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 .
The office action is in response to the application filed on November 30, 2023.
Claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed November 30, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
“the secondary users though a user interface" should read "the secondary users through a user interface"
Appropriate correction is required.
Claims 11-19 are objected to because of the following informalities:
the recitation of “one or more computer readable storage media” encompasses transitory forms of signal transmission and therefore fails to accurately reflect the scope of the invention as described in the specification. Examiner’s Note: Applicant is required to amend Claim 11 to recite “one or more non-transitory computer readable storage media” to bring the claim into conformity with the specification [0039] and to avoid claiming subject matter that the specification expressly disclaims.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 non-obviousness.
Claims 1, 2, 3, 5, 7, 8, 11, 12, 13, 15, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, (US20220114044A1, filed October 14, 2020), in view of Podgorny et. Al, (US10162734B1, filed on July 20, 2016, hereinafter "Podgorny"), further in view of Ghag et. Al, (US20230039566Al, filed on December 17, 2021, hereinafter "Ghag"). The specified dates are before the effective filing date of this application, i.e., November 30, 2023 where it applies.
With respect to independent Claims 1, 11, and 20:
Singh teaches:
“generating, by the processor set, a robot process automation (RPA) bot which compiles and runs a plurality of tests for executing an automated workflow verification process for each of the secondary users;” (Paragraph [0064] teaches creating/monitoring/deploying an RPA bot that runs a plurality of automated workflow verification tests for each of the secondary users, “Conductor 120 may manage a fleet of robots 130, connecting and executing robots 130 from a centralized point. Types of robots 130 that may be managed include, but are not limited to, attended robots 132, unattended robots 134, development robots (similar to unattended robots 134, but used for development and testing purposes), and non-production robots (similar to attended robots 132, but used for development and testing purposes).” Paragraph [0105] further teaches generating an RPA bot for executing workflow verification, “The process begins with executing an RPA robot (and thus, an RPA workflow) that performs a UI automation using an AI/ML model at 705. Using the AI/ML model, the RPA robot searches for a target graphical element in the UI to be interacted with by an activity of the RPA workflow at 710. When the target graphical element is uniquely found by the AI/ML model at 715, the RPA robot interacts with the target graphical element according to one or more RPA activities in the RPA workflow at 720. The RPA robot then continues executing the RPA workflow logic at 725 until an interaction with a next graphical element is needed.” Paragraph [0107] further discloses performing a plurality of tests for each of the secondary users, “In some embodiments, when the self-healing process was not successful, the automatic attempt to correct the anomaly includes attempting one or more different techniques and monitoring whether the one or more different techniques improve the one or more performance metrics. In certain embodiments, the self-healing process includes polling a plurality of users to provide proposed solutions to the anomaly and selecting a most optimal solution of the proposed solutions based on one or more performance metrics.”) Examiner’s Note: processor set infrastructure addressed in greater detail [0087].
“performing, by the processor set, global tracking for similar automated workflow verification processes as the RPA bot in multiple environments;” (Paragraph [0035] teaches tracking activities from RPA workflows in order to facilitate widespread training reuse in multiple environments, “UI descriptors may be extracted from activities in an RPA workflow and added to a structured schema that groups the UI descriptors by UI applications, screens, and UI elements. UI descriptors may be part of one project for wide reuse, part of global repositories for testing purposes, or part of UI object libraries for global cross-project sharing in some embodiments.” Paragraph [0098] further teaches the ability to perform global tracking for similar automated RPA bot workflows, “In some embodiments, server 630 may be part of a public cloud architecture, a private cloud architecture, a hybrid cloud architecture, etc. In certain embodiments, server 630 may host multiple software-based servers on a single computing system 630. Server 630 includes AI/ML models 632 in this embodiment that are called by RPA robots 610 to perform operations.” Paragraph [099] further describes similar automated workflow processes, “may allow information from multiple or many computing systems to be utilized, potentially providing more samples, examples of how users overcame issues, etc.”)
