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 .
DETAILED ACTION
The Amendment filed on 2/9/2026 has been received and entered. Application No. 17/508,556 Claims 1-33 are now pending. Claims 1-11 are withdrawn. Claims 12 & 22 have been amended.
Response to Amendment
Applicant’s amendment necessitated new grounds of rejection.
This action is made final in view of the new grounds of rejection.
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.
Claim(s) 12-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oros, U.S. Pub. No. 2020/0134374 A1, published April 2020, in view of Belanger et al. (“Belanger”), U.S. Pub. No. 2019/0340684 A1, published November 2019, in view of Ganor (U.S. Pub 2018/0375892).
Regarding independent claim 12, Oros teaches an apparatus, the apparatus comprising: memory; and logic circuitry coupled with the memory to: organize controls into groups, and wherein each group is associated with a hardware resource; because Oros teaches dynamically updating, or retraining and updating, AI/ML, models in digital processes at runtime (pars. 0004-0008). Oros teaches a processor and memory to retrain an AI/ML model at runtime and provide dynamic updates (par. 0007-0008). Oros teaches the user interacts with web pages from web application 232 via browser 220 in this embodiment in order to perform various actions to control conductor 230; where the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc. (par. 0050). Oros teaches The REST API in this embodiment covers configuration, logging, monitoring, and queuing functionality. The configuration endpoints may be used to define and configure application users, permissions, robots, assets, releases, and environments in some embodiments. Logging REST endpoints may be used to log different information, such as errors, explicit messages sent by the robots, and other environment-specific information, for instance. Deployment REST endpoints may be used by the robots to query the package version that should be executed if the start job command is used in conductor 230. Queuing REST endpoints may be responsible for queues and queue item management, such as adding data to a queue, obtaining a transaction from the queue, setting the status of a transaction, etc. (par. 0052). Oros teaches a server 240 in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc.; and this information may be managed through a web application (par. 0054). Oros further teaches grouping a collection of applications as a solution, and grouping a collection of services (par. 0030).
Oros teaches engage robotics to access the hardware resource as part of testing of a selected one of the controls, wherein engagement of robotics comprises initiating code and a robotic mechanism to physically manipulate and to access information in the hardware resource; since Oros teaches robots can install the Microsoft Windows® Service Control Manager (SCM)-managed service by default, and as a result, such robots 130 can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service (par. 0041). Oros teaches unattended robots may be responsible for remote execution, monitoring, scheduling, and providing support for work queues wherein once a workflow is developed in designer 110, execution of business processes is orchestrated by conductor 120 which 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 nonproduction robots (similar to attended robots 132, but used for development and testing purposes). (par. 0037, 0038, 0039; see also par. 0042-0046).
Oros suggests teaches receive information collected from the hardware resource; because Oros teaches logging REST endpoints may be used to log different information, such as errors, explicit messages sent by the robots, and other environment-specific information (par. 0052-0053).
