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 .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 18/317,801, filed on May 15, 2023. In response to Examiner's Non-Final Office Action dated November 12, 2025, Applicant on February 12, 2026, 2025, amended claims 1, 12, 14 and 15 and canceled claim 9. Claims 1, 3-8 and 10-15 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
The Claim Objections of claims 1, 12, 14 and 15 are hereby withdrawn in light of Applicant’s amendments to claims 1, 12, 14 and 15.
The 35 U.S.C. § 112(d) rejections of claims 9 are hereby withdrawn in light of Applicant canceling claim 9.
The 35 U.S.C. § 112(f) interpretation of claim 14 is hereby withdrawn in light of Applicant’s amendments to claim 14.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection
The 35 U.S.C. § 103 rejection of claims 1, 3-8 and 10- 15 are hereby amended pursuant Applicant’s amendments to claims 1 and 14.
Response to Arguments
Applicant's Arguments/Remarks filed February 12, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed February 12, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states the amended features of claim 1 are not mental steps that can be performed in a human mind or on pen and paper (see p. 8-9, Applicant Remarks).
In response, Examiner acknowledges Applicants remarks. The 35 U.S.C. 101 rejection has been updated in light of the current amendments. Please see below for updated rationale and analysis.
Regarding the 35 U.S.C. 101 rejection, Applicant submits that even if the claims could be considered to recite a judicial exception - a point not conceded - the amended claims integrate the alleged judicial exception into a practical application, under MPEP 22106.05 and USPTO Patent Examiner Guidance (see p. 9-10, Applicant Remarks).
In response, Examiner respectfully disagrees. Examiner finds the pending claims are not technological in nature and merely limits the abstract idea to a particular environment and thus fails to add an inventive concept to the claims; see MPEP 2106.05(h). Examiner finds Applicant’s arguments are directed to improvements to an existing business process, automated by the present invention without demonstrating an improvement to the technology or computer-related technology. Examiner notes Mackay Radio & Telegraph v. Radio Corp. of America is an example of applying a judicial exception with a particular machine. In this case, a mathematical formula was employed to use standing wave phenomena in an antenna system. The claim recited the particular type of antenna and included details of the shape of the antenna and the conductors, particularly the length and angle at which they were arranged. McRO, demonstrated improvements to a specific technological process (i.e., lip synchronization and manipulation of character facial expressions), thus improving computer animation without requiring an artist's constant intermediation with significant support in the specification and Enfish, assert improvements in computer capabilities (i.e., the self-referential table for a computer database, which achieves benefits over conventional databases). Additionally, Examiner notes DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014) presents an invention that is rooted in computer technology. Specifically, the court found when a third party's advertisement hyperlink was selected by a user on a host's web page, the system would automatically identify the host web page, retrieve corresponding "look and feel" information from storage for the host page and generate a hybrid web page including the merchant information from the third-party web page with the "look and feel" elements of the host's website. This is different from conventional Internet hyperlink operations which would redirect a user to the third-party page away from the host's web page when the hyperlink is activated and therefore added a specific limitation other than what is well-understood, routine and conventional in the field. Examiner finds there is no similar technology, technological problem or solution here. The claim currently discloses the determined error condition as an alternative limitation that is not currently required by the claim and the optional executable action commands are generic computer functions that follow rules or instructions (i.e., policy) based on the results of the data analysis. Examiner respectfully maintains, general purpose computer elements/structure, similar to the claimed inventions additional elements, used to apply a judicial exception, by use of instructions implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f).
Regarding the 35 U.S.C. 101 rejection, Applicant submits that the amended claims recite an inventive concept in an ordered combination (see p. 10, Applicant Remarks). This non-conventional arrangement is similar to the "inventive concept" recognized in BASCOM, which held that the inventive concept can lie in the non-conventional and non-generic arrangement of known components, and Amdocs, noting that an inventive concept could be found in distributed, unconventional architecture.
