DETAILED ACTION
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
2. The Amendment filed on January 20, 2026, has been entered. The examiner acknowledges the amendments to claims 1, 3, 8, 10, 15, and 17.
Rejections under 35 U.S.C. § 101: Applicant argues that the amended independent claims do not recite a judicial exception. Examiner disagrees, noting that the actions of receiving when associated with an execution of a sequence of actions, determining instances of interruptions, and instances of potential failures, association of metadata with instances of interruptions, troubleshooting, determining the nature of an error, the patterns indicative of potential failures and responding to avoid potential failures all employ judicial exceptions. That being said, the Examiner concludes that the amended claims do indicate a practical application, the claims describing machine learning taught to detect predictive indications of failure in the system, automatic corrective action to mitigate the failure, and learning from the incident for future application. The system of control is improved by self-analytics, taking corrective action, archiving actions taken, and referencing prior actions for future knowledge in continued troubleshooting.
Based on the improvements to the technology, the rejections under 35 U.S.C. § 101 will be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant argues Geffen includes no disclosure related to determining using ML patterns indicative of potential failures. Examiner agrees, citing Cella as the source of ML predictions of potential failures.
Applicant also argues recognition of patterns indicating potential execution failures. These are also taught in prior art.
Finally, Applicant argues updating the parameters for the bot in the server to avoid potential failures. Prior art also detects and provides guidance to avoid task execution risk factors, diminishing certain inputs to avoid exploding error problems, and adjusting operations and IT to improve results and/or avoid anticipated problems.
Given the above, the arguments for withdrawing rejections under 35 U.S.C. § 103 are not compelling and unless a novel innovation can be revealed, the rejections cannot be withdrawn.
Claim Rejections – 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed
to non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of managing workflow processes in robotic process automation.
Claim 1 discloses a method, comprising: A method for intelligent workflow management the method comprising:
receiving, information associated with an execution of a
sequence of actions; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion),
determining instances of interruptions in the execution of the sequence of actions,
wherein the instances of interruptions comprise at least potential failures in at least one action yet to be executed; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
extracting, metadata associated with the execution of the sequence of actions, wherein the metadata comprises information associated with the instances of interruptions; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion),
simulating, a replication of the sequence of actions, wherein the replication of the sequence of actions comprises a replication of the instances of interruption; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion),
troubleshooting, instances of interruptions in the execution of the sequence of actions based on at least simulating the replication of the sequence of actions; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
determining, instances of interruptions in the execution of the sequence of actions wherein the instances of interruptions comprises at least an action did not execute as intended; an action is taking longer than expected to complete, and an action providing unexpected outputs; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
extracting, information associated with the instances of interruptions, wherein the information associated with the instances of interruptions comprises metadata indicating an action where each instance of interruption occurred, a time of occurrence associated with each instance of interruption, recording of each instance of interruption, and a nature of the error; (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
determining, patterns indicative of potential failures in the execution of the sequence of actions based on the information associated with the instances of interruptions; (following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
in response, modifying, algorithmic execution parameters to avoid any potential failures, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk),
Additional limitations employ the method for storing the information associated with the instances of interruptions, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion - claim 9), accessing the information associated with the instances of interruptions and outputting a predictive alert comprising the identified patterns indicative of potential failures associated with at least one action yet to be executed, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion – claim 10), temporarily pausing the RPA bot; implementing pre-defined actions to remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; and resuming the RPA bot upon implementing the pre-defined actions, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk– claim 11), transmitting control signals to display the predictive alert for manual intervention, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk– claim 12), receiving the sequence of actions that the RPA bot is configured to execute; receiving information associated with the potential failure associated with the at least one action yet to be executed by the RPA bot; identifying a specific location within the sequence of actions where the at least one action associated with the potential failure is situated; dividing the sequence of actions into at least two distinct components wherein a first component comprises a sequence of actions up to and including the at least one action associated with the potential failure, and a second component comprises a sequence of actions subsequent to the at least one action; and outputting the first component and the second component for further to isolate and remedy the potential failure in the at least one action, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk– claim-13), determining, remedial actions configured to remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; simulating, an execution of the remedial actions; determining whether the execution of the remedial actions remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; generating a update based on the remedial actions in an instance where the execution of the remedial actions remedies the potential failures; and deploying the update, (economic principles and practices, following rules or instructions, observation, evaluation, judgement, opinion, mitigating risk– claim-14).