Oros does not expressly teach analyze the information collected from the hardware resource to determine differences between the information collected from the hardware resource and information expected based on the selected one of the controls associated with the hardware resource; however, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Oros does not expressly teach infer one or more remedial actions based on the differences; and perform the one or more remedial actions; however, Belanger teaches a value mapping the event to an entry in a taxonomy may indicate whether the event is an action taken by the subject-entity being controlled, an exogenous event, an event to be avoided and for which risk is to be calculated, or an act upon the subject-entity at the direction of the controller or other system to be controlled by the controller, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, such as a goal reflected in an objective function composed by a developer to effectuate the goal, like reducing or minimizing risk of bad outcomes, maximizing likelihood of good outcomes, or a net result based on a combination of both (par. 0051). Belanger discloses that examples include an attribute of a robot movement event indicating whether the robot movement event resulted in the robot moving closer to a barrier to be avoided, or an attribute of a datacenter event indicating whether a datacenter remains within a targeted band of temperature for a targeted duration of time (par. 0051). Belanger teaches the stream may be a real time stream, for instance, with data being supplied as it is obtained by, or in relation to, subject entities, for instance, in queries sent as the data is obtained to request a recommended responsive action in view of the new information (par. 0054). Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
However, Oros as modified does not explicitly teach wherein each of the controls defines a configuration, setting, or a software version that is required for mitigation of risk for an organization related to failure to comply with at least one requirement, wherien at least one of the controls concerns mitigation of risk related to failure to comply with a law, regulation, rule, or industry standard,
Ganor teaches wherein each of the controls defines a configuration, setting, or a software version that is required for mitigation of risk for an organization related to failure to comply with at least one requirement, wherien at least one of the controls concerns mitigation of risk related to failure to comply with a law, regulation, rule, or industry standard, (Fig. 3, Fig. 4A, ¶59 wherein ser interface logic 310 may include logic to allow a user (e.g., a CISO, network security manager, business owner, etc.) to view various aspects of an enterprise's security to determine compliance with an enterprise's security policies, regulations and privacy laws. For example, a CISO may wish to view information regarding whether the policies associated with security/protection of enterprise assets are being properly followed, view budgetary information regarding an enterprise's security budget (e.g., whether audits are being performed on time and within the allotted budget), view data/knowledge bases associated with enterprise security compliance, view statuses of current or past cyber threats and whether enterprise policies with respect to the handling of cyber threats has been performed in accordance with the enterprise's policies, etc. User interface logic 310 may also allow the CISO to input a search query regarding any of various security systems/devices in the enterprise, such as firewalls, network access control devices, anti-virus software, intrusion detections systems, etc., or assets in a company's enterprise network, and identify whether the responsible security personnel/teams are performing their tasks/functions in accordance with company policies/procedures to mitigate risk.)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of enterprise cyber security risk management and resource planning of Ganor with the teaching dynamic artificial intelligence of Oros as modified because Ganor teaches an improved system configured to monitor enterprise activity associated the enterprise's networked and determine, based on the enterprise activity, whether the enterprise is complying with the security policies and procedures. The device is also configured to calculate a risk exposure metric for an asset of the enterprise based on the enterprise activity and whether the enterprise is complying with the security policies and procedures, and output, to the display, a graphical user interface (GUI) identifying the risk exposure metric. The device may also be configured to receive, via the GUI, an input to initiate a change with respect to at least one of the enterprise's networked devices or initiate the generation of a plan to make a change to at least one of the networked devices. (¶Abstract)
Regarding dependent claim 13, Oros teaches the apparatus of claim 12, wherein receipt of information collected from the hardware resource comprises receipt of information via interpretation logic circuitry and a network connection; since Oros teaches a processor and memory to retrain an AI/ML model at runtime and provide dynamic updates (par. 0007-0008). Oros teaches the user interacts with web pages from web application 232 via browser 220 in this embodiment in order to perform various actions to control conductor 230; where the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc. (par. 0050). Oros teaches a server 240 in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc.; and this information may be managed through a web application (par. 0054).
Regarding dependent claim 14, Oros teaches in part the apparatus of claim 12, wherein the remedial actions comprise initiating code and a robotic mechanism to physically manipulate and access the hardware resource; because Oros teaches UI automation activities 330 facilitate these interactions via drivers 340 that allow the robot to interact with the desired software (par. 0057). However, Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055). Belanger teaches executing the functionality in code (par. 0084).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 15, Oros teaches in part the apparatus of claim 14, wherein the remedial actions comprise initiating code and a robotic mechanism to physically manipulate and to update the hardware resource, wherein updating the hardware resource comprises changing a hardware component of the hardware resource, updating a software application installed on a hardware resource, updating a configuration of the hardware resource, updating settings of a hardware resource, uninstalling a software application from a hardware resource, uninstalling a hardware component of the hardware resource, or a combination thereof; because Oros teaches UI automation activities 330 facilitate these interactions via drivers 340 that allow the robot to interact with the desired software (par. 0057).