In response Examiner respectfully disagrees. Examiner notes BASCOM was found eligible based on considerations relevant to Part 2B (does the claim as a whole amount to significantly more than the abstract idea) of the two-part framework outlined in MPEP § 2106; where claim 1 "carve[s] out a specific location for the filtering system (a remote ISP server) and require the filtering system to give users the ability to customize filtering for their individual network accounts". In contrast, the amended claims do not recite similar features. Examiner finds Applicant’s claim is not analogous to the network customization in BASCOM and the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception. Applicant has not identified any disclosure in the claimed invention showing and/or submitting that the technology used is being improved, there was a technical problem in the technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than instructions used to determining actions commands for a Robotics Process Automation robot based on policy and operation metrics.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Office's generic conclusion that the elements are "well-understood, routine and conventional" is improper. Here, the claim's specific use of operating system-level performance counters combined with time-stamped workflow event streams, ML-based time-to-threshold forecasting tied to policy, policy-driven command synthesis with identifier and parameter values, same-session machine actuation, and model retraining on outcomes is far from the generic "collect-analyze-display" pattern rejected in the Office's cited cases of FairWarning and Credit Acceptance. Instead, the present claims are directed toward a self-improving machine-control loop that changes the behavior of an executing RPA robot. Such operations amount to significantly more than any alleged abstract idea.
For at least the foregoing reasons Applicant submits that the claims are directed toward patent eligible subject matter under 35 U.S.C. § 101. Independent claim 14 recites similar features as independent claim 1 and is eligible for similar reasons. Claims 2-13 and 15 are likewise, based at least on their dependency from claims 1 and 14. Accordingly, Applicant requests reconsideration and withdrawal of the 35 U.S.C. § 101 rejection of claim 1-15.
In response, Examiner respectfully disagrees. First, Applicants arguments regarding the Office’s generic conclusion that the elements are “well-understood, routine and conventional” are moot because Examiner never referred to or described any of the claim elements as “well-understood, routine and conventional”. Examiner maintains Applicants abovementioned arguments are not technological in nature and merely limits the abstract idea to a particular environment. Specifically, Examiner finds the use of operating system-level performance counters combined with time-stamped workflow event streams, ML-based time-to-threshold forecasting tied to policy, policy-driven command synthesis with identifier and parameter values, same-session machine actuation, and model retraining on outcomes is merely rules and instructions implementing the abstract idea using generic computer components and technology. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity analyzing RPA robot operational data to alter a workflow without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Examiner maintains the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception. Therefore, Examiner maintains the claims recite additional elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrate the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 11-14, filed February 12, 2026, with respect to the rejection(s) of claims 1, 3-8 and 10-15 under 35 U.S.C. 103 have been fully considered. However, upon further consideration, a new ground(s) of rejection is made. Applicant’s arguments are considered moot because they are directed to newly amended subject matter and do not apply to the combination of references being used in the current rejection. Please refer to the 35 U.S.C. 103 rejection for further explanation and rationale.
Claim Objections
Claim 14 is objected to because of the following informalities: grammatical error. Claim limitation “action comman” should recite “action command”. Appropriate correction is required..
