claim s 1, 13, 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Rakshit (US 2021/0374032) . As per claim 1, 13, 20, Rakshit discloses a computer implement method comprising: storing into a data repository internet of things (IoT) sensor data of a plurality of IoT devices disposed within a workflow environment that includes one or more physical asset (Fig. 2a, IoT devices 221, sensor devices 219, Fig. 9a, step 903, [0036], [0002], Sensors and internet-of-things (IoT) devices connected to the physical asset collect data, [0071], a data collection engine 207. The data collection engine 207 may perform the functions, tasks or operations associated with collecting, organizing, maintaining, formatting and/or storing performance data and other data sets (collectively referred to as “collected datasets 235”) generated by the sensor device(s) 219 IoT device(s) 221, and/or recording system(s) 223 connected to or communicating with the physical asset 217. ); performing a simulation to simulate operating performance of the one or more physical asset disposed within the workflow environment (Fig. 2a, simulation engine 211), wherein the performing the simulation to simulate operating performance of the one or more physical asset disposed within the workflow environment includes using historical IoT data of the IoT sensor data ([0073], [0074], simulations predicting effects of replacing particular parts, adjusting timings, modifying onboard electrical or computing components or even replacing potentially defective sensors 219, IoT devices 221, recording systems 223. Collected datasets 235 collected by the data collection engine 207 may contribute to building one or more simulation models that may be used by the simulation engine 211); detecting, in dependence on the performing the simulation, that an alert condition is present in the workflow environment; and ([0070], the digital twin module 203 may alert the user of the detected changes in performance data ); prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment ([0070], [0071], the digital twin module 203 may alert the user of the detected changes in performance data and request updated information from the user about the physical asset 217 to ascertain one or more changes to the physical asset that may be responsible for the observed performance changes and create a new digital twin model 233 in accordance with the reported changes) . 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 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 of this title, 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 2-5, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit in view of Freeman et al. (US 2022/0237069, referred herein after Freeman) . As per claim 2, 14, Rakshit does not specifically disclose the computer implemented method of claim 1, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on the evaluating; Freeman discloses the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on the evaluating (Fig. 2, step 202, 204, abstract, [0045], [0039], KPI are measured and evaluated to show deviation to a baseline and alert is generated); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. As per claim 3, 15, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker to take action via UE devices of the one or more worker; However, Freeman discloses the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on evaluating (Fig. 2, step 202, 204, abstract, [0045], [0039], KPI are measured and evaluated to show deviation to a baseline and alert is generated); wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker to take action via UE devices of the one or more worker ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406.); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. As per claim 4, 16, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the detecting, in dependence on the performing the simulation that an alert condition is present in the workflow environment includes determining that the alert condition is characterized by one or more predicted KPI parameter value predicted by the simulation failing to satisfy a performance threshold, and ascertaining that the alert condition is characterized by a predictive accuracy of the simulation failing to satisfy an accuracy threshold, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes generating first prompting data in dependence on the determining, and producing second prompting data in dependence on the ascertaining ; However, Freeman discloses wherein the detecting, in dependence on the performing the simulation that an alert condition is present in the workflow environment includes determining that the alert condition is characterized by one or more predicted KPI parameter value predicted by the simulation failing to satisfy a performance threshold, and (Fig. 2, step 206, [0045], [0046], The results of applying each specified algorithm to KPI(s) are then analyzed to determine if any of them exceed the defined thresholds, 206); and ascertaining that the alert condition is characterized by a predictive accuracy of the simulation failing to satisfy an accuracy threshold, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes generating first prompting data in dependence on the determining, and producing second prompting data in dependence on the ascertaining ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406, [0081], subsequent alert ie claimed producing second prompting data); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. As per claim 5, 17, Rakshit does not specifically disclose the computer implemented method of claim 1, wherein the method includes subsequent to the prompting one or more worker within the workflow environment to take action recording data specifying responsive action performed by the one or more worker responsively to the prompting, applying the data specifying the responsive action as training data for training a machine learning predictive model, querying the machine learning predictive model subsequent to the training, and generating subsequent prompting data for prompting at least one worker within the workflow environment in dependence on the querying However, Freeman discloses the method includes subsequent to the prompting one or more worker within the workflow environment to take action [0081], subsequent alert ie claimed producing second prompting data); recording data specifying responsive action performed by the one or more worker responsively to the prompting, applying the data specifying the responsive action as training data for training a machine learning predictive model, querying the machine learning predictive model subsequent to the training, and generating subsequent prompting data for prompting at least one worker within the workflow environment in dependence on the querying ([0063], [0064], wherein the predictive algorithm or pre-defined algorithms (ie machine learning predictive model) that processes the KPI data and threshold to customize/train the algorithm); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action . 