However, Belanger teaches a value mapping the event to an entry in a taxonomy may indicate whether the event is an action taken by the subject-entity being controlled, an exogenous event, an event to be avoided and for which risk is to be calculated, or an act upon the subject-entity at the direction of the controller or other system to be controlled by the controller, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, such as a goal reflected in an objective function composed by a developer to effectuate the goal, like reducing or minimizing risk of bad outcomes, maximizing likelihood of good outcomes, or a net result based on a combination of both (par. 0051). Belanger discloses that examples include an attribute of a robot movement event indicating whether the robot movement event resulted in the robot moving closer to a barrier to be avoided, or an attribute of a datacenter event indicating whether a datacenter remains within a targeted band of temperature for a targeted duration of time (par. 0051). Belanger teaches the stream may be a real time stream, for instance, with data being supplied as it is obtained by, or in relation to, subject entities, for instance, in queries sent as the data is obtained to request a recommended responsive action in view of the new information (par. 0054). Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 16, Oros teaches the apparatus of claim 12, the logic circuitry is configured to autonomously access multiple hardware resources to verify implementation of controls; since Oros teaches robots can install the Microsoft Windows® Service Control Manager (SCM)-managed service by default, and as a result, such robots 130 can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service (par. 0041). Oros teaches unattended robots may be responsible for remote execution, monitoring, scheduling, and providing support for work queues (par. 0039; see also par. 0042-0046).
Regarding dependent claim 17, Oros does not expressly teach the apparatus of claim 16, the logic circuitry is configured to autonomously update multiple hardware resources in response to determination of differences between actual configurations and settings and expected configurations and settings based on controls associated with the multiple hardware resources; however, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 18, Oros teaches the apparatus of claim 16, the logic circuitry is configured to autonomously access and update, as needed, the multiple hardware resources continuously; because Oros teaches continuously updating the robots’ logic implemented via physical hardware (par. 0087-0090).
Regarding dependent claim 19, Oros does not expressly disclose the apparatus of claim 12, further comprising interpretation logic circuitry, the interpretation logic circuitry to convert raw data into input data for a machine learning model, the raw data provided by robotics including a picture of a screen, a screen shot, a log file, or a combination thereof; however, Belanger teaches the events may further include exogenous events, which are events that are not caused by the controller 12 or the subject entity to which a record pertains, but to which the subject entity is exposed or potentially exposed (par. 0047-0050), or sensor readings from sensors of a robot (par. 0054).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 20, Oros teaches the apparatus of claim 19, wherein the interpretation logic circuitry comprises a machine reading service (MRS); because Oros teaches a read text file activity 932 reads the text of the current file that “for each” activity 920 is iterating through in the folder (par. 0081).
Regarding dependent claim 21, Oros does not expressly teach the apparatus of claim 12, wherein the controls is associated with one or more operational requirements, regulatory agencies, regulatory jurisdictions, geographical locations, or a combination thereof; however, Belanger teaches the system can facilitate compliance with regulatory agencies and geographical locations (par. 0034; see also par 0031-0033 wherein In Australia, the Anti-Money Laundering and Counter-Terrorism Financing Act 2006 (AML/CTF Act) gives effect to KYC laws. The Anti-Money Laundering and Counter-Terrorism Financing Rules Instrument 2007 provides guidance for applying the powers and requirements of the Act. Compliance is governed by the Australian Government agency, Australian Transaction Reports and Analysis Centre, established in 1989, known as AUSTRAC. In Canada, the Financial Transactions Reports Analysis Centre of Canada, also known as FINTRAC, was created in 2000 as Canada's financial intelligence unit. FINTRAC updated its regulations in June 2016 regarding acceptable methods to determine the identity of individual clients to ensure compliance with AML and KYC regulations. In the United Kingdom, the Money Laundering Regulations 2017 are the underlying rules that govern KYC in the UK. Many UK businesses use the guidance provided by the European Joint Money Laundering Steering Group along with the Financial Conduct Authority's ‘Financial Crime: A guide for firms’ as an aid to compliance. In the United States, pursuant to the USA Patriot Act of 2001, the Secretary of the Treasury was required to finalize regulations before Oct. 26, 2002 making KYC mandatory for all US banks. The related processes are required to conform to a customer identification program (CIP).).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oros, U.S. Pub. No. 2020/0134374 A1, published April 2020, in view of Belanger et al. (“Belanger”), U.S. Pub. No. 2019/0340684 A1, published November 2019, in view of Ganor (U.S. Pub 2018/0375892), further in view of Parales (U.S. Pub 2009/0018885).