Claim Rejections - 35 USC § 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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1, 3-8, and 10-12 are directed towards a method, claims 13 is directed towards a computer-readable storage medium and claims 14 and 15 are directed towards a system, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1, 3-8 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining actions commands for a Robotics Process Automation robot based on policy and operation metrics.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity. Specifically, determining CPU usage of the RPA robot, and generating a time-stamped sequence of events in the automation workflow; analysing the acquired operation metric data to (i) determine an error condition, or (ii) to predict that an operation metric will reach a policy-specified CPU threshold within a time period; determining, based on a policy and the results of the analysis, an action command comprising an identifier and a parameter value which is an automation of a business process constituting methods based on commercial interactions. The recitation of the RPA robot, operating system and processors and first machine learning model stored in memory does not take the claim out of the certain methods of organizing human activity grouping. Thus the claim recites an abstract idea. Claims 13 and 14 recite certain method of organizing human activity for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites acquiring, during execution of an automation workflow by the RPA robot, operation metric data via one or more sensors, which is considered an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Claim 1 further recites an RPA robot, operating system and processors stored in memory at a high-level of generality such that it amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, the instructions of causing the RPA robot, while executing the automation workflow, to automatically execute the action command, wherein the action command comprises at least one of: restarting execution of the automation workflow by the RPA robot, postponing or stopping the execution of the automation workflow by the RPA robot, changing an operation parameter of the RPA robot, skipping an optional stage in the automation workflow during execution, requesting different allocation of central processing unit, CPU, resources, requesting different allocation of memory resources, and requesting extra workload to be performed by the RPA robot. Thus, the additional element does not integrate the abstract idea into practical application because the RPA robot functions are not technological in nature and merely limits the abstract idea to a particular technological environment; see MPEP 2106.05(h). Claim 1 further recites analysing, by one or more processors and a first machine learning model stored in memory, the acquired operation metric data to (i) determine an error condition, or (ii) to predict that an operation metric will reach a policy-specified CPU threshold within a time period and performing, by the first machine learning model, a subsequent analysis based at least in part on stored information comprising at least an outcome of the executed action command. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claim is solely used as a tool to perform the instructions of the abstract idea. Claim 1 as a whole, looking at the additional element individually and in combination with the remaining claim limitations, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The computer-readable storage medium comprising executable instructions by a computer recited in claim 13 and the system comprising one or more processors and a memory storing instructions executable by the one or more processor in claim 14 also amount to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f). Thus, the additional elements recited in claims 13 and 14 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including system comprising one or more processors and a memory storing instructions executable by the one or more processor, operating system, a computer-readable storage medium and RPA robot, amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning model recited in the claims is disclosed at a high-level of generality (see at least Specification [0037]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claim 15recite wherein the system is providable as an external component to the automation workflow, which is not technological in nature and merely limits the abstract idea to a particular environment. Additionally, claims 3-8 and 10-12 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in the independent claims. Therefore claims 3-8, 10-12 and 15 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1 and 3-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kothandaraman et al., U.S. Publication No. 2019/0155225 [hereinafter Kothandaraman], and further in view of Hall et al., U.S. Publication No. 2018/0370029 [hereinafter Hall].
A computer-implemented method for managing a Robotics Process Automation, RPA, robot, the method comprising:
acquiring, during execution of an automation workflow by the RPA robot, operation metric data via one or more sensors for (i) determining, by the operating system, CPU usage of the RPA robot, and (ii) generating a time-stamped sequence of events in the automation workflow (Kothandaraman, [0028]), “provide information collected from bot adapters within a single glass view that provides real-time bot monitoring, such as insights about a bot's health, activity, current configuration(s), scheduling, logging, and alerts…provides process related key performance indicators (KPIs), infrastructure metrics, and exception to SLAs. The bot management framework is powered by a common data model”; (Kothandaraman, [0030]), “Each of the deployed bots 112 may generate execution logs and provide status updates as to the bot's health (e.g., state information, execution times, errors, and so forth)”; (Kothandaraman, [0046]), “the data is loaded in sequence to ensure the data integrity is maintained in terms of primary foreign key relationships created across the tables within the common data model employed within target CDM repository 230”; (Kothandaraman, [0036]; [0045]); and