“and training, by the processor set, an artificial intelligence (Al) model based on success metrics of the RPA bot and the similar automated workflow verification processes in multiple environments.” (Paragraph [0107] discloses training the AI model as a result of the RPA bot’s success in regards to following through with the automated workflow verification processes in multiple environments, “the automatic attempt to correct the anomaly includes attempting a self-healing process to complete missing data without user input, by the RPA robot or the AI/ML model. This may be accomplished via an exploration phase in reinforcement learning, for example. In certain embodiments, the RPA robot or the AI/ML model is configured to determine whether the self-healing process was successful by monitoring whether one or more performance metrics improve responsive to the self-healing process.” Paragraph [0108] further teaches facilitating the training of the AI model with respect to the automated workflow process running in multiple environments during future deployments, “When the automatic attempt to correct the anomaly is successful at 735, data pertaining to the automatic correction is provided (e.g., sent to a remote server and database such as server 630 and database 640 of FIG . 6) for subsequent retraining of the AI/ML model at 740, and the process proceeds to step 720.”)
“wherein the success metrics comprise optical character recognition (OCR) metrics, screen comparison metrics, and whether a secondary user was unable to successfully execute the automated workflow verification process.” (Paragraph [0056] teaches obtaining success metrics (self-healing measure) in regards to OCR, “For document understanding or other applications, for example, if human validation is frequently required, it can be inferred that the AI/ML model is not good enough. The AI/ML model may employ various techniques to attempt to improve its own performance as a “self-healing” measure. For instance, the AI/ML model may try a different optical character recognition (OCR) engine, modify the properties of the image (e.g., brightness, hue, contrast, convert to grayscale, etc.), search for images that appear similar and check the technique(s) and/or results that were applied for those images, etc.” Paragraph [0104] further teaches screen comparison being utilized to analyze patterns (obtain metrics), “Using multiple layers may allow the system to develop a global picture of what is happening in the screens. For example, one AI layer could perform OCR, another could detect buttons, another could compare sequences, etc. Patterns may be determined individually by an AI layer or collectively by multiple AI layers.” Paragraph [0109] further teaches an instance of a specified secondary user failing to successfully execute the automated workflow verification process, “When the guidance provided by the user is not successful in enabling the RPA robot to interact with the target graphical element at 750, and the target graphical element is not necessary to complete an overall task of the RPA workflow and continued operation is possible at 755, the RPA robot may continue execution of the RPA workflow at 725. This may be possible, for instance, if the target graphical element is not required to complete the overall task with a sufficient degree of accuracy. However, when continued operation is not possible at 755, an exception is thrown at 760 and the process ends.”) Examiner’s Note: Podgorny representative of a plurality of secondary users as per second user(s) 182 [Fig. 1]. *This limitation is an exclusive additional limitation to Independent Claim 20 only.
Singh alone does not appear to explicitly disclose the remaining limitations of the independent claims.
However, Podgorny teaches:
“determining, by the processor set, that a primary user, different from the secondary users, encounters the error;” ([col. 14 lines 62-67] discloses distinguishing that the primary user (first user) is not only different from the secondary user(s) (second user), but is also the one to encounter the error/a separate set of errors than the secondary user(s), “The potential issues 140 include first errors 144 and first questions 146 that correspond with errors and questions that are identified, and/or submitted by one or more of the first users 172…” [col. 15 lines 3-9] further teaches this distinction, “The first errors 144 and/or the first questions 146 represent errors and questions identified by users who are concurrently going through a tax return preparation interview, but who have already visited one or more of the user experience pages 114 that the second user 182 is currently experiencing a potential issue with, according to one embodiment.”) Examiner’s Note: processor set infrastructure addressed in greater detail [col. 20].