Regarding dependent claim 22, Oros does not disclose the apparatus of claim 12, wherein at least one of the controls is associated with cybersecurity standards, or a combination thereof, associated with or related to regulatory requirements in the single regulatory jurisdiction; however, Parales teaches the system is associated with cybersecurity standards (Fig. 2, par. 0054; wherein a control could be implemented in the software area to ensure that all software purchased is licensed for network distribution prior to being made available on the network via a server. A control intended to reinforce the software control could be implemented in the people area by requiring that the group leader of the information technology department approve all postings of software for network distribution prior to copying of the program to the server. As illustrated in FIG. 2, controls may also be implemented via automated processes, such as a network security provisioning system that operates to monitor compliance by individuals with the existing controls (i.e., the provisioning system notifies the group leader if a member of the group posts software without the group leader first registering an approval of the posting within the provisioning system).).
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of risk management and compliance system of Parales with the teaching of dynamic artificial intelligence of Oros as modified because Parales teaches and improved compliance, risk management, and auditing systems wherein configured to receive one or more compliance and risk drivers and a control objective database coupled with the compliance database and configured to receive one or more control objectives based at least in part on the one or more compliance and risk drivers. The system may include one or more controls configured to correspond with the one or more control objectives stored in the control objective database and configured to monitor a business activity to produce one or more monitoring results. One or more tests may be coupled with the one or more controls and may be configured to validate the performance of the one or more controls. A control risk evaluation module may be coupled with the one or more controls and may be coupled with a risk database. The control risk evaluation module may be configured to evaluate the one or more controls using one or more risk criteria stored in the risk database and to produce one or more control gaps (¶3, ¶6)
Claim(s) 23-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oros, U.S. Pub. No. 2020/0134374 A1, published April 2020, in view of Belanger et al. (“Belanger”), U.S. Pub. No. 2019/0340684 A1, published November 2019, in view of SISBOT (U.S. Pub 2016/0077526) hereinafter Sisbot, further in view of Parales (U.S. Pub 2009/0018885).
Regarding independent claim 23, Oros teaches A Computer system comprising: a memory storing requirements and controls for organization, wherein requirements specify things required of the organization and controls are constructs for mitigating or reducing risks to the organization; and logic circuitry coupled with the memory to: access uncorrelated requirements; because Oros teaches dynamically updating, or retraining and updating, AI/ML, models in digital processes at runtime (pars. 0004-0008). Oros teaches a processor and memory to retrain an AI/ML model at runtime and provide dynamic updates (par. 0007-0008). Oros teaches the user interacts with web pages from web application 232 via browser 220 in this embodiment in order to perform various actions to control conductor 230; where the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc. (par. 0050). Oros teaches The REST API in this embodiment covers configuration, logging, monitoring, and queuing functionality. The configuration endpoints may be used to define and configure application users, permissions, robots, assets, releases, and environments in some embodiments. Logging REST endpoints may be used to log different information, such as errors, explicit messages sent by the robots, and other environment-specific information, for instance. Deployment REST endpoints may be used by the robots to query the package version that should be executed if the start job command is used in conductor 230. Queuing REST endpoints may be responsible for queues and queue item management, such as adding data to a queue, obtaining a transaction from the queue, setting the status of a transaction, etc. (par. 0052). Oros teaches Indexer server 250, which is optional in some embodiments, stores and indexes the information logged by the robots. In certain embodiments, indexer server 250 may be disabled through configuration settings. In some embodiments, indexer server 250 uses ElasticSearch®, which is an open source project full-text search engine. Messages logged by robots (e.g., using activities like log message or write line) may be sent through the logging REST endpoint(s) to indexer server 250, where they are indexed for future utilization.(par. 0055). Oros teaches a server 240 in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc.; and this information may be managed through a web application (par. 0054). Oros further teaches grouping a collection of applications as a solution, and grouping a collection of services (par. 0030).
