DETAILED ACTION
This action is in response to the submission filed on 2/2/2026. Claims 1-20are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments- Drawings
Applicant’s amendments have been fully considered and are persuasive. The objections are withdrawn.
Response to Arguments - 35 USC § 102
Applicant's arguments filed 2/2/2026 have been fully considered but they are not persuasive.
Argument 1:
Applicant argues on pages 11-12 that “Cella does not describe "utilizing a virtual agent to represent a physical robot in the virtual representation[,]" as recited in claim 1” and that “The present application requires an active, learning virtual agent that represents a physical robot within a simulated data center for purposes of diagnosing and remediating simulated component failures and acquiring remediation behavior for later execution by the physical robot. Cella's enterprise digital-twin models of robots as workflow assets are structurally and functionally distinct and therefore cannot teach or suggest the claimed limitation”. This argument is traversed as follows.
It is unclear what Applicant is attempting to argue here. Wikipedia defines a “digital twin” as
A digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as a digital counterpart of it for purposes such as simulation, integration, testing, monitoring, and maintenance.
…
A digital twin is "a set of adaptive models that emulate the behaviour of a physical system in a virtual system getting real time data to update itself along its life cycle. The digital twin replicates the physical system to predict failures and opportunities for changing, to prescribe real time actions for optimizing and/or mitigating unexpected events observing and evaluating the operating profile of the system."
This is exactly what Applicant states is required by the claim language.
Cella recites in para [0270], “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as… robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”). Paragraph [0193], “FIG. 52 is a diagrammatic view that depicts embodiments of a set of value chain network digital twins representing virtual models of a set of value chain network entities”. Paragraph [0019] recites “the task definition system generates a data structure for each task in the set of tasks that includes a reference to a digital twin for at least one of the task and at least one robot operating unit for performing the tasks for use by the workflow simulation system”.
Clearly, Cella discloses utilizing a virtual agent to represent a physical robot in the virtual representation.
Argument 2:
Applicant argues on pages 12-14 that “Cella does not describe "simulating a component failure within the virtual representation of the data center[,]" as recited in claim 1”. This argument is traversed as follows.
Again, it is unclear what Applicant is attempting to argue here. Applicant refers to specific types of failure simulation and how the failures are used on pages 13-14 which is not present in the claim language and has no relevance to the claim language. Cella recites in paragraph [2314] “ the monitoring and notification service 12163 may leverage machine-learned models that are trained to diagnose certain conditions of robot (e.g., failing
components, loose components, and/or the like)”. Para [0277] recites “Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others”. Paragraph [1071] recites “the digital twin system I/O system 8104 may periodically query and/or receive data from a connected data source 8020, such as a sensor system 8022 having sensors that sensor data from facilities (e.g., … data center facilities, and many others) …” Clearly component failure within the virtual representation of the data center is simulated.
Argument 3:
Applicant argues on pages 14-16 that “Cella does not describe "analyzing, using the virtual agent, data associated with the component failure in order to identify the component failure[,]" as recited in claim 1”. This argument is traversed as follows.
Cella recites in paragraph [0383], “one of the processes automated by robotic process automation involves prediction of maintenance requirements in supply chain infrastructure.” Para [2418] recites, “scheduling and routing of robotic systems with additive manufacturing capabilities may be influenced by prediction capabilities of an AI-based robot health monitoring system, so that service or maintenance visit value can be optimized by ensuring that additive manufacturing resources are either routed to the service area for localized part manufacturing or are utilized to produce components (e.g., hydraulic assemblies with fewer interconnections) so that they are available locally when a service can include deployment of improved reliability robotic elements.” If component failure has been detected, the robotic process automation analysis to provide manufacturing resources.
Argument 4:
Applicant argues on pages 16-17 that “Cella does not describe "determining, using the virtual agent, one or more solutions to remediate the component failure[,]" as recited in claim 1”. This argument is traversed as follows.