07-21-aia AIA Claim s 6, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit and Freeman in view of Cella et al. (US 2022/0108262, referred herein after Cella) . As per claim 6, 18, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually; However, Freeman discloses the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the detecting that the alert condition is present is in dependence on the evaluating (Fig. 2, step 202, 204, abstract, [0045], [0039], KPI are measured and evaluated to show deviation to a baseline and alert is generated); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action; Neither Rakshit nor Freeman discloses wherein the detecting that the alert condition is present is in dependence on the evaluating, the wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually ; However, Cella discloses wherein the detecting that the alert condition is present is in dependence on the evaluating, prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually ([1191], if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, to ensure the component stands out in a virtual reality environment being used to help a user monitor or service the machine, wherein virtual reality session is broadly interpreted as displaying information); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Cella’s industrial digital twin systems with virtualization into Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated allow a user to drilldown and see underlying sensor or input data that is used as an input to the display . 07-21-aia AIA Claim s 7, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit, Freeman and Cella in view of Suthar et al. (US 2020/0134441, referred herein after Suthar) . As per claim 7, 19, Rakshit does not specifically discloses computer implemented method of claim 1, wherein the performing the simulation includes querying a predictive machine learning model that has been trained with training data that includes the historical IoT data of the IoT sensor data, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive machine learning model with use of a test query, wherein the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually; However, Freeman discloses performing the simulation includes querying a predictive machine learning model that has been trained with training data that includes the historical IoT data of the IoT sensor data, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation ([0063], [0064], wherein the predictive algorithm or pre-defined algorithms (ie machine learning predictive model) that processes the KPI data and threshold to customize/train the algorithm); method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, (Fig. 2, step 202, 204, abstract, [0045], [0039], KPI are measured and evaluated to show deviation to a baseline and alert is generated); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action; neither Rakshit nor Freeman discloses wherein the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually; However, Cella discloses the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually ([1191], if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, to ensure the component stands out in a virtual reality environment being used to help a user monitor or service the machine, wherein virtual reality session is broadly interpreted as displaying information); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Cella’s industrial digital twin systems with virtualization into Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated allow a user to drilldown and see underlying sensor or input data that is used as an input to the display; Neither Rakshit nor Freeman, Cella discloses wherein the evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive machine learning model with use of a test query, However, Suthar discloses evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive machine learning model with use of a test query ([0065], [0066], predicted KPI values generated and compared with incoming real time KPI data produced on machine learning model as claimed); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Suthar’s Techniques for adaptive thresholding into Cella’s industrial digital twin systems with virtualization into Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure predictive models deliver actionable, trustworthy insights that support strategic decision-making . 07-21-aia AIA Claim s 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit, Freeman and Verma et al. (US 2024/0248790, referred herein after Verma) . As per claim 8, Rakshit does not specifically disclose the computer implemented method of claim 1, wherein the method includes recording data specifying an historical action of at least one worker within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of the historical impact data a result of performing a candidate action, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the predicting; However, Verma discloses recording data specifying an historical action of at least one worker within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment ([0067], Orchestrator 110 with the nearest neighbor faults identified, can examine audit trail records (including KPI metrics data) of the nearest neighbor historical faults, in order to ascertain a classification for the impact of the historical nearest neighbor faults (e.g., loss of service to customer, degraded service to customer, no service degradation to customer) for the historical nearest neighbor faults); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Verma’s prioritized fault remediation method into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure providing compliance and performance