“engaging, by the processor set, the primary user with the secondary users though a user interface (UI) to ask whether the secondary users have also encountered the error that the primary user encountered;” ([col. 17 lines 21-31] discloses determining if the secondary user(s) have encountered the same error as the primary user by presenting the issue resolution content as a form of inquiring for diagnostics (ask), “The issue resolution engine 118 is configured to insert issue resolution content 156 into one or more of the user experience pages 114,…, and/or into the user experience display 124, to enable viewing of the issue resolution content 156 by the second user 182, according to one embodiment. The issue resolution engine 118 causes the issue resolution content 156 to be displayed to the second user 182, in response to detecting and/or determining that the second user 182 is likely experiencing one or more potential issues 140, according to one embodiment.” [col. 19 lines 11-17] further discloses asking the secondary users if they’ve encountered errors similar to that of the primary user, “The issue resolution engine 118 searches through one or more of the first questions 146 to define relevant ones of the first questions 146 and/or to provide one or more of the most relevant ones of the first questions 146 to the second user 182, in attempt to immediately or quickly respond to one or more of the second questions 162, according to one embodiment.”)
Singh and Podgorny are analogous art and in the same field of invention because both references pertain to proactive error resolution and minimizing user frustration when interacting with complex digital systems. While Singh teaches mitigating the unreliability of standard UI automation where bots often fail or crash if interface elements move or change, Podgorny teaches proactively offering assistance and gathering error logs in the background to resolve system issues automatically. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Singh (RPA Self-Healing via AI/ML) with the teachings of Podgorny (Human/Crowdsourced Approach) in order to defuse technical errors and streamline the user journey without requiring exhaustive human intervention and lengthy disruptions. One of ordinary skill in the art would be motivated to do so because by integrating Podgorny's framework into the methods of Singh one would be able to provide, "improvement to the field efficient deployment of software systems. As a result, service providers can accomplish more development with less manpower and/or deploy additional technology under shorter integration delays, {[col. 3, lines 46-49] of Podgorny}.”
The combination of Singh-Podgorny does not appear to explicitly disclose the remaining limitations of the independent claims.
However, Ghag teaches:
"receiving, by a processor set, an opt-in to give consent to capture information which determines whether a plurality of secondary users encounters an error;" (Paragraph [0048] teaches an onboarding process performed by a processor set that retrieves consent related permissions (access credentials) from the user in order to fulfill error encountering determination (monitoring tasks/remediation action plans), “The access of all the RPA component to the RPA platform may be verified by way of web API or direct call to database. Before initiating the auto-discovery, the system may request user to provide access credentials (username and password) in case of API or the database credentials (username and password) in case of call to database. The system may then use these credentials to check access permission before initiating auto discovery process. Further, access to other RPA components requires RPA admin to grant permission to the credentials which may be used by the system to query RPA components for verification checks…may comprise steps such as configuring credentials for script execution and other parameters, activating the monitoring task and activating remediation plans for the RPA components,” Paragraph [0061] further discloses that this capturing of information is done to determine if a plurality of secondary users encounters an error (anomaly), “If the validation determines that if there is a breach of threshold, then the captured observable metric may be marked as an anomaly and an event/alert may be raised to take further actions which could be sending out a notification to users and/or triggering an automated remediation process as configured.” Paragraph [0056] also teaches receiving opt-in consent (inputs) from the user to capture information (mark/identify patterns) that will be useful in determining errors that secondary users can/might encounter, “The error type labeling module 236 may receive inputs from user which allows the user to mark/identify patterns identified by the error pattern extraction engine 242 as errors. This will help the system to learn from this labeling and use the captured knowledge to be leveraged for other RPA components where similar errors may occur.”) Examiner’s Note: Processor set infrastructure addressed in greater detail [0041-0043]. Distinction between the plurality of secondary users and the primary user (in certain embodiments: “administrator/user”) is recorded [0008].
Singh, Podgorny, and Ghag are analogous art and in the same field of invention because all three references pertain to automated error detection and remediation designed to identify operational issues and execute solutions to minimize human intervention. While Singh teaches improving quality assurance by automatically triggering system fixes through self-healing functionalities, Podgorny teaches tracking primary and secondary users’ system access data across networked workflow pages. Similarly, Ghag teaches monitoring RPA resources by comparing observation metric values to implement resolution protocols upon anomaly detection. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Singh (RPA computer vision remediation) with the teachings of Podgorny (crowdsources UX error resolution) and the teachings of Ghag (backend RPA automated remediation) in order to establish technological innovations focused on automating issue detection, accelerating debugging, and increasing system reliability across complex software workflows. One of ordinary skill in the art would be motivated to do so because by integrating Podgorny and Ghag's frameworks into the methods of Singh one would be able to recognize that a system as such can, "drastically reduce the number of simulations required to achieve a winning state, which enables AI/ML models trained via reinforcement learning to be developed and deployed more quickly, to be trained using fewer computing hardware resources, or both., {[0058] of Singh}."