Para [2186] recites “ embodiments, the maintenance management system 12026 may monitor the state of the fleet resources, such as robot operating units via resource state reports that may be provided on a scheduled basis or in response to an inquiry for robot operating unit state by the maintenance management system 12026 and the like. In embodiments, the maintenance management system 12026 may monitor robot operating unit communication for an indication of a potential service condition, such as a robot operating unit signaling to a supervisor robot that it is experiencing reduced power output, a robot operating unit reporting exposure to certain ambient conditions (e.g., excessive heat), a lack of heartbeat signal from a robot operating unit to a robot health monitor resource, and the like.... Yet further a maintenance management system 12026 may include maintenance robots that may be deployed with other robots in a team of robot operating units for performing a requested job. A maintenance robot may be a configuration of a multi-purpose robot deployed with a robot team. Such a configuration may be temporal within bounds of a team deployment. A multi-purpose robot deployed for performing tasks of a job workflow may be reconfigured dynamically (and optionally temporarily) while deployed to a team to perform maintenance actions on other robots and fleet resources”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20220197306 A1 (“Cella”).
Regarding claim 1, Cella teaches:
A computer-implemented method, comprising:
generating a virtual representation of a data center (Cella: para [0101], “the user interface includes a virtual reality (VR) interface configured to enable a user to build 3D models in VR”; para [0275], “Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: … software, information technology resources,
data processing resources, data storage resources, power generation and/or storage resources, computational resources and other assets”; para [1071], “the digital twin system I/O system 8104 may periodically query and/or receive data from a connected data source 8020, such as a sensor system 8022 having sensors that sensor data from facilities (e.g., … data center facilities, and many others) …”);
utilizing a virtual agent to represent a physical robot in the virtual representation (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”; para [0270], “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as… robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”)”);
simulating a component failure within the virtual representation of the data center (Cella: para [0465], “the machine learning model 3000 may automatically predict hypothetical situations for simulation with the digital replica, such as by…predicting when one or more components of the one or more value chain entities 652 may fail”; paragraph [2314] “ the monitoring and notification service 12163 may leverage machine-learned models that are trained to diagnose certain conditions of a robot (e.g., failing components, loose components, and/or the like)”; para [1071], “the digital twin system I/O system 8104 may periodically query and/or receive data from a connected data source 8020, such as a sensor system 8022 having sensors that sensor data from facilities (e.g., … data center facilities, and many others) …”);
analyzing, using the virtual agent, data associated with the component failure in order to identify the component failure (Cella: para [2316], “…perform failure forecasting and predictive maintenance. Additionally or alternatively, the machine learning services may include a clustering algorithm to identify the failure pattern hidden in the failure data to train a model for detecting uncharacteristic or anomalous behavior”; para [2418], “scheduling and routing of robotic systems with additive manufacturing capabilities may be influenced by prediction capabilities of an AI-based robot health monitoring system, so that service or maintenance visit value can be optimized by ensuring that additive manufacturing resources are either routed to the service area for localized part manufacturing or are utilized to produce components (e.g., hydraulic assemblies with fewer interconnections) so that they are available locally when a service can include deployment of improved reliability robotic elements.”);
determining, using the virtual agent, one or more solutions to remediate the component failure (Cella: para [0982], “Referring to FIG. 26, a set of opportunity miners 1460 may be provided as part of the adaptive intelligence layer 614, which may be configured to seek and recommend opportunities to improve one or more of the elements of the platform 604, such as via addition of artificial intelligence system 1160, automation (including robotic process automation 1402), or the like to one or more of the maritime facilities 622 and for each of floating assets 620 including their systems, sub-systems, components, applications with which the platform 100 interacts. In embodiments, the opportunity miners 1460 may be configured or used by developers of AI or RPA solutions to find opportunities for better solutions and to optimize existing solutions in a value chain network 668”; para [2419], “repair of the object may be achieved by use of visual and other sensors of the robotic system determining that a handle of the object to be repaired is broken, thereby preventing performance of the repair as instructed. Based on the determination of this unexpected condition, a supplemental set of robot operations may be generated for the current repair assignment to instruct the robotic control system for 3D printing to fashion a replacement handle or perform a repair of the handle (e.g., mend a break in a structural portion of the handle). These supplemental operations may be determined based on an assessment of an object to be repaired and integrated in the current instance of the object repair process even when the cause of failure that requires the repair task is other than the handle”); and
causing the virtual agent to learn to perform at least one selected solution, of the one or more solutions, to be used by the physical robot in response to an occurrence of component failure in the data center (Cella: para [0367], “The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people... In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions”; Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”; para [0270], “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as… robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”)”)).
Regarding claim 2, Cella teaches:
The computer-implemented method of claim 1, further comprising:
using reinforcement learning to train the virtual agent to identify, and determine how to remediate, the component failure (Cella: [0484], “the machine learning model 3000 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming techniques such that the machine learning model 3000 may train by taking actions in an environment in order to maximize a cumulative reward”; para [0276], “an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation … product failure incidents, system failure incidents”).
