CTNF 18/154,137 CTNF 102043 DETAILED ACTION The instant application having application number 18/154,137 filled on 1/13/23 present claims 1-18 pending for examination. There are 3 independent claims and 15 dependent claims, all of which are examined below. 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. Information Disclosure Statement The Information Disclosure Statement (IDS) submitted on January 13, 2023, fails to comply with the provisions of 37 CFR 1.98(a)(2) and MPEP § 609.04(a) because the submitted copies of the following Non-Patent Literature (NPL) references were incomplete or illegible: "5 V's of Big Data," Geeks for Geeks, June 28, 2022, 7 pages, available at https://www.geeksforgeeks.org/5-vs-of-big-data/. "Digital Twin – Simulation in Operation," CADFEM, Ansys, 2020, 2 pages. SALVADOR et al., "Machine Learning Soft-Sensors Trained with Digital Twin for Improving Product Quality & Reducing Energy," Inprocess, Beyond Optimize, September 2021, 25 pages. The Examiner independently located legible and complete copies of these documents and considered them during examination. Drawings 06-37 AIA The drawings were received on 1/13/23 . These drawings are accepted . Claim Objections 07-36 AIA The following is a quotation of 35 U.S.C. 112(d): (d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph: Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. Claims 12 and 18 are objected to under 35 U.S.C. 112(d) as being improper dependent claims because they fail to further limit the subject matter of the claims from which they depend and merely duplicate the limitations of claim 6. To resolve this issue, it is recommended that applicant amend claim 12 to depend on claim 7 and amend claim 18 to depend on claim 13, since this appears to have been the intended claim structure. 07-30-03-h AIA Claim Interpretation For the purposes of examining claims 13–18, the ambiguous phrase 'an object type digital twin of... [a] process' is given its broadest reasonable interpretation, regardless of the indefiniteness noted under 35 U.S.C. 112(b). This phrase is interpreted as a digital twin that models, simulates, or tracks an object within a real-world process, such as a vehicle or vehicle component executing a trip or routing mission. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 5, 11, and 13-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 5 recites the term “the machine logic algorithm.” There is insufficient antecedent basis for this limitation in the claims. Correction is required. Claim 11 recites the term “the machine logic algorithm.” There is insufficient antecedent basis for this limitation in the claims. Correction is required. Claim 13 recites “creating an object type digital twin of the real world instantiation of the process,” however it is unclear how a process is associated with an “object type digital twin.” The claim language creates ambiguity as to whether the digital twin is directed to a process, an object, or objects involved in the process. In contrast, claim 1 clearly recites an “environment type digital twin” for an environment and claim 7 recites an “object type digital twin” for an object, which provides clear correspondence between the digital twin type and the claimed subject matter. Here, that relationship is not clear, and therefore the scope of claim 13 is unclear and correction is required. Because claims 14-18 depend from claim 13, this rejection of indefiniteness also applies to these dependent claims. Claim 15 recites the term “the physical object.” There is insufficient antecedent basis for this limitation because claim 15 depends from claim 13, which is directed to a process and does not recite a physical object. Correction is required. Claim 17 recites the term “the machine logic algorithm.” There is insufficient antecedent basis for this limitation in the claims. Correction is required. 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 1-6 are rejected under 35 U.S.C. § 103 as being unpatentable over Stubbs, et al., U.S. Patent Application Publication No. 2021/0133607 A1 (hereinafter "Stubbs") in view of ALBRECHT, et al., U.S. Patent Application Publication No. 2022/0269259 A1 (hereinafter "Albrecht") Regarding Claim 1, Stubbs teaches A computer-implemented method (CIM) comprising: (Stubbs 11, "...some embodiments of the present invention, aspects may include a method of providing power savings in an artificial intelligence of things (AIoT) device, the AIoT device used to monitor or predict a service event for an industrial asset, the device comprising one or more sensors, a machine learning inference engine, a communication module, a data store, a processor, and a power source…") instantiating a real world instantiation of an environment (Stubbs 41, “Systems and methods in accordance with some embodiments of the present invention may monitor and predict anomalous behavior of a wide variety of industrial assets. Predictive analytics may be used to monitor assets in various industries, including but not limited to: manufacturing, pulp and paper, oil and gas, water and wastewater treatment, metals and minerals, infrastructures (e.g., high speed rail systems, roads, bridges, power supplies, etc.), power generation, power distribution (including but not limited to demand response actions, etc.), etc.”) that includes object(s) within the environment and