infer new controls for the uncorrelated requirements; (¶74 wherien workflow running as a service that monitors the sentiment of comments received from customers where the workflow is implemented as an FSM. The workflow may monitor a folder for new files that include the customer comments and indicate whether the sentiment in the comments is positive or negative. FIG. 6 is a screenshot of a flow diagram of an FSM 600 configured to dynamically update, or retrain and update, an AI/ML model at runtime, according to an embodiment of the present invention. FSM 600 is shown in UiPath Studio™.)
Oros does not expressly teach infer one or more remedial actions based on the new controls; and perform the one or more remedial actions; however, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Belanger teaches a value mapping the event to an entry in a taxonomy may indicate whether the event is an action taken by the subject-entity being controlled, an exogenous event, an event to be avoided and for which risk is to be calculated, or an act upon the subject-entity at the direction of the controller or other system to be controlled by the controller, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, such as a goal reflected in an objective function composed by a developer to effectuate the goal, like reducing or minimizing risk of bad outcomes, maximizing likelihood of good outcomes, or a net result based on a combination of both (par. 0051). Belanger discloses that examples include an attribute of a robot movement event indicating whether the robot movement event resulted in the robot moving closer to a barrier to be avoided, or an attribute of a datacenter event indicating whether a datacenter remains within a targeted band of temperature for a targeted duration of time (par. 0051). Belanger teaches the stream may be a real time stream, for instance, with data being supplied as it is obtained by, or in relation to, subject entities, for instance, in queries sent as the data is obtained to request a recommended responsive action in view of the new information (par. 0054). Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
However, Oros as modified does not explicitly teach requirements which are requirements that are not mapped to a control; infer new controls to mitigate risks of the requirements; and infer remedial actions to mitigate one or more risks associated with one or more of the requirements.
Parales teaches Once the existing controls in the process are identified, a control mapping module 142 may be used to 1) determine the risks and compliance issues that the existing controls are handling, and 2) evaluate the process using the policies 80 to map existing policies 80 to corresponding implemented controls. The resulting process documentation 138 and control information from the control identification module 140 and the control mapping module 142 are sent to the gap analysis module 124 which may determine what control gaps exist with 1) the existing controls in the process, and 2) what additional control requirements 96 will be needed for the existing controls and for developing new controls to mitigate risks or deal with compliance issues noted. The new control requirements 96 may be forwarded along indicator E for storage in the control requirements library 98.(par. 0081).The audit report analysis module 162 may also be used to send information relating to the existing controls and process to a control map generating module 164. The control map generating module 164 may operate to produce a wide variety of summaries of the existing controls and/or process documentation including, by non-limiting example, a control map 166, a list of all controls and risks mitigated by the controls, a flowchart showing process steps and corresponding controls, organization charts, or any other document or summary that may be helpful for the audit or documentation purposes.(par. 0086). Parales also teaches The mitigation analysis module 122 may also receive information from a control remediation module 132 and from the gap analysis module 124 relating to risks and or compliance issues that need to be evaluated and determine what control gaps will allow mitigation of them. The mitigation analysis module 122 sends generated control gaps to the control remediation module 132 for implementation. The control remediation module 132 may execute some or all of the control design and test design modules illustrated in FIG. 6 as part of remediating one or more controls in the process using the control gaps from the mitigation analysis module 122 and the gap analysis module 124. In addition, the control remediation module 132 may determine the area of the process and/or the individuals who should work to close the control gap.