Regarding claim 3, Cella teaches:
The computer-implemented method of claim 2, further comprising:
using, as part of the reinforcement learning, a reward function to further train the virtual agent to maintain an integrity of the data center (Cella: [0484], “the machine learning model 3000 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming techniques such that the machine learning model 3000 may train by taking actions in an environment in order to maximize a cumulative reward”; para [0367], “The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people... In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions”;).
Regarding claim 4, Cella teaches:
The computer-implemented method of claim 3, wherein maintaining the integrity of the data center includes performing predictive maintenance (Cella: para [0465], “the machine learning model 3000 may automatically predict hypothetical situations for simulation with the digital replica, such as by…predicting when one or more components of the one or more value chain entities 652 may fail”; para [2316], “…perform failure forecasting and predictive maintenance. Additionally or alternatively, the machine learning services may include a clustering algorithm to identify the failure pattern hidden in the failure data to train a model for detecting uncharacteristic or anomalous behavior”), preventative maintenance, or testing of one or more physical components in the data center.
Regarding claim 5, Cella teaches:
The computer-implemented method of claim 1, wherein generating the virtual representation includes simulating physical, spatial, communication, and configuration aspects of the data center, and wherein additional virtual representations are able to be generated to represent additional data centers (Cella: para [0101], “the user interface includes a virtual reality (VR) interface configured to enable a user to build 3D models in VR”; para [0275], “Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: … software, information technology resources,
data processing resources, data storage resources, power generation and/or storage resources, computational resources and other assets”).
Regarding claim 6, Cella teaches:
The computer-implemented method of claim 1, further comprising:
utilizing at least one second virtual agent to represent a second physical robot in the virtual representation (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”; and
utilizing the at least one second virtual agent to assist in identifying or remediating the component failure (Cella: Abstract, “A robot fleet management platform includes a job parsing system that applies filters to identify portions of a job request suitable for robot automation. Based on the identified portions and a first fleet objective of the job request, a task system establishes tasks that define a robot type and task objective”; para [0276], “an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation … product failure incidents, system failure incidents”).
Regarding claim 7, Cella teaches:
The computer-implemented method of claim 1, further comprising:
providing learnings of the virtual agent to the physical robot for operation in the data center; and enabling the physical robot to update the learnings based, at least in part, upon additional data obtained by the physical robot during the operation in the data center (Cella: para [0367], “The robotic process automation 1442 may be trained (e.g., through machine
learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people …In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions. For example, the robot may be trained to rebuild a set of bearings by having the robot watch a video of someone doing this task. This may include tracking physical interactions and tracking interactions at a software level”).
Regarding claim 8, Cella teaches:
The computer-implemented method of claim 1, further comprising:
using a natural language system to generate human-understandable text relating to the component failure and the at least one selected solution to remediate the component failure (Cella: para [0726], “Clustering processes 5342 may be implemented to identify the failure pattern hidden in the failure data to train a model for detecting uncharacteristic or anomalous behavior…Analytics processes 5344 perform data analytics on various data to identify insights and predict outcomes. Natural language processes 4348 coordinate with machine twin 1770 to communicate the outcomes and results to the user of machine twin 1770”; para [1058], “ the artificial intelligence services system 8010 includes a natural language processing system that receives text/speech and determines a context of the text and/or generates text in response to a request to generate text”).
Regarding claim 9, Cella teaches:
The computer-implemented method of claim 1, wherein the component failure relates to at least one of a network health, a component health, an enumeration, a network state, or a network capacity (Cella: para [0276], “events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and/or safety incidents, traffic congestion and/or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, as well as combinations among them), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others)”).
Regarding claim 10, Cella teaches:
The computer-implemented method of claim 1, wherein the one or more solutions to remediate the component failure include at least one of fixing, removing, replacing, or taking offline one or more physical components in the data center (Cella: para [1867], “artificial intelligence system (e.g., a robotic process automation system trained on a training set of expert service visit data), to determine a recommended action, which in embodiments may involve replacement of a part and/or repair of a part, or some other activity”).