process(es) within the environment and a plurality of sensor devices (Stubbs 42, “Assets and processes that may be monitored include, but are not limited to motors, engines, fans, pumps, valves, compressors, conveyors, heat exchangers, sensors, flow and flow meters, process controls, … screw gas compressors, pipelines, etc.” ; Stubbs 151-152 “For example, although the present invention is predominantly directed to the monitoring and anomaly detection in industrial assets, the technology disclosed can be used for additional purposes and in additional use cases. For example, systems and methods of the present invention may be utilized to remotely monitor health of patients at their homes or health care and nursing home facilities. The device can include various sensors, such as biological sensor that may measure a patient's vital statistics such as temperature, heart rate...”) with the plurality of sensor devices being configured in an initial configuration; (Stubbs 62-64, “In other words, the baseline of “normal” operation of the device is based upon its orientation, and future readings are compared to this baseline. This feature may also be particularly relevant in crowded facilities where other and/or different assets may also be detected by devices… the model learns the operating conditions and circumstances (even seasonal conditions) and can adjust accordingly… The enclosure may further comprise an airflow port for temperature and humidity sensors, as well as ports for microphones… The enclosure shall thermally couple the sensor package to the monitored object. We shall power up the sensor once per minute and check the temperature reading.”) applying predictive analytics to predict a potential issue that has occurred or may occur in the real world instantiation of the environment; (Stubbs 41-43, “Predictive analytics may be used to monitor assets in various industries, including but not limited to: manufacturing, pulp and paper… power distribution (including but not limited to demand response actions, etc.), etc… Assets and processes that may be monitored include, but are not limited to motors, engines, fans, pumps, valves, compressors, conveyors… Predictive analytics at the edge may be used to obtain and provide various types of information and anomaly alerts. For example, systems and methods of the present invention may be utilized to determine situations associated with worn bearings, misaligned shafts, noise from bushings or bearings, prolonged moisture exposure, pressure loss, flow variances, excess temperatures, etc.”) and automatically, by machine logic, determining an improved sensor configuration that will more quickly, precisely and/or accurately detect a real world instantiation of the potential issue in the real world instantiation of the environment; (Stubbs abstract, “The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration."; Stubbs 70-75 “the device may operate in an “anomaly mode,” wherein the system may power up components and engines on the device in a tiered manner and may send anomaly data to the system administrator when an anomalous behavior is detected. In general, one or more sensors may be powered on at scheduled intervals to collect data and process the data to determine anomalous trend in collected data. If an anomaly is detected, the device may power on more sensors and may provide data from sensors to inference engine to classify the anomaly. If the machine learning inference engine classifies the data as anomalous, the device may power up the wireless communication module and send the anomalous data and/or a classification from the machine learning inference engine to a system administrator… vibrations detected above a pre-defined threshold can be analyzed to determine if the pattern or trend of vibration data indicates an anomalous shake or a non-anomalous shake. If the vibration data indicates a non-anomalous shake, the system can power off the sensor for a predetermined amount of time. Thus, using the tiered power management technique, the technology disclosed can operate with very low power consumption.”. The use of sensor data collected from the system is interpreted as data coming from the real-world environment. This data is then provided to the “the machine learning inference engine” to detect anomalies or issues in the environment. This “machine learning inference engine” is interpreted as the machine logic. Finally, the powering on and off of sensors based on “the machine learning inference engine” triggering “anomaly mode” is interpreted as the improved sensor configurations.) and automatically, by machine logic, reconfiguring the plurality of sensors from the initial configuration to the improved configuration. (Stubbs 70, "For example, the device may operate in an 'anomaly mode,' wherein the system may power up components and engines on the device in a tiered manner... In general, one or more sensors may be powered on at scheduled intervals to collect data and process the data... If an anomaly is detected, the device may power on more sensors and may provide data from sensors to inference engine to classify the anomaly”; Stubbs 73-75 “For example, at 410 the device may consume three (3) micro amperes or less when it is in 'deep sleep' state... In this state, all components of the system may be powered off except one sensor… when the device processes