Each of these claimed limitations involve organizing human activity, following rules or instructions, and/or employ mental processes involving observation, evaluation, judgement, and opinion, fundamental economic principles and practices based on mitigating risk.
Claims 1-7, and 15-20 recite similar abstract ideas as those identified with respect to claims 8-14.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, the claims 1-20 recite additional limitations which are hardware or software elements such as an application server, robotic process automation (RPA) by an RPA bot on the application server, a fog computing subsystem, a workflow replication subsystem, an anomaly resolution subsystem, an interruption recordation subsystem, a video recorder, a machine learning (ML) subsystem associated with the interruption recordation subsystem, these limitations are sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements integrate the instructions of the abstract ideas in a specific technological environment, (MPEP § 2106.05 (f) & (h)).
The additional elements integrate the identified abstract ideas into a practical application. The claims describe an ordered combination that adds a predictive indication of a failure, automatic corrective action to mitigate the failure, and learning from the incident for future application. In this way, the system continues to improve.
Since the limitations in the claims 1-20 transform the exception into a patent eligible application, the claims are directed to statutory subject matter and are not rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 8-14, 1-7, and 15-20, are rejected under 35 U.S.C. § 103 as being taught by Cella,
(US 20220187847 A1), hereafter Cella, “Robot Fleet Management For Value Chain Networks,” in view of Geffen, (US 11642788 B2), hereafter Geffen, “System and Method for Detecting and Fixing Robotic Process Automation Failures.”
Regarding Claim 8, A method for intelligent workflow management in robotic process automation (RPA), Cella teaches, (the adaptive intelligence layer 614 may include a robotic process automation (RPA) system 1442, which may include a set of components, processes, services, interfaces and other elements for development and deployment of automation capabilities for various value chain entities 652, environments, and applications 630. Without limitation, robotic process automation 1442 may be applied to each of the processes that are managed, controlled, or mediated by each of the set of applications 614 of the platform application layer, [0366]), the method comprising:
receiving, from an application server, information, (“the system”, or “the platform”) may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and/or other data configurations and transmission modalities received from, for example, digital product networks 252, directly from customers (e.g., direct connected customer 250), or some other third party 220, Cella, [0260] associated with an execution of a sequence of actions by an RPA bot on the application server; Cella does not teach, Geffen teaches, (The terms ‘software’, ‘program’, ‘software procedure’ or ‘procedure’ or ‘software code’ or ‘code’ or application’ may be used interchangeably according to the context thereof, and denote one or more instructions or directives or electronic circuitry for performing a sequence of operations that generally represent an algorithm and/or other process or method, [13:38-44] and a robotic process automation unit configured to pull tasks from the task queue database for processing said tasks, [1:42-43],
Cella and Geffen are both considered to be analogous to the claimed invention because they are both in the field of robotic process automation and application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the robotic fleet management approach of Cella with the methods for detecting and fixing robotic process automation failures of Geffen to thereby fixing the robotic process automation failures [1:55-56],
determining instances of interruptions in the execution of the sequence of actions, Cella teaches, (alerts, such as indicating failure modes, congestion, delays interruptions in service, poor latency, diminished quality of service, bandwidth constraints, poor performance on key performance indicators, downtime, or other issues may be provided as augmentation or overlays of the converged information technology and operations digital twin, [1037]
wherein the instances of interruptions comprise at least potential failures in at least one action yet to be executed by the RPA bot; (the predictive maintenance service 12165 may provide an intelligence request to the intelligence layer that includes current operational data obtained from the MPR 12100 (e.g., sensor data, environmental data, and/or the like), whereby the intelligence layer 12140 (e.g., the machine-learning service) may leverage one or more machine-learning models (e.g., prediction models, classification models, neural networks, and/or the like) to identify a potential failure of a component of the MPR 12100, Cella, [2315]),
extracting, using a fog computing subsystem, metadata associated with the execution of the sequence of actions, wherein the metadata comprises information associated with the