Therefore, Claims 1, 11, and 20 are rejected.
With respect to Claims 2 and 12:
The combination of Singh-Podgorny-Ghag teaches:
"further comprising providing feedback using the Al model to improve response accuracy in real-time." (Singh Paragraph [0056] discloses monitoring the AI model for potential feedback regarding how to improve accuracy in real-time, “The AI/ML model may monitor whether the human validation effort decreases, speed and/or efficiency of execution increases (e.g., the process runs faster and/or steps in an RPA workflow can be sped up or eliminated), or the return on investment (ROI) improves to determine whether the self-healing efforts of the AI/ML model are working. If not, the Al/ML model may try different techniques and/or use different information. The AI/ML model may then learn how to complete missing, incorrect, and/or incomplete data at runtime based on this self-healing approach.”)
Therefore, Claims 2 and 12 are rejected.
With respect to Claims 3 and 13:
The combination of Singh-Podgorny-Ghag teaches:
"wherein the error is a problem related to a local computing machine of the primary user." (Ghag Paragraph [0065] discloses the error (anomaly) being present on the primary user’s local computing machine, “The analytics engine 210 may be configured to notify user through a notification displayed at the computing device associated with the user when an anomaly is detected such as bots stopped running, bot running for long, VM is down, database not connecting, etc. Also, the analytics engine 210 may be configured to display the status of the remediation action performed.”)
Therefore, Claims 3 and 13 are rejected.
With respect to Claims 5 and 15:
The combination of Singh-Podgorny-Ghag teaches:
"further comprising presenting a consent and complete button to the secondary users to opt-in to executing the automated workflow verification process." (Podgorny [pg. 18 col. 2 lines 41-45] discloses presenting consent and complete buttons to the secondary users in regards to launching an automated workflow verification process (presenting issue resolution content), “The second errors 160 are described, characterized, or otherwise identified simply by the second user 182 pressing a selection button that indicates that the second user 182 experienced an error in one or more of the user experience pages 114…” [pg. 21 col. 1 lines 20-31] further teaches presenting iterations of consent and complete buttons to the secondary users in order to initiate an automated workflow verification process (resolving error-based questions by presenting issue resolution content), “The user experience page 200 includes a user selection element 202 and a user selection element 204, to identify whether the user has a question or has identified an error, according to one embodiment. The user selection element 202 is a button that allows the user to indicate (e.g., by selecting the button) that the user has a question about the content of one or more user experience pages…to indicate (e.g., by selecting the button) that the user has identified an error or malfunction.” [col. 22 lines 17-21] describes an example of executing the automated workflow verification process, “The sidebar widget is an example of issue resolution content that is provided to a user to support crowdsourcing quality assurance testing and/or error detection of user experience pages…”)
Therefore, Claims 5 and 15 are rejected.
With respect to Claims 7 and 17:
The combination of Singh-Podgorny-Ghag teaches:
"wherein the success metrics comprise optical character recognition (OCR) metrics and screen comparison metrics." (Singh Paragraph [0104] discloses OCR-based screen comparison being utilized to analyze differentiations/patterns (obtain metrics), “Using multiple layers may allow the system to develop a global picture of what is happening in the screens. For example, one AI layer could perform OCR, another could detect buttons, another could compare sequences, etc. Patterns may be determined individually by an AI layer or collectively by multiple AI layers.” Paragraph [0106] further discloses obtaining success metrics (feature differentiations), “In some embodiments, the automatic attempt to correct the anomaly includes determining whether one or more features differentiate the target graphical element from other similar graphical elements. In certain embodiments, the determining of whether the one or more features differentiate the target graphical element from the other similar graphical elements includes analyzing graphical elements surrounding the target graphical element within a radius, utilizing an order of the graphical elements in the UI, determining whether the target graphical element has one or more different visual characteristics, or a combination thereof.” Paragraph [0107] further reinforces that success metrics for the RPA bot coincide with screen comparisons and OCR-related techniques, “the automatic attempt to correct the anomaly includes attempting one or more different techniques and monitoring whether the one or more different techniques improve the one or more performance metrics.”)