a sensor hint at 420... If the sensor hint is above a threshold, the system can gather further data from one or more sensors and provide that to the machine learning inference engine to detect anomalous behavior”; Stubbs 73-75 ... Sensors on the devices can generate interrupts based on thresholds such as amount of vibration on the accelerometer, rate of change of temperature... If so, the system powers up the machine learning inference engine to classify the trends in data.".) Stubbs teaches the use of a digital twin in its automatic machine logic anomaly correction system (Stubbs 97, “During provisioning, the device connects to cloud and may result in a ‘soft device’ being created on the cloud. This may act as the digital twin of the monitoring device.”; Stubbs 86 “Systems and methods of the present invention may generally be broken down into (i) enabling anomaly detection … Enabling anomaly detection may comprise using a backend system component deployed in a cloud that may receive a request (such as from a user interface) when a user enables anomaly detection for a device.” ; Stubbs Figure 7 further illustrates the use of the machine learning inference engine in the MCU digital twin mailbox; ). Stubbs does not appear to explicitly teach creating an environment type digital twin of the real world instantiation of the environment; starting a digital twin simulation of the real world environment to provide digital twin simulation output relating to simulated operations and/or status of simulated objects involved in the digital twin simulation . However, Albrecht does teach creating an environment type digital twin of the real world instantiation of the environment (Albrecht 23, "..invention corresponds to a system for automatically detecting an operational malfunction of a device in the food industry or the beverage industry, in particular for predictive maintenance, which includes a digital twin of the device and a machine learning algorithm."; Albrecht 40 “According to the invention, a digital twin 10B of a (real) device 10A is created... This digital twin 10B has the same physical properties as the real device 10A. Thus, the digital twin 10B provides simulation data 20B that corresponds one-to-one to the operating data 20A of the device 10A.". The real device is being interpreted as the device or system found in a food or beverage industry environment.) starting a digital twin simulation of the real world environment to provide digital twin simulation output relating to simulated operations and/or status of simulated objects involved in the digital twin simulation (Albrecht 41, “Subsequently, simulation of these malfunctions 30 is performed while using the digital twin 10B, and a plurality of simulation data 40 may be generated for each of the malfunctions 30.”; Albrecht 49 “The digital twin 10B of the device 10A may then be used to simulate any processes performed by the (real) device 10A. For example, the digital twin 10B may be used to simulate the timing of the translational and rotational motion of the device 10A, as well as the control signals used for this purpose and the output signals generated by the device 10A in the process. The digital twin 10B may thus generate simulation data 20B corresponding to the operating data 20A of the real device 10A.”) Stubbs and Albrecht are analogous art because they both relate to obtaining data for predictive maintenance and malfunction detection using sensor inputs. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Stubbs and Albrecht before him or her, to modify the predictive maintenance and malfunction detection workflow of Stubbs to include the sensor reduction framework of Albrecht, such that operating data used for fault detection is obtained from operational sensors already present in the device rather than requiring the attachment of multiple additional sensors for better diagnostic performance. The motivation for doing so would have been that Stubbs already teaches using sensor obtained operating data for fault detection and diagnosis of malfunctions in a predictive maintenance context, while Albrecht paragraph 44 teaches that “no (or only a few) additional sensors need to be attached to the device 10 A, which (as described above) are used in the prior art to obtain additional operating data of the device to enable more precise detection of a malfunction.” A person having ordinary skill in the art would therefore have been motivated to combine these teachings because incorporating the operation sensors data gathering approach of Albrecht into the automatic machine logic-based anomaly correction system of Stubbs would reduce system complexity, cost, and hardware modification requirements while maintaining or improving the ability to detect malfunctions. Regarding Claim 2, Stubbs teaches, the CIM of claim 1 further comprising: subsequent to the reconfiguration of the plurality of sensors, receiving sensor output from the plurality of sensors (Stubbs 70, "If an anomaly is detected, the device may power on more sensors and may provide data from sensors to inference engine to classify the anomaly."; Stubbs 73-74, "For example, at 410 the device may consume three (3) micro amperes or less when it is in 'deep sleep' state also referred to as sleep mode above. In this state, all components of the system may be powered off except one sensor. When