instances of interruptions; Cella teaches, (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, [2315]), a digital twin model is based on a combination of data and the data's relationship to the digital twin environments and/or processes. As such, different digital twins may share the same data and different digital twin perspectives can be the results of a set of metadata built on top of a digital twin data model or data environment, [1089],
simulating, using a workflow replication subsystem, a replication of the sequence of actions, (a digital twin can represent a process, such as a workflow, such as with moving elements that represent steps of the process, such as the flow of items through a plant or warehouse, [1017], and in general, digital twins merge data from multiple data sources into a model and representation of the salient characteristics of things, assets, systems, devices, machines, components, equipment, facilities, individuals or other entities, [ ], including robotic systems, [ ], and testing and diagnostic processes, Cella, [1016], wherein the replication of the sequence of actions, comprises a replication of the instances of interruption; and the role-based digital twin may thus depict location-based and/or logical interconnections between operations and information technologies. In embodiments, alerts, such as indicating failure modes, congestion, delays, interruptions in service, poor latency, diminished quality of service, bandwidth constraints, poor performance on key performance indicators, downtime, or other issues may be provided as augmentations or overlays of the converged information technology and operations digital twin, [1037], and operate in coordination with an adaptive edge computing system and/or a set of adaptive edge computing systems that provide coordinated edge computation include a wide range of systems, such as [ ] predictive systems (such as motion prediction systems, output prediction systems, activity prediction systems, fault prediction systems, failure prediction systems, accident prediction systems, event predictions systems, event prediction systems, and many others), Cella, [1087],
and, troubleshooting, using an anomaly resolution subsystem, instances of interruptions in the execution of the sequence of actions based on at least simulating the replication of the sequence of actions, Cella teaches, (The simulations, analytics and/or modeling performed by the CTO digital twin 8310 may be used to evaluate product go-to-market timing and preparedness. The CTO equipped with a CTO digital twin 8310 will be better able to adapt quickly to identify product and/or technical parameters in need of further development and predict products' operational performance. This may reduce errors, speed testing and reduce the need for patches, bug fixes, updates and the like and flatten agile process management, [1176], and the CIO digital twin 8312 may present a user interface that allows a user (e.g., the CIO) to select particular network assets to review in greater detail, such as an asset the real time operations data indicates is experiencing an operational failure or other issue. Such real time operations data related to IT and other information asset performance may allow the CIO to better track the performance and needs of an organization's information and IT infrastructure and better enable him to troubleshoot issues, simulate solutions, select appropriate information and IT management actions, and maintain the organization's information and IT infrastructure, [1189]),
determining, using an interruption recordation subsystem, instances of interruptions in the execution of the sequence of actions by the RPA bot, Cella does not teach, Geffen teaches, (between each two adjacent steps of a task or process, there may be defined a transition. If a process comprises moving from step A to step B, then the transition was complete. However, in case of a failure, the process or task may not proceed from step A to step B, thus indicating a failure in the transition point between step A to step B. Failure evaluation processor 110 may be configured to run an examination on all transitions between each two adjacent steps in each of the failed tasks, Geffen, [5:57-66],
wherein the instances of interruptions comprises at least an action did not execute as intended; an action is taking longer than expected to complete, and an action providing unexpected outputs; Cella teaches, (an incident management application, [ ] 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, [0276], and