insight. Neither Rakshit nor Verma discloses wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the predicting ; However, Freeman discloses wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the predicting ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406.); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation and Verma’s prioritized fault remediation method because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. As per claim 9, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the method includes recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions; However, Verma discloses recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions ([0067], Orchestrator 110 with the nearest neighbor faults identified, can examine audit trail records (including KPI metrics data) of the nearest neighbor historical faults, in order to ascertain a classification for the impact of the historical nearest neighbor faults (e.g., loss of service to customer, degraded service to customer, no service degradation to customer) for the historical nearest neighbor faults); and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting ([0067], producing scoring values to ascertain a classification for the impact of the historical nearest neighbor faults); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Verma’s prioritized fault remediation method into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure providing compliance and performance insight; Neither Rakshit nor verma discloses wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions; However, Freeman discloses wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406.); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation and Verma’s prioritized fault remediation method because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. As per claim 10, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the method includes recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions, wherein the predicting includes querying a trained machine learning model that has been trained with training data provided by the impact data of the historical impact data; However, Verma discloses recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, ([0067], Orchestrator 110 with the nearest neighbor faults identified, can examine audit trail records (including KPI metrics data) of the nearest neighbor historical faults, in order to ascertain a classification for the impact of the historical nearest neighbor faults (e.g., loss of service to customer, degraded service to customer, no service degradation to customer) for the historical nearest neighbor faults); and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, ([0067], producing scoring values to ascertain a classification for the impact of the historical nearest neighbor faults); wherein the predicting includes querying a trained machine learning model that has been trained with training data provided by the impact data of the historical impact data ([0070], [0071]); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Verma’s prioritized fault remediation method into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure providing compliance and performance insight; Neither Rakshit nor verma discloses wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions; However, Freeman discloses the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406.); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation and Verma’s prioritized fault remediation method because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action . 07-21-aia AIA Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Rakshit, Freeman, Cella, Suthar and Verma . As per claim 11, Rakshit does not specifically discloses the computer implemented method of claim 1, wherein the performing the simulation includes querying a predictive neural network machine learning model that has been trained with training data that includes the historical IoT data of the IoT sensor data, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, wherein the evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive neural network machine learning model with use of a test query, wherein the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually, wherein the method includes recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, wherein the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions, wherein the predicting includes querying a trained machine learning model that has been trained with training data provided by the impact data of the historical impact data; However, Freeman discloses performing the simulation includes querying a predictive machine learning model that has been trained with training data that includes the historical IoT data of the IoT sensor data, wherein the method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation ([0063], [0064], wherein the predictive algorithm or pre-defined algorithms (ie machine learning predictive model) that processes the KPI data and threshold to customize/train the algorithm); Wherein method includes evaluating accuracy of one or more key performance indicator (KPI) prediction resulting from the performing the simulation, (Fig. 2, step 202, 204, abstract, [0045], [0039], KPI are measured and evaluated to show deviation to a baseline and alert is generated); the prompting one or more worker within the workflow environment to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting the one or more worker within the workflow environment to take action in dependence on the ranked order of the respective ones of the candidate actions ([0047], [0048], configuration settings and changes to the collector 406. Any configuration changes made by the user, such as through user interface (UI) service 428 will be either pushed by the collector configuration service 420, or pulled by the collector 406.); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system into Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to improve system responsiveness and reduce delay in corrective action. neither Rakshit nor Freeman discloses wherein the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually; However, Cella discloses the detecting that the alert condition is present is in dependence on the evaluating, wherein the prompting the one or more worker to take action in response to the detecting that the