Therefore, Claims 7 and 17 are rejected.
With respect to Claims 8 and 18:
The combination of Singh-Podgorny-Ghag teaches:
"wherein the success metrics further comprise whether a secondary user was unable to successfully execute the automated workflow verification process." (Singh paragraph [0109] discloses an instance of when this specified secondary user fails to successfully execute the automated workflow verification process, “When the guidance provided by the user is not successful in enabling the RPA robot to interact with the target graphical element at 750, and the target graphical element is not necessary to complete an overall task of the RPA workflow and continued operation is possible at 755, the RPA robot may continue execution of the RPA workflow at 725. This may be possible, for instance, if the target graphical element is not required to complete the overall task with a sufficient degree of accuracy. However, when continued operation is not possible at 755, an exception is thrown at 760 and the process ends.”) Examiner’s Note: Podgorny representative of an instance and a plurality of secondary users as per second user(s) 182 [Fig. 1].
Therefore, Claims 8 and 18 are rejected.
Claims 1, 2, 3, 5, 7, 8, 11, 12, 13, 15, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, (US20220114044A1, filed October 14, 2020), in view of Podgorny et. Al, (US10162734B1, filed on July 20, 2016, hereinafter "Podgorny"), further in view of Ghag et. Al, (US20230039566Al, filed on December 17, 2021, hereinafter "Ghag"), further in view of Singh, (US20220113703A1, filed October 14, 2020, hereinafter “Singh(ii)”). The specified dates are before the effective filing date of this application, i.e., November 30, 2023 where it applies.
With respect to Claims 4 and 14:
The combination of Singh-Podgorny-Ghag does not appear to explicitly disclose:
"wherein the opt-in gives consent to run in a background on a local computing machine of each of the plurality of secondary users."
However, Singh(ii) teaches:
"wherein the opt-in gives consent to run in a background on a local computing machine of each of the plurality of secondary users." (Paragraph [0020] discloses that each one of the pluralities of secondary user machines (respective computing systems) encapsulates a listener/recorder process that runs in the background, “In some embodiments, RPA robots or other listener/recorder processes may watch user interactions with respective computing systems. The listener/recorder processes may determine recurring user actions and the content. In some embodiments, the reasons for the user actions may also be determined. The recorder/listener process may then suggest an automation to the user or create the automation automatically…” Paragraph [0021] further teaches that opt-in consent (checking with the user) is utilized before the background process is run and replicated for a plurality of secondary users, “there may be a training phase where the recorder/listener process checks with the user before automating user actions and receives labeled training data to further train the AI/ML model. Alternatively, automatically generated RPA robots may be rolled out to a subset of users initially, potentially further train the AI/ML model during this phase. The RPA robot could then be rolled out to a broader group of user computing systems if the automation is successful/beneficial.”)
Singh, Podgorny, Ghag, and Singh(ii) are analogous art and in the same field of invention because all four references pertain to eliminating manual intervention, reducing downtime, and making software interactions resilient across robotic process automation (RPA) and user interface (UI) workflow. While Singh teaches searching for information and automatically adapting to error-encompassing scenarios rather than remaining in a failure state, Podgorny teaches providing real-time resolution content to the user while logging the data to resolve the bug for future users. Similarly, while Ghag teaches automatically executing a remediation plan when a technical failure occurs on the backend, Singh(ii) teaches pattern recognition and proactive assistance in relation to anticipating when a task needs to be completed based on external triggers in repeated workflows. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Singh (graphical element interaction and self-healing) with the teachings of Podgorny (UX monitoring and predictive troubleshooting) in view of the teachings of Ghag (auto-discovery and automated remediation) further in view of the teachings of Singh(ii) (intelligent task observation and habit automation) in order to solve the inefficiency, high cost, and human error associated with repetitive software tasks, bug detection, and system maintenance. One of ordinary skill in the art would be motivated to do so because by integrating Podgorny, Ghag, and Singh(ii)'s frameworks into the methods of Singh one could understand how it, "helps developers, support users, and computing systems more easily run, identify, and track what each component is executing, {[0037] of Singh(ii)}."