extracting, using the interruption recordation subsystem, information associated with the instances of interruptions, wherein the information associated with the instances of interruptions comprises metadata, Cella teaches, (The artificial intelligence store 3504 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems, [0463]), indicating an action where each instance of interruption occurred, a time of occurrence associated with each instance of interruption, Cella does not teach, Geffen teaches, (the failed tasks may be pulled for manual execution, e.g., by evaluators unit 108. The manual execution of the failed tasks may be recorded and store in real time database 120, [6:65-7:01]), a video recording of each instance of interruption, Cella teaches, (the warehouse digital twin kit system 5000 allows an owner or operator 5008 of the one or more warehouse entities 654 to get complete portfolio overview of all these entities [ ] including warehouse photographs 5010, 3D images 5012, live video feeds, [0672], and a nature of the error, Cella does not teach, Geffen teaches, (system 100 may further comprise an evaluators unit 108, which may be configured to evaluate the failed tasks that are stored in failed tasks queue database 106. The evaluators unit 108 may actively pull or passively receive the failed tasks from failed tasks queue database 106 in order to determine the reason and nature of their failure, [5:25-31]),
Cella and Geffen are both considered to be analogous to the claimed invention because they are both in the field of robotic process automation and application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the robotic fleet management approach of Cella with the methods for detecting and processing RPA interruptions of Geffen, to enable the system to provide selected execution steps that best fix the failed tasks, thereby fixing the robotic process automation failures, 2:26-27]),
determining, using a machine learning (ML) subsystem associated with the interruption recordation subsystem, patterns indicative of potential failures in the execution of the sequence of actions by the RPA bot based on the information associated with the instances of interruptions; Cella teaches, (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. The failure data across multiple robots and their historical records may be clustered to understand how different patterns correlate to certain wear-down behavior and develop a maintenance plan resonant with the failure, [2315]),
in response, modifying, using the ML subsystem, algorithmic execution parameters of the RPA bot in the application server to avoid any potential failures. Cella teaches, (the models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting deformations or failure in a 3D printed part. In embodiments, the models may also determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect. In embodiments, the system may send a warning or error signal to an operator or a user, or automatically abort the printing process, [0050]).
Regarding Claim 9, The method of Claim 8, further comprising:
storing, using the interruption recordation subsystem, the information associated with the instances of interruptions in a vector database, Cella does not teach, Geffen teaches, (a failed tasks queue database configured to collect and store tasks and data on tasks that the robotic process automation unit failed to complete, [1:44-46]).
Cella and Geffen are both considered to be analogous to the claimed invention because they are both in the field of robotic process automation and application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the robotic fleet management approach of Cella with the methods for detecting and processing RPA interruptions of Geffen to thereby fixing the robotic process automation failures [1:55-56].
Regarding claim 10, The method of Claim 9, further comprising:
accessing the information associated with the instances of interruptions from the vector database; Cella teaches, (the artificial intelligence system 10212 may use a clustering algorithm to identify the failure pattern hidden in the failure data to train a model for detecting uncharacteristic or anomalous behavior. The failure data across multiple parts and their historical records may be clustered to understand how different patterns correlate, [1809]), the adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adaptors 1110 for facilitating data storage, retrieval access, query management, loading, extraction, normalization, and/or transformation to enable use of the various other data storage architectures 1002, such as allowing extraction from one form of database and loading to a data system that uses a different protocol or data structure, [0290]), and
outputting a predictive alert comprising the identified patterns indicative of potential
failures associated with at least one action yet to be executed by the RPA bot, (when the diagnostic chip 9300 is configured to perform diagnostic analysis of a machine, the electromechanical analysis circuit 9326 may retrieve analytics data from analytics library 9352 specifying configuration parameters for the machine (e.g., frequencies and/or frequency patterns indicating particular states of the machine or sub-parts of the machine, electrical information indicating correct or incorrect operating levels for electrical circuits of the machine, etc.) and may retrieve an intelligence module 9358 trained to predict a potential breakdown or other condition of the machine, effects of maintenance actions, etc., [1669]), and (alerts, such as indicating failure modes, congestion, delays interruptions in service, poor latency, diminished quality of service, bandwidth constraints, poor performance on key performance indicators, downtime , or other issues may be provided as augmentation or overlays of the converged information technology and operations digital twin, [1037].