alert condition is present in the workflow environment includes prompting a plurality of workers in the workflow environment to participate in a virtual reality session in which the one or more physical asset within the workflow environment is represented virtually ([1191], if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, to ensure the component stands out in a virtual reality environment being used to help a user monitor or service the machine, wherein virtual reality session is broadly interpreted as displaying information); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Cella’s industrial digital twin systems with virtualization into Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated allow a user to drilldown and see underlying sensor or input data that is used as an input to the display; Neither Rakshit nor Freeman, Cella discloses wherein the evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive machine learning model with use of a test query, However, Suthar discloses evaluating the accuracy of the one or more key performance indicator (KPI) prediction resulting from the performing the simulation includes comparing real time KPI data to predicted KPI data produced on querying the predictive machine learning model with use of a test query ([0065], [0066], predicted KPI values generated and compared with incoming real time KPI data produced on machine learning model as claimed); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Suthar’s Techniques for adaptive thresholding into Cella’s industrial digital twin systems with virtualization into Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure predictive models deliver actionable, trustworthy insights that support strategic decision-making. Neither Rakshit, nor Freeman, Cella, Suthar disclsoes recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, wherein the predicting includes querying a trained machine learning model that has been trained with training data provided by the impact data of the historical impact data ; However, Verma discloses recording data specifying historical action of multiple workers within the workflow environment, storing historical impact data indicating an impact of the historical action on at least one key performance indicator (KPI) of the workflow environment, and predicting with use of impact data of the historical impact data a result of performing a plurality of candidate actions, ([0067], Orchestrator 110 with the nearest neighbor faults identified, can examine audit trail records (including KPI metrics data) of the nearest neighbor historical faults, in order to ascertain a classification for the impact of the historical nearest neighbor faults (e.g., loss of service to customer, degraded service to customer, no service degradation to customer) for the historical nearest neighbor faults); and producing a ranked order of the respective ones of the candidate actions in dependence on the predicting, ([0067], producing scoring values to ascertain a classification for the impact of the historical nearest neighbor faults); wherein the predicting includes querying a trained machine learning model that has been trained with training data provided by the impact data of the historical impact data ([0070], [0071]); Therefore it would have been obvious to the one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Verma’s prioritized fault remediation method into Cella’s industrial digital twin systems with virtualization and Freeman’s intelligent alert reduction in a backup and recovery activity monitoring system and Rakshit’s digital twin workflow simulation because one of the ordinary skill in the art would have been motivated to ensure providing compliance and performance insight . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See form 892 . Ambhore teaches a method for receiving an integrated digital twin including multiple digital twins, each digital twin including a computer-executable model of a real-world system used to execute a portion of a process, within the integrated digital twin. Quigley teaches a method for tokenizing real-world items for use in digital commerce. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMINI B PATEL whose telephone number is (571)270-3902. The examiner can normally be reached on M-F 8-4:30. 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, Ashish Thomas can be reached on 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KAMINI B PATEL/ Primary Examiner, Art Unit 2114 Application/Control Number: 18/651,915 Page 2 Art Unit: 2114 Application/Control Number: 18/651,915 Page 3 Art Unit: 2114 Application/Control Number: 18/651,915 Page 4 Art Unit: 2114 Application/Control Number: 18/651,915 Page 5 Art Unit: 2114 Application/Control Number: 18/651,915 Page 6 Art Unit: 2114 Application/Control Number: 18/651,915 Page 7 Art Unit: 2114 Application/Control Number: 18/651,915 Page 8 Art Unit: 2114 Application/Control Number: 18/651,915 Page 9 Art Unit: 2114 Application/Control Number: 18/651,915 Page 10 Art Unit: 2114 Application/Control Number: 18/651,915 Page 11 Art Unit: 2114 Application/Control Number: 18/651,915 Page 12 Art Unit: 2114 Application/Control Number: 18/651,915 Page 13 Art Unit: 2114 Application/Control Number: 18/651,915 Page 14 Art Unit: 2114 Application/Control Number: 18/651,915 Page 15 Art Unit: 2114 Application/Control Number: 18/651,915 Page 16 Art Unit: 2114 Application/Control Number: 18/651,915 Page 17 Art Unit: 2114 Application/Control Number: 18/651,915 Page 18 Art Unit: 2114 Application/Control Number: 18/651,915 Page 19 Art Unit: 2114 Application/Control Number: 18/651,915 Page 20 Art Unit: 2114 Application/Control Number: 18/651,915 Page 21 Art Unit: 2114 Application/Control Number: 18/651,915 Page 22 Art Unit: 2114 Application/Control Number: 18/651,915 Page 23 Art Unit: 2114 Application/Control Number: 18/651,915 Page 24 Art Unit: 2114 Application/Control Number: 18/651,915 Page 25 Art Unit: 2114 Application/Control Number: 18/651,915 Page 26 Art Unit: 2114 Application/Control Number: 18/651,915 Page 27 Art Unit: 2114 Application/Control Number: 18/651,915 Page 28 Art Unit: 2114 Application/Control Number: 18/651,915 Page 29 Art Unit: 2114 Application/Control Number: 18/651,915 Page 30 Art Unit: 2114 Application/Control Number: 18/651,915 Page 31 Art Unit: 2114 Application/Control Number: 18/651,915 Page 32 Art Unit: 2114