the device processes a sensor hint at 420, the power consumption can be 45 micro amperes or less. If the sensor hint is above a threshold, the system can gather further data from one or more sensors and provide that to the machine learning inference engine to detect anomalous behavior of the monitored asset at 430." Powering on more sensors from a state where only one sensor is active shows reconfiguration of the plurality of sensors, and sending and collecting data from those sensors shows receiving sensor output after that reconfiguration.). detecting occurrence of the potential issue in the real world instantiation of the environment (Stubbs 34, "The system can identify anomalies and detect real-time machine failures using machine learning inference engines deployed in devices and edge devices."; Stubbs 74, "...provide that to the machine learning inference engine to detect anomalous behavior of the monitored asset at 430."). correcting the potential issue in the real world instantiation of the environment (Stubbs 36, "Through the use of anomaly detection at the edge, causal factors may be removed, and timely preventative actions may be conducted to prevent and/or reduce the chance of asset failure."; Stubbs 12, "...if the one or more cloud-based processors classifies the anomaly, determine alerts, corrective actions, and/or maintenance requests associated with the anomaly." The removal of causal factors and performance of preventative or corrective actions is interpreted as correcting the potential issue in the real world instantiation of the environment. ”; Stubbs 73-75 “For example, at 410 the device may consume three (3) micro amperes or less when it is in 'deep sleep' state... In this state, all components of the system may be powered off except one sensor… when the device processes a sensor hint at 420... If the sensor hint is above a threshold, the system can gather further data from one or more sensors and provide that to the machine learning inference engine to detect anomalous behavior”; Stubbs 73-75 ... Sensors on the devices can generate interrupts based on thresholds such as amount of vibration on the accelerometer, rate of change of temperature... If so, the system powers up the machine learning inference engine to classify the trends in data.".) Regarding Claim 3, Stubbs teaches the CIM of claim 1 wherein the environment is a manufacturing facility that manufactures physical products (Stubbs 41-42, “Systems and methods in accordance with some embodiments of the present invention may monitor and predict anomalous behavior of a wide variety of industrial assets. Predictive analytics may be used to monitor assets in various industries, including but not limited to: manufacturing, pulp and paper, oil and gas, water and wastewater treatment, metals and minerals, infrastructures … etc. Assets and processes that may be monitored include, but are not limited to motors, engines, fans, pumps, valves, compressors, conveyors … computer numeric controlled (CNC) machines, robots, automated systems, diesel engines, reciprocating gas compressors, screw gas compressors, pipelines, etc.”). Regarding Claim 4, Stubbs teaches the CIM of claim 1 wherein the plurality of sensors include at least one of the following sensor types: camera, microphone, temperature sensor, motion detector and/or carbon dioxide detector (Stubbs 54, “One or more built-in sensors may comprise an asset temperature sensor 221, one or more temperature sensors 222, one or more accelerometers 223... one or more microphones 224…” ; Stubbs 57 “The one or more external sensors may include, for example, temperature probes, ultrasonic level and distance sensors...occupancy detection sensors, smoke detection sensors, carbon dioxide (CO2) or carbon monoxide (CO) detection sensor, and/or any other type of sensor.”). Regarding Claim 5, Albrecht teaches applying a machine learning algorithm to determine the initial sensor configuration (Albrecht 46, “...the type of additional sensor (e.g., a vibration, voltage, or torque sensor) and the location of the sensor on the device 10A may be determined by using the digital twin 10B in a manner that optimally improves the detection of a malfunction by a trained machine learning algorithm 60.”) wherein the determination of the improved sensor configuration includes applying the machine logic algorithm to determine the improved sensor configuration (Albrecht 46, “Thereafter, it may be compared whether the trained machine learning algorithm 60-2 that was additionally trained on the basis of the simulation data of the one or more additional sensors is better (e.g., earlier or more likely) at predicting a malfunction than the trained machine learning algorithm 60-1 that was trained based only on the simulation data of the operational sensors. This process may be repeated with one or more other additional sensors on the digital twin 10B, and a plurality of trained machine learning algorithms 60-1 . . . 