Regarding claim 11, The method of claim 10, further comprising temporarily pausing the RPA bot; automatically implementing pre-defined actions on the application server to remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; and resuming the RPA bot upon implementing the pre-defined actions. (The method step comprising temporarily pausing the RPA bot is a design choice with no benefit to the utility of the invention. The key element is to remedy the potential failure in an action yet to be executed and an RPA bot may be able to accept pre-defined actions while operating. Regardless, Cella teaches, (robot operating units that are deployed may be configured with one or more maintenance protocols to perform, among other things self-maintenance [ ]. Self-maintenance may include, without limitation, reduction in capabilities responsive to detection of a compromised robot operating unit feature, such as a rotating mechanism that no longer rotates continuously through 360 degrees. [ ] Also, robot operating unit intelligence (e.g., on robot AI and the like) may predict a compromise in robot capabilities based on, for example, time-to failure data for the robot capability. If the time of this predicted compromise lands within a target task performance timeframe, the robot operating unit may call for pre-emptive maintenance to be performed while the robot operating unit is in transit to a job site. The maintenance management system 12026 may process this call for maintenance and coordinate maintenance resources to be available during transit, and/or at a job site when the robot operating unit is expected to arrive, [2187].
It would be a design choice to include pausing the RPA bot for remediation and it would be obvious for one of ordinary skill in the art to rearrange parts of an invention, in this case including scheduled maintenance of fleet recourses in the field to mitigate impact on robot operating unit utilization failure, Cella - [0040], (MPEP 2144.04 I, 2144.04 VI (C)).
Regarding claim 12, The method of Claim 10, further comprising:
transmitting control signals to cause a user input device to display the predictive alert for manual intervention, Cella teaches, (The intelligence layer includes a set of artificial intelligence services that includes at least one of a machine learning service, a rules-based intelligence service, a digital twins service, a robot process automation service, [0014], and the respective actions include an action to request human intervention, [0016]).
Regarding claim 13, The method of claim 10, further comprising:
receiving the sequence of actions that the RPA bot is configured to execute; Cella does not teach, Geffen teaches, (The terms ‘software’, ‘program’, ‘software procedure’ or ‘procedure’ or ‘software code’ or ‘code’ or application’ may be used interchangeably according to the context thereof, and denote one or more instructions or directives or electronic circuitry for performing a sequence of operations that generally represent an algorithm and/or other process or method, [13:38-44] and a robotic process automation unit configured to pull tasks from the task queue database for processing said tasks, [1:42-43],
receiving information associated with the potential failure associated with the at least one action yet to be executed by the RPA bot; Cella teaches, (the predictive maintenance service 12165 may provide an intelligence request to the intelligence layer that includes current operational data obtained from the MPR 12100 (e.g., sensor data, environmental data, and/or the like), whereby the intelligence layer 12140 (e.g., the machine-learning service) may leverage one or more machine-learning models (e.g., prediction models, classification models, neural networks, and/or the like) to identify a potential failure of a component of the MPR 12100, [2315]),
identifying a specific location within the sequence of actions where the at least one action associated with the potential failure is situated; Cella does not teach, Geffen teaches, see Geffen FIG. 5, (failed process 502 may comprise a process ID, which may comprise the name of the process as well as the list of steps that were performed by the robotic automation unit 104 (FIG. 1). Failed process 502 may further comprise the step (or transition) which failed to complete by robotic automation unit 104,such to indicate where failure occurred during process of completing or attempting to complete a task, [11:30-37]),
dividing the sequence of actions into at least two distinct components, wherein a first
component comprises a sequence of actions up to and including the at least one action associated with the potential failure, (failure evaluation processor configured to collect the failed tasks per task type and to receive recordings of successful execution steps per each of the failed tasks, whereby the failure evaluation processor is further configured to evaluate the recorded successful execution steps with respect to the failed task types in order to provide selected execution steps that best fix the tasks that the robotic process automation unit failed to complete, thereby fixing the robotic process automation failures, Geffen, [1:48-56]), and a second component comprises a sequence of actions subsequent to the at least one action; and Cella does not teach, Geffen teaches, (GUI 600 may provide a detailed report per each task, as to whether it has been completed, whether it is being processed or whether it failed, see FIG. 6, 614, [12:30-32] and the failed tasks may be pulled for manual execution, e.g., by evaluators unit 108. The manual execution of the failed tasks may be recorded and store in real time database 120, [6:65-7:01]),
outputting the first component and the second component for further processing to the anomaly resolution subsystem to isolate and remedy the potential failure in the at least one action. Cella does not teach, Geffen teaches, (GUI 600 may provide a detailed report per each task, as to whether it has been completed, whether it is being processed or whether it failed, such that the failure evaluation processor 110 may receive the data per each task, in order to evaluate executed tasks with respect to failed tasks, [12:33-35]).