Therefore, Claims 4 and 14 are rejected.
With respect to Claims 6 and 16:
The combination of Singh-Podgorny-Ghag does not appear to explicitly disclose:
"further comprising generating the RPA bot in response to at least one of the secondary users pressing the consent and complete button to opt-in to executing the automated workflow verification process."
However, Singh(ii) teaches:
"further comprising generating the RPA bot in response to at least one of the secondary users pressing the consent and complete button to opt-in to executing the automated workflow verification process." (Paragraph [0077] discloses generating an RPA bot upon consent (indication) from at least one secondary user to allow task automation, “the user of the respective computing system is asked whether automation of the respective task is desired at 815. However, in certain embodiments, this step may not be employed. If the user indicates that the automation is desired at 815, or potentially automatically without user input, RPA workflow(s) implementing the respective task(s) (e.g., the initiating task and the responsive task) are generated at 820. The RPA workflow(s) may include activities that implement the user interactions associated with the respective task. RPA robot(s) are then generated at 825 using the RPA workflow(s), and the RPA robot(s) are deployed at 830.”) Examiner’s Note: The user of the respective computing system in this instance is representative of at least one of the secondary user(s) as a result of monitoring user interactions between the first users and second users [Fig. 8]. Task automation is selected/initiated via UiPath Studio™ design-based buttons [0026].
Therefore, Claims 6 and 16 are rejected.
Claims 1, 2, 3, 5, 7, 8, 11, 12, 13, 15, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, (US20220114044A1, filed October 14, 2020), in view of Podgorny et. Al, (US10162734B1, filed on July 20, 2016, hereinafter "Podgorny"), further in view of Ghag et. Al, (US20230039566Al, filed on December 17, 2021, hereinafter "Ghag"), further in view of Fung et. Al, (US20230379173A1, filed June 1, 2023, hereinafter “Fung”). The specified dates are before the effective filing date of this application, i.e., November 30, 2023 where it applies.
With respect to Claims 9 and 19:
The combination of Singh-Podgorny-Ghag does not appear to explicitly disclose:
"wherein the UI comprises a chat system."
However, Fung teaches:
"wherein the UI comprises a chat system." (Paragraph [0079] discloses the user interface encompassing chat functionality, “The user interface 700 includes elements 602-604 described above in connection with FIG. 6. The user interface 700 also includes an indication (702) that user data was shared with the bot in response to user granting the permission, e.g., by selecting input element 612. The user interface 700 also includes a message (704) from the bot that indicates that the bot is working on the request, and one or more optional suggestions from the bot (706 and 708). If a user selects one of the suggestion elements (706, 708), the bot can cause to be displayed details about the suggestion from the bot…” Paragraph [0085] further teaches characteristics of the chat system, “At 812, the bot can start a one-to-one chat with the user. The one-to-one chat and the messages exchanged in the one-to-one chat are not visible to the group of users in the group messaging conversation.”)
Singh, Podgorny, Ghag, and Fung are analogous art and in the same field of invention because all four references pertain to proactively identifying and resolving software, process, or security friction in automated digital systems to mitigate broken workflows, and reduce the high cost of manual system maintenance. While Singh teaches exhibiting self-restoration and resilience practices within RPA robots amidst user interface changes, Podgorny teaches utilizing predictive models to actively identify potential user issues, providing real-time resolution content, and prompting users for more information to help troubleshoot the error for everyone. Similarly, while Ghag teaches actively "auto-discovering" RPA components and their specific dependencies to automatically remediate any anomalies within the RPA platform itself, Fung teaches enforcing permission data privacy management before granting access to digital communication bots in order to limit the risks of unauthorized data access and security. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Singh (visual anomaly detection/self-healing RPA) with the teachings of Podgorny (debugging and predictive issue resolution) in view of the teachings of Ghag (automated remediation in RPA environments) further in view of the teachings of Fung (bot permission control for messaging apps) in order to leverage machine learning and predictive models to shift from reactive manual troubleshooting to proactive system resilience. One of ordinary skill in the art would be motivated to do so because by integrating Podgorny, Ghag, and Fung's frameworks into the methods of Singh one would be able to note, "a reduction in the problem of consumption of system processing and transmission resources required for completing user tasks across communication networks, {[0044] of Fung}."