60-L may be generated using the respective simulation data. Finally, the influence of different additional sensors on the precision of the trained machine learning algorithms 60-1 to 60-L for detecting a malfunction may thus be compared. Then the additional sensor (or sensors) may be selected, based on whose simulation data a trained machine learning algorithm with the best precision was generated. This sensor, determined using the digital twin 10B, may then be attached to the real device 10A and used for predictive maintenance of the real device.”) Regarding Claim 6, Stubbs teaches the CIM of claim 1 wherein the initial sensor configuration and the improved sensor configuration include at least one of the following types of sensor configuration attribute(s): identity of active and inactive sensors, sensor location, sensor positioning, sensor sampling rate and/or identity of sensor device models (Stubbs 73, "For example, at 410 the device may consume three (3) micro amperes or less when it is in 'deep sleep' state... In this state, all components of the system may be powered off except one sensor." The powering off of all sensors except one is interpreted as an initial configuration defining active and inactive sensors. Stubbs 70, "If an anomaly is detected, the device may power on more sensors and may provide data from sensors to inference engine to classify the anomaly." The powering on of additional sensors is interpreted as an improved configuration defining active and inactive sensors. Stubbs 93, "These additional settings include but are not limited to sensor sampling rate, sensor data pre-processing, and device wake pattern." Sensor sampling rate is expressly disclosed as a configurable sensor attribute.) . 07-21-aia AIA Claim s 7-18 are rejected under 35 U.S.C. § 103 as being unpatentable over Stubbs, et al., U.S. Patent Application Publication No. 2021/0133607 A1 (hereinafter "Stubbs") in view of ALBRECHT, et al., U.S. Patent Application Publication No. 2022/0269259 A1 (hereinafter "Albrecht"), and further in view of Domingos et al., U.S. Patent Application Publication No. 2022/0042472 A1 (hereinafter “Domingos”) . Regarding Claim 7, the claim recites substantially similar limitations to claim 1, and is rejected. Specifically, the processing steps of instantiating a real-world entity, creating a corresponding digital twin, executing a simulation, applying predictive analytics to predict an issue, and automatically reconfiguring a plurality of sensors via machine logic are fully mapped to the combination of Albrecht and Stubbs as set forth in the rejection of claim 1 above. The additional features introduced in claim 7 add specific limitations that deviate from the generalized "environment-type" framework of claim 1 by narrowing the target of the digital twin tracking system to a physical real world object. Specifically, the new limitation of a physical object and an object type digital twin of the real world instantiation of the object are not explicitly taught by Stubbs or Albrecht. However, Domingos teaches a physical object (Domingos 6, “calculate a cumulative damage of a component of the propulsion subsystem based on the parameter, and to determine an end of life date of the component based on the cumulative damage.”; Domingos 19 “Various embodiments described herein relate to detecting degradation of a propulsion subsystem. The degradation may be detected by a monitoring system that may analyze a propulsion subsystem of a vehicle system … The propulsion subsystem may include components such as one or more engines, motors, alternators, generators, brakes, batteries, turbines, turbochargers, oil filters (e.g., centrifuge filters), and/or the like. Propulsion systems operate to propel the vehicle system. The vehicle system can include one or more vehicles. Suitable vehicles may include rail vehicles, automobiles, marine vessels, aircraft, mining vehicles, agricultural or industrial vehicles, or other off-highway vehicles (e.g., vehicles that are not designed for travel on public roadways and/or that are not legally permitted for travel on public roadways), and the like.”) an object type digital twin of the real world instantiation of the object (Domingos 21, “the monitoring system can generate a digital model or digital twin of the propulsion subsystem component based at least in part on the parameters and/or prior information.”; Domingos 22 “digital twin can be an electronic representation of the current state of the component that is based on previous duty cycles and/or conditions in which the component operated”) Albrecht, Stubbs, and Domingos are analogous art because they are from the same problem-solving area of automated predictive maintenance, digital twin modeling, and adaptive sensor data gathering. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Albrecht and Stubbs before him or her, to modify the environment-type tracking framework of Albrecht and Stubbs to include the physical object and object-type digital twin of Domingos. This modification incorporates Domingos's tracking of historical duty cycles and cumulative material wear for an individual physical object into the primary combination. This ensures that the automated sensor reconfiguration logic of Albrecht and Stubbs framework optimizes sensor attributes (such as sampling rates and active states) based on the actual physical degradation state of a specific object, rather than relying strictly on real-time operational anomalies or temporary threshold spikes. The motivation for doing so would have been to directly cut inspection costs and optimize sensor resource allocation by preventing the system from prematurely or incorrectly reacting to baseline operational variations in healthy components. Domingos explicitly provides