analysing, by one or more processors and a first machine learning model stored in memory, the acquired operation metric data to (i) determine an error condition, or (ii) to predict that an operation metric will reach a policy-specified CPU threshold within a time period (Kothandaraman, [0034]), “a ticket may be raised by the bot management framework 120 when a process related exception is raised in RPA system 111, e.g., a process fails during the execution; the infrastructure (e.g., CPU, Memory, Disk, Network, Software Change, EventLog, and so forth) executing the process for the client/source system 110 raises an alert, for example CPU Utilization is greater than a threshold; or a threshold level from a measured metric of the bots 112 is crossed”; (Kothandaraman, [0047]), “The AI models employed within transform module 224 may be trained through a series of machine learning techniques applied to an algorithm using, for example, previously determined responses and categorizations of incidents raised within the RPA system 210 and/or training data provided by system administrators, which may include human agent observation. Such machine learning techniques automates model building and allows for the discovery of insights without explicit programming. Using automated and iterative algorithms, models may be trained to find high-order interactions and patterns within data”; (Kothandaraman, [0054]), “Example information regarding a corresponding RPA systems that can be displayed in the information box elements 352 includes a total number of processes and whether the processes have been terminated and/or completed, an average handling time of each monitored process, a productivity percentage for each monitored process, an indication of each monitored process's volume, exception thrown by each monitored process, a list of monitored process and bots, the efficiency of each monitored process and bot, the utilization (hourly or daily) of each monitored process and bot, the alerts from each monitored process and bot, the critical incidents of each monitored process and bot, a number of total open incidents within the RPA system, and a list of any incident SLAs”; (Kothandaraman, [0055]), “An adapter receives (402) information from an RPA system regarding bots deployed within the RPA system. The received information may include unstructured incident descriptions based on the incidents, such as alerts and exceptions, raised by the bots within the RPA system. The received information may include incident descriptions structured or formatted according to the specifications of the RPA product employed to build the RPA system, such as information retrieved through a stored procedure and bot/system API call. The adapter determines (404) categorization and assignment of incidents according to a common data model and based on a semantic analysis through a trained AI model of the incident descriptions”; (Kothandaraman, [0039]); and
determining, based on a policy and the results of the analysis, an action command comprising an identifier and a parameter value; causing the RPA robot, while executing the automation workflow, to automatically execute the action command (Kothandaraman, [0028]), “The bot management framework enables solutions that ensure the bots are run accurately, and without user intervention”; (Kothandaraman, [0055]), “… The categorization and assignment of incidents are persisted (406) in a common data model repository. The adaptor, determines (408) a resolution based on the AI model for at least one of the incidents based on the respective determined categorization and assignment of the at least one incident. The adaptor implements (410) the resolution, and the process ends”.
Kothandaraman teaches determining a resolution based on the AI model for at least one of the incidents based on the respective determined categorization and assignment of the at least one incident (see par. 0055), but Kothandaraman does not explicitly teach:
performing, by the first machine learning model, a subsequent analysis based at least in part on stored information comprising at least an outcome of the executed action command;
wherein the action command comprises at least one of: restarting execution of the automation workflow by the RPA robot, postponing or stopping the execution of the automation workflow by the RPA robot, changing an operation parameter of the RPA robot, skipping an optional stage in the automation workflow during execution, requesting different allocation of central processing unit, CPU, resources, requesting different allocation of memory resources, and requesting extra workload to be performed by the RPA robot.
However Hall teaches:
performing, by the first machine learning model, a subsequent analysis based at least in part on stored information comprising at least an outcome of the executed action command (Hall, [0038]), “The system 100 attempts to correct the error by accessing a script repair model. The script repair model may be trained to suggest a correction to the script to repair the error. The script repair model may use neural networks and machine learning to recommend a correction… The training data may include data related to the outcome of each correction and attempted correction. In some implementations, the training data may include data related to script actions and results for those actions that worked properly and did not generate an error”; (Hall, [0004]);
wherein the action command comprises at least one of: restarting execution of the automation workflow by the RPA robot, postponing or stopping the execution of the automation workflow by the RPA robot, changing an operation parameter of the RPA robot, skipping an optional stage in the automation workflow during execution, requesting different allocation of central processing unit, CPU, resources, requesting different allocation of memory resources, and requesting extra workload to be performed by the RPA robot (Hall, [0005]), “The system may update the script to include an action to locate the close button of a pop-up window in the event of an error in locating the captured portion. The action may be an optional action that the system performs if it detects a pop-up window. In some implementations, the system may also update other scripts. For example, the system may update other scripts that scan the user interface and to locate an area of the screen similar to the captured portion. The system may add a similar optional step to those other scripts”; (Hall, [0042]), “The system 200 may generate an error during execution of a script. The system 200 applies a script repair model to the error and the script to identify a modification to repair the script. The system may be implemented using one or more computing devices”; (Hall, [0056]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified incident resolutions in Kothandaraman to include the subsequent analysis and action command limitations as taught by Hall. The motivation for doing this would have been to improve the method of a management solution framework for a robotic process automation (RPA) system in Kothandaraman (see par. 0002) to include the results of a self-learning robotic process automation (see Hall par. 0006).