(par. 0077)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of risk management and compliance system of Parales with the teaching of dynamic artificial intelligence of Oros as modified because Parales teaches and improved compliance, risk management, and auditing systems wherein configured to receive one or more compliance and risk drivers and a control objective database coupled with the compliance database and configured to receive one or more control objectives based at least in part on the one or more compliance and risk drivers. The system may include one or more controls configured to correspond with the one or more control objectives stored in the control objective database and configured to monitor a business activity to produce one or more monitoring results. One or more tests may be coupled with the one or more controls and may be configured to validate the performance of the one or more controls. A control risk evaluation module may be coupled with the one or more controls and may be coupled with a risk database. The control risk evaluation module may be configured to evaluate the one or more controls using one or more risk criteria stored in the risk database and to produce one or more control gaps (¶3, ¶6)
However, Oros as modified does not explicitly teach wherein the remedial actions comprise initiating code and a robotic mechanism to physically manipulate and to update a hardware resource to mitigate the one or more risks associated with the one or more of the requirements based on the controls,
Sisbot teaches wherien the remedial actions comprise initiating code and a robotic mechanism to physically manipulate and to update a hardware resource to mitigate the one or more risks associated with the one or more of the requirements based on the controls, (Fig. 1, Fig. 2, ¶22 wherein the risk management system 199 may include code and routines for using the robot to prevent risk to a visually impaired user, for example, a blind user. The risk management system 199 identifies a point of interest, such as a drop, a fixed obstacle, or a mobile object. The risk management system 199 determines whether the point of interest is a risk for the user based on the user's position. The risk management system 199 estimates the degree of risk based on distance and confidence. If a first predetermined threshold for distance and a second predetermined threshold for confidence are met, the risk management system 199 determines where to move the robot 190 to mitigate risk to the user. In some embodiments, the risk management system 199 also instructs the robot 190 to provide a warning to the user, such as an auditory warning or a tactile warning in the form of vibrations)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of Robot assist for detecting, managing, and mitigating risk of Sisbot with the teaching dynamic artificial intelligence of Oros as modified because Sisbot teaches a innovative system for preventing risk to a user wherein the system identifies a point of interest, determines whether the point of interest is a risk for a user with visual impairments based on the user's position, responsive to a first predetermined threshold for distance being met, determine where to move a robot to mitigate the risk to the user, and instruct the robot to move to a new location. (¶4)
Regarding dependent claim 24, Oros teaches the computer system of claim 23, the one or more processors are configured determine uncorrelated controls, controls that do not correlate to requirements, and retire the uncorrelated controls; because Oros teaches comparing performance of the retrained AI/ML model to a performance threshold, against performance of a previous version of the AI/ML model, or both, and only updating the AI/ML model if the new version meets performance criteria; in certain embodiments, if these criteria are not met, no update request is sent (par. 0106).
Regarding dependent claim 25, Oros does not expressly teach the computer system of claim 23, wherien the one or more processors are configured to further: infer a risk associated with one of the uncorrelated requirements; determine if the risk exceeds a risk threshold; and if the risk exceeds the risk threshold, infer one or more new controls to correlate with the one of the uncorrelated requirements to mitigate the risk; however, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Belanger teaches embodiments mitigate a risk management system using an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs) that considers the entire variable set concurrently to capture the latent interactions amongst the variables (par 0027-0028). Belanger teaches a risk threshold determination (par. 0038).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 26, Oros teaches the computer system of claim 25, wherein updating the hardware resource comprises changing a hardware component of the hardware resource, updating a software application installed on the hardware resource, updating a configuration of the hardware resource, updating settings of the hardware resource, uninstalling a software application from the hardware resource, uninstalling a hardware component of the hardware resource, or a combination thereof; because Oros teaches UI automation activities 330 facilitate these interactions via drivers 340 that allow the robot to interact with the desired software (par. 0057).