this motivation in paragraph 4, noting that conventional tracking models can incorrectly predict a shortened life cycle of components and increase the costs of inspecting parts that are not actually at their end of life. To solve this, Domingos teaches in paragraphs 6, 22, and 32 that using an object-type digital twin as an electronic representation of the current state of a component allows the system to track cumulative damage, accurately determine an end-of-life date, and drastically reduce unplanned maintenance and lost revenue. A person of ordinary skill in the art would be motivated to integrate this cycle-based wear tracking into the adaptive sensor routines of Albrecht and Stubbs to achieve these exact cost-saving and operational efficiencies. Regarding Claim 8, the claim recites substantially similar limitations to claims 1 and 2, but narrows the target of the post-reconfiguration steps to the physical object. As demonstrated in the rejection of claim 2 above, Stubbs explicitly teaches the functional steps of receiving sensor output subsequent to reconfiguration, detecting the occurrence of the potential issue, and correcting the potential issue within a real-world instantiation. In the present combination, these exact processing steps from Stubbs are directed to the digital twin system of the physical object introduced by Domingos rather than a generalized environment. Specifically, the subsequent sensor outputs are received from the sensors monitoring the physical object (Domingos 21, 22), the issue is detected within that specific vehicle component (Domingos 19), and the corrective or preventative actions are executed to prevent unexpected component failure (Domingos 32). Accordingly, the combination of Albrecht, Stubbs, and Domingos meets the limitations of claim 8. Regarding Claim 9, Domingos teaches the CIM of claim 7 wherein the physical object is a vehicle or a portion of a vehicle (Domingos 19, “The degradation may be detected by a monitoring system that may analyze a propulsion subsystem of a vehicle system … The propulsion subsystem may include components such as one or more engines, motors, alternators, generators, brakes, batteries, turbines, turbochargers, oil filters… The vehicle system can include one or more vehicles. Suitable vehicles may include rail vehicles, automobiles, marine vessels, aircraft, mining vehicles, agricultural or industrial vehicles…”) Regarding Claims 10–12, the claims recite substantially similar limitations to claims 4–6, respectively, and are therefore rejected for the same reasons and based on the same logic as set forth in the rejection of claims 4–6 above. Regarding Claim 13, the claim recites substantially similar limitations to claim 1, respectively, and is rejected. Specifically, the processing steps of instantiating a real-world entity, creating a corresponding digital twin, executing a simulation, applying predictive analytics to predict an issue, and automatically reconfiguring a plurality of sensors via machine logic are fully mapped to the combination of Albrecht and Stubbs as set forth in the rejection of claim 1 above. The additional features introduced in claim 13 add specific limitations that deviate from the generalized "environment-type" framework of claim 1 by narrowing the target of the digital twin tracking system to a process. Specifically, the new limitation of a process and an object-type digital twin of the real-world instantiation of the process are not explicitly taught by Stubbs or Albrecht. However, Domingos teaches a process in the form of a vehicle trip or routing mission (Domingos 21, “In another embodiment, the monitoring system may generate a trip plan that maps a route from the vehicle's current location to an appropriate service and maintenance shop (and notify interested parties of the need for service along with particular details of the requested service and scheduling information).”). Domingos further teaches an object type digital twin tied directly to this operational trip process (Domingos 21, “In one embodiment, the monitoring system can generate a digital model or digital twin of the propulsion subsystem component based at least in part on the parameters and/or prior information.”; Domingos 22, “digital twin can be an electronic representation of the current state of the component that is based on previous duty cycles and/or conditions in which the component operated”). Albrecht, Stubbs, and Domingos are analogous art because they are from the same problem-solving area of automated predictive maintenance, digital twin modeling, and adaptive sensor data gathering. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Albrecht and Stubbs before him or her, to modify the environment-type tracking framework of Albrecht and Stubbs to include the trip process and object-type digital twin of Domingos. This modification incorporates Domingos's tracking of historical duty cycles and routing parameters for an individual operational trip process into the primary combination. This ensures that the automated sensor reconfiguration logic of Albrecht and Stubbs framework optimizes sensor attributes (such as sampling rates and active states) based on the actual operational parameters of a specific vehicle trip process, rather than relying strictly on real-time operational anomalies or temporary threshold spikes. The motivation for doing so would have been to directly cut inspection costs and optimize sensor resource allocation by preventing the system from prematurely or incorrectly reacting to baseline operational variations in healthy components. Domingos explicitly provides this motivation in paragraph 4, noting that conventional tracking models can incorrectly predict a shortened life cycle of components and increase the costs of inspecting parts that are not actually at their end of life. To solve this, Domingos teaches in paragraphs 6, 22, and 32 that using an object-type digital twin as an electronic representation of the current state of a component allows the system to track cumulative damage incurred during the trip process, accurately determine an end-of-life date, and drastically reduce unplanned maintenance and lost revenue. A person of ordinary skill in the art would be motivated to integrate this cycle-based wear tracking into the adaptive sensor routines of Albrecht and Stubbs to achieve these exact cost-saving and operational efficiencies. Regarding Claim 14, the claim recites substantially similar limitations to claims 1 and 2, but narrows the target of the post-reconfiguration steps to the process. As demonstrated in the rejection of claim 2 above, Stubbs explicitly teaches the functional steps of receiving sensor output subsequent to reconfiguration, detecting the occurrence of the potential issue, and correcting the potential issue within a real-world instantiation. In the present combination, these exact processing steps from Stubbs are directed to the digital twin system of the operational trip process introduced by Domingos rather than a generalized environment. Specifically, the subsequent sensor outputs are received from the sensors monitoring the process (Domingos 21, 22), the issue is detected within that specific vehicle trip routing or component operation (Domingos 19, 21), and the corrective or preventative actions are executed to prevent unexpected failure (Domingos 32). Accordingly, the combination of Albrecht, Stubbs, and Domingos meets the limitations of claim 14. Regarding Claims 15 and 16–18, these claims recite substantially similar limitations to claims 9 and 4–6, respectively, and are therefore rejected for the same reasons and based on the same logic as set forth in the rejections of those claims above. Conclusion 07-96 The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure. Park et al. US 20220365498 A1 teaches a building management system that creates virtual "agents" paired with physical entities, such as equipment or building spaces. The system uses communication channels to share data among agents, perform automated operations, and ingest time-series data to operate physical systems or generate configuration updates. Additionally, the reference features monitoring systems that analyze data to identify operational abnormalities. This reference is pertinent to the applicant's disclosure as it relates to tracking and controlling real-world objects and capturing tracking anomalies through a network of virtual models. Sha et al. US 11,463,322 B1 teaches a system for constructing and managing a virtual representation, such as a digital twin model, of an internet-connected device computing environment. The reference utilizes the digital twin architecture to manage the environment and to understand, predict, and optimize the performance of applications and systems operating within it (such as an IoT-based computing environment). This reference is pertinent to the applicant's disclosure as it relates to creating virtual digital twin models of real-world device environments to predict performance issues and optimize operations Any inquiry concerning this communication or earlier communications from the examiner should be directed to AREEBAH FATIMA whose telephone number is (571)270-0294. The examiner can normally be reached 9am - 5pm. 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, Rehana Perveen can be reached at (571) 272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AREEBAH FATIMA/Examiner, Art Unit 2189 /REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189 Application/Control Number: 18/154,137 Page 2 Art Unit: 2189 Application/Control Number: 18/154,137 Page 3 Art Unit: 2189 Application/Control Number: 18/154,137 Page 4 Art Unit: 2189 Application/Control Number: 18/154,137 Page 5 Art Unit: 2189 Application/Control Number: 18/154,137 Page 6 Art Unit: 2189 Application/Control Number: 18/154,137 Page 7 Art Unit: 2189 Application/Control Number: 18/154,137 Page 8 Art Unit: 2189 Application/Control Number: 18/154,137 Page 9 Art Unit: 2189 Application/Control Number: 18/154,137 Page 10 Art Unit: 2189 Application/Control Number: 18/154,137 Page 11 Art Unit: 2189 Application/Control Number: 18/154,137 Page 12 Art Unit: 2189 Application/Control Number: 18/154,137 Page 13 Art Unit: 2189 Application/Control Number: 18/154,137 Page 14 Art Unit: 2189 Application/Control Number: 18/154,137 Page 15 Art Unit: 2189 Application/Control Number: 18/154,137 Page 16 Art Unit: 2189 Application/Control Number: 18/154,137 Page 17 Art Unit: 2189 Application/Control Number: 18/154,137 Page 18 Art Unit: 2189 Application/Control Number: 18/154,137 Page 19 Art Unit: 2189 Application/Control Number: 18/154,137 Page 20 Art Unit: 2189
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