Cella and Geffen are both considered to be analogous to the claimed invention because they are both in the field of robotic process automation and application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the robotic fleet management approach of Cella with the methods for detecting and fixing robotic process automation failures of Geffen to thereby fixing the robotic process automation failures [1:55-56],
Regarding claim 14, The method of Claim 8, further comprising:
determining, using the anomaly resolution subsystem, remedial actions configured to
remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; Cella does not teach, Geffen teaches, (failure evaluation processor configured to collect the failed tasks per task type and to receive recordings of successful execution steps per each of the failed tasks, whereby the failure evaluation processor is further configured to evaluate the recorded successful execution steps with respect to the failed task types in order to provide selected execution steps that best fix the tasks that the robotic process automation unit failed to complete, thereby fixing the robotic process automation failures, [1:48-56]).
simulating, using the workflow replication subsystem, an execution of the remedial
actions; Cella teaches, (A job workflow system generates a workflow that defines an order of performance of the robot tasks based on the fleet resource configuration data structure and the set of robot tasks. A workflow simulation system is configured to simulate performance of the job based on the workflow and a job execution simulation environment, [0018], and the models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting deformations or failure in a 3D printed part. In embodiments, the models may also determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect. In embodiments, the system may send a warning or error signal to an operator or a user, [0050]).
determining whether the execution of the remedial actions remedy the potential failures associated with the at least one action yet to be executed by the RPA bot; Cella teaches, (the digital twin simulation system 2020 may be leveraged by the artificial intelligence system 2010 to test a decision made by the artificial intelligence system 2010 before providing the decision to the value chain entity, [0558]).
generating a server update based on the remedial actions in an instance where the
execution of the remedial actions remedies the potential failures; and deploying the server update to the application server, Cella teaches, (Referring to FIG. 23, any of the value chain network entities 652 can be depicted in a set of one or more digital twins 1700, such as by populating the digital twin 1700 with value chain network data object 1004, such as event data 1034, state data 1140, or other data with respect to value chain network entities 652, applications 630, or components or elements of the platform 604 [361], the platform may include [ ] vehicle twins 1850; robotics twins 1860; drone twins 1870; and logistics factor twins 1880; among others. Each of these may have characteristics of digital twins described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in states of associated physical objects or other entities, providing capabilities for modeling behavior or interactions of associated physical objects or other entities, enabling simulations, providing indications of status, and many others. [362], the digital twin simulation system 2020 may be leveraged by the artificial intelligence system 2010 to test a decision made by the artificial intelligence system 2010 before providing the decision to the value chain entity, [0558].
Cella and Geffen are both considered to be analogous to the claimed invention because they are both in the field of robotic process automation and application. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the robotic fleet management approach of Cella with the methods for detecting and fixing robotic process automation failures of Geffen to thereby fixing the robotic process automation failures [1:55-56],
Claims 1-7 and 15-20 are rejected for reasons corresponding to those provided for Claims 8-14. In these claims, the addition of a system and a non-transitory computer-readable medium configured to perform operations does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art, Cella teaches information technology methods and systems for management of value chain network entities and a non-transitory computer readable memory that is accessible by at least one processor of the set of processors [0028].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624