However, Belanger teaches a value mapping the event to an entry in a taxonomy may indicate whether the event is an action taken by the subject-entity being controlled, an exogenous event, an event to be avoided and for which risk is to be calculated, or an act upon the subject-entity at the direction of the controller or other system to be controlled by the controller, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, such as a goal reflected in an objective function composed by a developer to effectuate the goal, like reducing or minimizing risk of bad outcomes, maximizing likelihood of good outcomes, or a net result based on a combination of both (par. 0051). Belanger discloses that examples include an attribute of a robot movement event indicating whether the robot movement event resulted in the robot moving closer to a barrier to be avoided, or an attribute of a datacenter event indicating whether a datacenter remains within a targeted band of temperature for a targeted duration of time (par. 0051). Belanger teaches the stream may be a real time stream, for instance, with data being supplied as it is obtained by, or in relation to, subject entities, for instance, in queries sent as the data is obtained to request a recommended responsive action in view of the new information (par. 0054). Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 27, Oros teaches the computer system of claim 23, wherein the one or more processors are configured to autonomously access controls mapped to requirements and uncorrelated requirements and infer new controls for the uncorrelated requirements; because Oros teaches dynamically updating, or retraining and updating, AI/ML, models in digital processes at runtime (pars. 0004-0008). Oros teaches a processor and memory to retrain an AI/ML model at runtime and provide dynamic updates (par. 0007-0008). Oros teaches the user interacts with web pages from web application 232 via browser 220 in this embodiment in order to perform various actions to control conductor 230; where the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc. (par. 0050). Oros teaches a server 240 in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc.; and this information may be managed through a web application (par. 0054). Oros further teaches grouping a collection of applications as a solution, and grouping a collection of services (par. 0030).
Regarding dependent claim 28, Oros does not expressly teach the computer system of claim 27, wherien the one or more processors are configured to autonomously infer the one or more remedial actions based on the new controls; and perform the one or more remedial actions, wherein the one or more remedial actions comprise engagement of robotics; however, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Belanger teaches a value mapping the event to an entry in a taxonomy may indicate whether the event is an action taken by the subject-entity being controlled, an exogenous event, an event to be avoided and for which risk is to be calculated, or an act upon the subject-entity at the direction of the controller or other system to be controlled by the controller, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, such as a goal reflected in an objective function composed by a developer to effectuate the goal, like reducing or minimizing risk of bad outcomes, maximizing likelihood of good outcomes, or a net result based on a combination of both (par. 0051). Belanger discloses that examples include an attribute of a robot movement event indicating whether the robot movement event resulted in the robot moving closer to a barrier to be avoided, or an attribute of a datacenter event indicating whether a datacenter remains within a targeted band of temperature for a targeted duration of time (par. 0051). Belanger teaches the stream may be a real time stream, for instance, with data being supplied as it is obtained by, or in relation to, subject entities, for instance, in queries sent as the data is obtained to request a recommended responsive action in view of the new information (par. 0054). Belanger teaches examples of actions include setting a process parameter setpoint (like temperature, rate of acceleration, robot route, workload allocation among data center (par. 0055).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 29, Oros teaches the computer system of claim 27, wherien the one or more to autonomously access and update, as needed, the multiple hardware resources continuously; since Oros teaches robots can install the Microsoft Windows® Service Control Manager (SCM)-managed service by default, and as a result, such robots 130 can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service (par. 0041). Oros teaches unattended robots may be responsible for remote execution, monitoring, scheduling, and providing support for work queues (par. 0039; see also par. 0042-0046).