Regarding claims 11 and 16, Cella teaches:
A system, comprising: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the system (Cella: para [2772]) to:
simulate operation of a physical computing environment (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”; para [0101], “the user interface includes a virtual reality (VR) interface configured to enable a user to build 3D models in VR”; para [0275], “Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: … software, information technology resources, data processing resources, data storage resources, power generation and/or storage resources, computational resources and other assets”);
utilize a virtual agent to simulate a physical robot in the computing environment (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”; para [0270], “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as… robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”)”);
analyze, using the virtual agent, data associated with the simulated operation to identify a simulated failure in the computing environment (Cella: para [0465], “the machine learning model 3000 may automatically predict hypothetical situations for simulation with the digital replica, such as by…predicting when one or more components of the one or more value chain entities 652 may fail”);
determine, using the virtual agent, one or more solutions to remediate the simulated failure (Cella: para [0982], “Referring to FIG. 26, a set of opportunity miners 1460 may be provided as part of the adaptive intelligence layer 614, which may be configured to seek and recommend opportunities to improve one or more of the elements of the platform 604, such as via addition of artificial intelligence system 1160, automation (including robotic process automation 1402), or the like to one or more of the maritime facilities 622 and for each of floating assets 620 including their systems, sub-systems, components, applications with which the platform 100 interacts. In embodiments, the opportunity miners 1460 may be configured or used by developers of AI or RPA solutions to find opportunities for better solutions and to optimize existing solutions in a value chain network 668”; para [2419], “repair of the object may be achieved by use of visual and other sensors of the robotic system determining that a handle of the object to be repaired is broken, thereby preventing performance of the repair as instructed. Based on the determination of this unexpected condition, a supplemental set of robot operations may be generated for the current repair assignment to instruct the robotic control system for 3D printing to fashion a replacement handle or perform a repair of the handle (e.g., mend a break in a structural portion of the handle). These supplemental operations may be determined based on an assessment of an object to be repaired and integrated in the current instance of the object repair process even when the cause of failure that requires the repair task is other than the handle”);
and causing the virtual agent to learn to perform at least one selected solution, of the one or more solutions, to be used by the physical robot in response to a physical occurrence of the simulated failure in the computing environment (Cella: para [0367], “The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people... In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions”; para [0275], “Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: … software, information technology resources, data processing resources, data storage resources, power generation and/or storage resources, computational resources and other assets”; para [0270], “These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as… robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”)”).
Regarding claim 12 and 17, Cella teaches:
The system of claim 11, wherein the physical entity is a human or an at least partially automated manipulable component (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”).
Regarding claim 13 and 18, Cella teaches:
The system of claim 11, wherein the instructions when executed further cause the system to:
use reinforcement learning to train the virtual agent to identify, and determine how to remediate, the simulated failure, wherein a reward function is to be used to further train the virtual agent to maintain an integrity of the physical computing environment (Cella: [0484], “the machine learning model 3000 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming techniques such that the machine learning model 3000 may train by taking actions in an environment in order to maximize a cumulative reward”; para [0367], “The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people... In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions”;).
Regarding claim 14 and 19, Cella teaches:
The system of claim 11, wherein the instructions when executed further cause the system to:
utilize at least one second virtual agent to represent a second physical robot in the virtual representation (Cella: Abstract, “A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks”); and
utilize the at least one second virtual agent to assist in identifying or remediating the simulated failure (Cella: Abstract, “A robot fleet management platform includes a job parsing system that applies filters to identify portions of a job request suitable for robot automation. Based on the identified portions and a first fleet objective of the job request, a task system establishes tasks that define a robot type and task objective”; para [0276], “an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation … product failure incidents, system failure incidents”).
Regarding claim 15 and 20, Cella teaches:
The system of claim 11, wherein the instructions when executed further cause the system to:
provide learnings of the virtual agent to the physical robot for operation in the data center; and enable the physical robot to update the learnings based, at least in part, upon additional data obtained by the physical robot during the operation in a data center (Cella: para [0367], “The robotic process automation 1442 may be trained (e.g., through machine
learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., trained agent or trained robotic process automation system) execute these tasks that were previously performed by people …In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train a robotic system to sequence or undertake the same physical interactions. For example, the robot may be trained to rebuild a set of bearings by having the robot watch a video of someone doing this task. This may include tracking physical interactions and tracking interactions at a software level”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NITHYA J. MOLL whose telephone number is (571)270-1003. The examiner can normally be reached Monday-Friday 10am-6pm EST.
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/NITHYA J. MOLL/ Primary Examiner, Art Unit 2189