Referring to Claim 3, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein analysing the acquired data associated with the operation of the RPA robot during execution of the automation workflow to predict or determine an error comprises predicting or determining at least one of an error in the execution of the automation workflow by the RPA robot and an operation parameter to be optimised in the execution of the automation workflow by the RPA robot (Kothandaraman, [0039]), “analytics-based insights are provided through unified view 140 for continuous improvement and increased predictability in service levels, exceptions handling, and an automated audit trail and so that appropriate action can be taken proactively. Such analytics-based insights may include alerting the administrator 150 in case any business SLA is about to breach (i.e., reach a threshold), whether any of the bots 112 are overloaded or underutilized, whether any processes, such as the bots 112, productivity is low. The unified view 140 further provides administrator/bot controller 150 with screens to automatically raise an incident when any anomalies are detected or approaching a threshold within RPA system 111 at the infrastructure level, example of which may include CPU utilization, screen resolution changes, exceptions raised and which specific processes raise them, and so forth. Additionally, analytics-based insights for continuous improvement and increased predictability in service levels may also be depicted through the unified view 140. The determination and presentation to appropriate an administrator/bot controller 150 of these analytics-based insights drastically reduces exception handling time within the monitored RPA system 100 and allows for the bot 112 to quickly be brought back to an active mode/status”; (Kothandaraman, [0048]), “The transform module 224 may analyze received unstructured or structured claims data to identify optimization opportunities in automated processes, which includes reduction of errors in, for example, images, handwriting, or speech recognition. In some implementations, the transform module 224 transforms the received data in such a way that each record from the source RPA system 210 is properly mapped to the respective tables in the common data model based on, for example, the entity relationship created”; (Kothandaraman, [0012]; [0055]).
Referring to Claim 4, Kothandaraman in view Hall teaches the method according to claim 3. Kothandaraman further teaches:
wherein the predicted or determined error in the execution of the automation workflow by the RPA robot corresponds to an abnormal behaviour in the execution of the automation workflow (Kothandaraman, [0034]), “the bot management framework 120 raises a ticket in an incident management tool, such as Servicenow®, when an anomaly or incident is detected in the data received from the client/source system 110. For example, a ticket may be raised by the bot management framework 120 when a process related exception is raised in RPA system 111, e.g., a process fails during the execution; the infrastructure (e.g., CPU, Memory, Disk, Network, Software Change, EventLog, and so forth) executing the process for the client/source system 110 raises an alert, for example CPU Utilization is greater than a threshold; or a threshold level from a measured metric of the bots 112 is crossed. Example metrics measured for the bots 112 may include average handling time, productivity, volume, general bot status, efficiency, and utilization. More information is provided in FIG. 2 regarding the bot management adaptors 122”; (Kothandaraman, [0039]).
Referring to Claim 5, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein the optimising action further comprises at least one of: a message corresponding to the results of the analysis and an action recommendation corresponding to the results of the analysis (Kothandaraman, [0013]), “the resolution includes following an escalation procedure that includes alerting a bot controller assigned to the software bot of the anomaly and determined resolution”; (Kothandaraman, [0036]-[0037]).
Referring to Claim 6, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein the automatic action further comprises at least one of: changing an element of the environment in which the RPA robot operates, and storing information associated with the results of the analysis (Kothandaraman, [0031]), “…The example RPA system 111 includes the incident system 116. In some implementations, the incident system 116 may include various incident management tools to handle and escalate incidents as they occur to, for example, restore defined service levels of the RPA system 111”; (Kothandaraman, [0033]; [0036]).
Referring to Claim 7, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein analysing the acquired data comprises analysing the acquired data using at least one of a deterministic algorithm and a first machine learning model (Kothandaraman, [0047]), “The AI models employed within transform module 224 may be trained through a series of machine learning techniques applied to an algorithm using, for example, previously determined responses and categorizations of incidents raised within the RPA system 210 and/or training data provided by system administrators, which may include human agent observation. Such machine learning techniques automates model building and allows for the discovery of insights without explicit programming. Using automated and iterative algorithms, models may be trained to find high-order interactions and patterns within data”; (Kothandaraman, [0055]).