Regarding dependent claim 30, Oros teaches in part the computer system of claim 23, wherein inference of new controls for the uncorrelated requirements comprises creating input data for at least one machine learning engine based on an uncorrelated requirement; and identifying a new control by the at least one machine learning engine based on the input data; because Oros teaches comparing performance of the retrained AI/ML model to a performance threshold, against performance of a previous version of the AI/ML model, or both, and only updating the AI/ML model if the new version meets performance criteria; in certain embodiments, if these criteria are not met, no update request is sent (par. 0106).
However, Belanger teaches a process of associating interaction event records with respective risks (par. 0005), and teaches a system to manage (e.g., infer and effectuate decisions based on) continuous risk as a time series of events and actions taken (or not) within a system's context (this may include human, computing and other types of components) and implement a methodology to continuously assess a continuous risk posture (par. 0023), where machine learning models may be trained using inference systems to convert static risk management models into a nonlinear mapping system configured to adapt itself to new input data (par. 0026). Belanger teaches a controller 12 is configured to train a risk scoring machine learning model based upon historical interaction-event records 14 and then use the trained model to characterize risk as a continuous stochastic variable that is updated as current events are received via the event streams (par. 0042-0044). Belanger teaches each record may be time-series of events for one of a relatively large number of independent entities for which actions are selected to influence behavior or responsive to predicted behavior, such as of different people in a population, or in some embodiments, the entity may be non-human, for instance, a state of a robot, a manufacturing process, a market, or a datacenter's HVAC systems (par. 0044-0045). Belanger teaches an example of a control system for a datacenter HVAC system, examples include applying a particular set point for temperature or humidity for some duration of time, setting a fan speed for some duration of time, adjusting a balance between external and internal air recirculation, and the like (par. 0045). Belanger teaches the events may include actions taken by nonhuman subjects, for instance, changing a process setpoint, actuating a thruster in a particular direction for a particular duration, or undertaking a computing load in a datacenter for some duration (par. 0046; par. 0050).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 31, Oros does not disclose the computer system of claim 23, wherein the controls are associated with one or more regulatory requirements, statutory requirements, operational requirements, regulatory agencies, standards organizations, regulatory jurisdictions, geographical locations, or a combination thereof; however, Belanger teaches the system can facilitate compliance with regulatory requirements (par. 0034; see also par 0031-0033).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 32, Oros does not disclose the computer system of claim 23, wherein the controls are associated with industry standards, cybersecurity standards, or a combination thereof, associated with or related to regulatory requirements in the single regulatory jurisdiction; however, Belanger teaches the system can facilitate compliance with regulatory requirements (par. 0034; see also par 0031-0033).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Regarding dependent claim 33, Oros does not disclose the computer system of claim 23, the logic circuitry to further receive a new control via manual input in response to an inability to infer a new control for one or more of the uncorrelated requirements; however, Belanger teaches that in some embodiments, risk management is assessed using a batch data processing model which classifies data in a supervised training environment (par. 0025; see also par. 0038-0039).
Both Oros and Belanger were directed to applying machine learning models to robots. It would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to have combined the dynamically updating ML models taught by Oros, with the process of associating interaction event records with respective risks taught by Belanger, where events corresponding to actions by the subject-entity may have attributes indicating whether the respective event is advancing a goal, since Belanger recognized that there was a need in the art for computation to assess the likelihood of undesirable probabilistic events in industrial process controls and complex systems (par. 0003), and therefore would have provided these benefits to Oros.
Response to Arguments
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection wherien Ganor is relied upon to teach the following newly amended limitation “wherein each of the controls defines a configuration, setting, or a software version that is required for mitigation of risk for an organization related to failure to comply with at least one requirement, wherien at least one of the controls concerns mitigation of risk related to failure to comply with a law, regulation, rule, or industry standard,”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-Form 892 for listed of cited references.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGIE BADAWI whose telephone number is (571)270-7590. The examiner can normally be reached Monday thru Wednesday 9:00am - 5:00pm EST with Thursdays and Fridays off.
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/ANGIE BADAWI/Primary Examiner, Art Unit 2179