Therefore, Claims 9 and 19 are rejected.
With respect to Claim 10:
The combination of Singh-Podgorny-Ghag teaches:
"wherein the opt-in gives consent to capture information--------------" (Ghag Paragraph [0048] teaches an onboarding process that retrieves consent related permissions (access credentials) from the user, “The access of all the RPA component to the RPA platform may be verified by way of web API or direct call to database. Before initiating the auto-discovery, the system may request user to provide access credentials (username and password) in case of API or the database credentials (username and password) in case of call to database. The system may then use these credentials to check access permission before initiating auto discovery process. Further, access to other RPA components requires RPA admin to grant permission to the credentials which may be used by the system to query RPA components for verification checks…may comprise steps such as configuring credentials for script execution and other parameters, activating the monitoring task and activating remediation plans for the RPA components,” Paragraph [0061] further discloses that this capturing of information is done relative to a plurality of secondary users encountering an error (anomaly), “If the validation determines that if there is a breach of threshold, then the captured observable metric may be marked as an anomaly and an event/alert may be raised to take further actions which could be sending out a notification to users and/or triggering an automated remediation process as configured.” Paragraph [0056] also teaches receiving opt-in consent (inputs) from the user to capture information (mark/identify patterns), “The error type labeling module 236 may receive inputs from user which allows the user to mark/identify patterns identified by the error pattern extraction engine 242 as errors. This will help the system to learn from this labeling and use the captured knowledge to be leveraged for other RPA components where similar errors may occur.”) Examiner’s Note: Distinction between the plurality of secondary users and the primary user (in certain embodiments: “administrator/user”) is recorded [0008].
The combination of Singh-Podgorny-Ghag does not appear to explicitly disclose:
“--on a local computing machine of each of the plurality of secondary users.”
However, Fung teaches:
“--on a local computing machine of each of the plurality of secondary users." (Paragraph [0070] discloses the presence of a plurality of secondary users with their respective local computing devices, “FIG. 3 is a diagram of an example arrangement of two or more user devices and a single bot or assistive agent in communication in accordance with some implementations. In the example arrangement show in FIG. 3, user devices 302-306 (e.g., 115a-115n from FIG. 1) may be in a group messaging conversation that includes a bot 308 (e.g., 105, 107a, 107b, 109a, 109b, 111, and/or 113). One or more of the users associated with the user devices 302-306 (e.g., one or more of users 125a-125n) may interact with the bot 308 and engage in a communication session with the bot 308. Some or all of the communications from the bot may be placed into the group messaging conversation. Also, some information provided to and from the bot 308 may only be available, e.g., displayed, to the user associated with that information.” Paragraph [0065] further teaches that these local computing machines embody a plurality of secondary user(s), utilizing their consent to carry out information/knowledge gathering processes, “If the second user selects this response, the assistant bot is added to the conversation and the message is sent to the bot. A response from the bot may then be displayed in the conversation, and either of the two users may send further messages to the bot. In this example, the assistant bot is not provided access to the content of the conversation, and suggested responses are generated by the messaging application 103.”)
Therefore, Claim 10 is rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Bangalore et. Al (US20200192736A1) regarding Claim 1, Ahmed et. Al (US20220101217A1) regarding Claims 3, 5, 6, and 8, and Subramanian et. Al (US20250028586Al).
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/N.F.C./ Examiner, Art Unit 2142
/Mariela Reyes/ Supervisory Patent Examiner, Art Unit 2142
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