Referring to Claim 8, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
further comprising receiving at least one of: a user input to enable or disable automatic execution of the determined optimising action, a user input to confirm or reject the determined optimising action, a user input to agree or disagree with the predicted or determined error and/or the operation parameter to be optimised, a user input to provide an alternative to the predicted or determined error and/or the operation parameter to be optimised, a user input to agree or disagree with the determined optimising action, a user input to provide an alternative to the determined optimising action, and a user input indicating user feedback subsequent to execution of the optimising action (Kothandaraman, [0038]), “The unified view 140 may be a presentation interface that accepts input and provides output through various screens and/or pages… The unified view 140 provides access for a user, such as the administrator/bot controller 150, to the bot management framework built solution within example system 100, and more specifically, to the information received from the bot management adaptors 122 regarding the deployed bots 112 on the source RPA system 111, which is processed, modeled, and then stored in the common data model repository 130…an administrator is responsible to configure the master data. In some example, a bot controller is responsible to manage, monitor, and govern the machine where a bot/process is deployed (e.g., a box), a bot and corresponding process, and/or incidents in the configured environment”; (Kothandaraman, [0039]), “Such analytics-based insights may include alerting the administrator 150 in case any business SLA is about to breach (i.e., reach a threshold), whether any of the bots 112 are overloaded or underutilized, whether any processes, such as the bots 112, productivity is low. The unified view 140 further provides administrator/bot controller 150 with screens to automatically raise an incident when any anomalies are detected or approaching a threshold within RPA system 111 at the infrastructure level, example of which may include CPU utilization, screen resolution changes, exceptions raised and which specific processes raise them, and so forth”; (Kothandaraman, [0050]).
Referring to Claim 10, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein the policy is a rule-based policy that dictates a corresponding condition for comparison with the operation metric and an action to be carried out based on results of the comparison between the condition and the operation metric (Kothandaraman, [0010]), “the anomaly is one of the software bot being overloaded, the software bot being underutilized, a service level agreement (SLA) approaching a threshold regarding a breach of the SLA, an exception raised by the software bot or process associated with the software bot, or a central processing unit (CPU) utilization of an infrastructure assigned to execute the software bot reaching a threshold”; (Kothandaraman, [0034]), “the bot management framework 120 raises a ticket in an incident management tool, such as Servicenow®, when an anomaly or incident is detected in the data received from the client/source system 110. For example, a ticket may be raised by the bot management framework 120 when a process related exception is raised in RPA system 111, e.g., a process fails during the execution; the infrastructure (e.g., CPU, Memory, Disk, Network, Software Change, EventLog, and so forth) executing the process for the client/source system 110 raises an alert, for example CPU Utilization is greater than a threshold; or a threshold level from a measured metric of the bots 112 is crossed. Example metrics measured for the bots 112 may include average handling time, productivity, volume, general bot status, efficiency, and utilization. More information is provided in FIG. 2 regarding the bot management adaptors 122”; (Kothandaraman, [0039]; [0046]).
Referring to Claim 11, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman further teaches:
wherein the policy dictates an optimisation goal for the RPA robot during execution of the automation workflow, and wherein determining an optimising action is further based on at least one of a deterministic algorithm and a second trained machine learning model (Kothandaraman, [0047]-[0048), “The AI models employed within transform module 224 may be trained through a series of machine learning techniques applied to an algorithm using, for example, previously determined responses and categorizations of incidents raised within the RPA system 210 and/or training data provided by system administrators, which may include human agent observation. Such machine learning techniques automates model building and allows for the discovery of insights without explicit programming. Using automated and iterative algorithms, models may be trained to find high-order interactions and patterns within data…The transform module 224 may analyze received unstructured or structured claims data to identify optimization opportunities in automated processes, which includes reduction of errors in, for example, images, handwriting, or speech recognition”; (Kothandaraman, [0046]).
Referring to Claim 13, Kothandaraman teaches:
A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method of claim 1 (Kothandaraman, [0059]-[0060]; [0063]; [0007]).
Referring to Claim 14, Kothandaraman teaches:
A system for managing performance of a Robotics Process Automation, RPA robot, the system comprising one or more processors and a memory storing instructions, which when executed by the one or more processors, cause the system to at least (Kothandaraman, [0062]-[0063]):
Claim 14 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Referring to Claim 15, Kothandaraman in view Hall teaches the system according to claim 14. Kothandaraman further teaches:
wherein the system is providable as an external component to the automation workflow (Kothandaraman, [0066]), “The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a network, such as the described one. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other”; (Kothandaraman, [0030]), “The RPA system 111 may be installed within the client/source system 110 in the Cloud or at the enterprise's premises on either physical or virtual machines. The RPA system 111 may be integrated to deliver enterprise-wide automation solutions by managing the deployed software bots 112”; (Kothandaraman, Fig. 1, [0029]).
Claims 1 and 3-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kothandaraman et al., U.S. Publication No. 2019/0155225 [hereinafter Kothandaraman], in view of Hall et al., U.S. Publication No. 2018/0370029 [hereinafter Hall], and further in view of Rajagopalan et al., U.S. Publication No. 2022/0121982 [hereinafter Rajagopalan].
Referring to Claim 12, Kothandaraman in view Hall teaches the method according to claim 1. Kothandaraman teaches an intelligent and robust bot management framework that may be employed to provide bot management solutions for software bots deployed in a RPA system (see par. 0028), but Kothandaraman does not explicitly teach:
wherein acquiring data associated with an operation metric of the RPA robot during execution of an automation workflow comprises acquiring first data associated with a first session at a runtime resource corresponding to the automation workflow, and wherein the method further comprises: acquiring second data associated with an operation metric of the RPA robot during execution of the second automation workflow, wherein the second data are associated with a second session at the runtime resource; and determining an optimising action based on a second policy and the acquired second data.
However Rajagopalan teaches:
wherein acquiring data associated with an operation metric of the RPA robot during execution of an automation workflow comprises acquiring first data associated with a first session at a runtime resource corresponding to the automation workflow (Rajagopalan, [0077]-[0079]), and wherein the method further comprises:
acquiring second data associated with an operation metric of the RPA robot during execution of the second automation workflow, wherein the second data are associated with a second session at the runtime resource (Rajagopalan, [0079]), “Ongoing monitoring and evaluation of the workload executions and/or the computing system environment may comprise monitoring, logging, and/or analyzing real-time/runtime configuration parameter settings, values, and/or real-time/runtime performance metrics”; (Rajagopalan, [0077]-[0078]); and
determining an optimising action based on a second policy and the acquired second data (Rajagopalan, [0079]), “Logged real-time/runtime data may be validated against defined policies and/or templates. Deviations from acceptable values and ranges may be flagged to notify an administrative function of the deviations, initiate an analysis, and/or initiate a determination of one or more corrective actions that may be taken to bring the performance metrics back into conformance with the policies and/or templates”; (Rajagopalan, [0078]; [0080]), Examiner notes “Mere duplication of parts has no patentable significance unless a new and unexpected result is produced”; see MPEP 2144.04.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the RPA system in Kothandaraman to include the workflow and action limitations as taught by Rajagopalan. The motivation for doing this would have been to improve the method of a management solution framework for a robotic process automation (RPA) system in Kothandaraman (see par. 0002) to include the results of optimizing resource utilization, bandwidth utilization, and efficient operations of the enterprise computing infrastructure (see Rajagopalan par. 0002).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stocker et al. (US 20210191843 A1) – A system and a computer-implemented method for analyzing workflow of test automation associated with a robotic process automation (RPA) application are disclosed herein. The computer-implemented method includes receiving the workflow of the test automation associated with the RPA application and analyzing, via an Artificial Intelligence (AI) model associated with a workflow analyzer module, the workflow of the test automation based on a set of pre-defined test automation rules. The computer-implemented method further includes determining one or more metrics associated with the analyzed workflow of the test automation and generating, via the AI model, corrective activity data based on the determined one or more metrics.
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.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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