Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Amendments to the claims in view of 103 rejection have been carefully considered but are moot in view of the new ground(s) 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.
Claims 1-8, 12, 13, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 20200244704 A1) in view of Jacob (US 20200379609 A1) in view of He (US 20190086988 A1) in view of Thornley (US 20190278651 A1) in view of CHAKRADHAR (US 20230067473 A1), and in view of KELLY (US 20170346760 A1).
Regarding claim 1, Andrews teach an Information Handling System (IHS), comprising:
a heterogeneous computing platform comprising a plurality of devices (Fig.1. Para [0009]); and
a memory coupled to the heterogeneous computing platform, wherein the memory comprises a plurality of sets of firmware instructions, wherein each of the plurality of sets of firmware instructions, upon execution by a respective device among the plurality of devices in a respective device memory space independent of a host processor memory space wherein the host processor is configured to execute at least an IHS Operating System (OS) (Para [0005]. Para [0031]. Para [0041]: an IHS 203 includes system memory includes the operating system 405. The system memory includes the operating system 405. System memory 403 including, (e.g., RAM, ROM and/or cache memory) may be configured to store a plurality of software and/or firmware modules, including but not limited to, operating system (OS) 405, and data collection module(s) 409. The software and/or firmware modules stored within system memory 403.), enables the respective device to provide a corresponding firmware service (Para [0041]: [0041] System memory 403 including, (e.g., RAM, ROM and/or cache memory) may be configured to store a plurality of software and/or firmware modules, including but not limited to, operating system (OS) 405, and data collection module(s) 409. The software and/or firmware modules stored within system memory 403 contain program instructions (or computer program code), which are executed by processing device(s) 401 to instruct components of IHS 203 to perform various tasks and functions for the information handling system, as well as to perform various steps of the methods disclosed herein.), and wherein at least one of the plurality of devices operates as an orchestrator configured to perform operations that comprise (Para [0005]- [0007]):
identify one of a plurality of privacy modes to be applied to the IHS (Para [0025], the manage service 201 receives telemetry information from the IHSs 203, and based on information received from each of the IHSs, supplies dynamically created policies (Privacy modes) based on the information received from or about the IHSs. Para [0027]-[0031]); and
based upon the identified privacy mode, modify one or more IHS settings (Para [0025]. Para [0027]-[0028]. Para [0038]. Para [0046]. Para [0052]. Para [0054]: a dynamically created policy may modify a current policy being applied to an IHS. Management service policy engine 201 generates a custom policy for respective ones of the IHSs in response to information received from or about the individual IHSs. The customized policy controls IHS privacy settings (e.g., control or manage logon security features (e.g., multifactor authentication, user password, etc.), control access (e.g., to a secure or public Wi-Fi, local and/or network storage device, to input/output devices of the IHS (e.g., a camera or microphone)).
Andrews does not explicitly disclose device configured to communicate via a communication bus through at least one firmware service to firmware service Application Programming Interface (API); (Perform an action) via the at least one firmware service to firmware service API;
Jacob teaches device configured to communicate via a communication bus through at least one firmware service to firmware service Application Programming Interface (API) (Para [0049]. Para [0052]: the machine learning framework 158 can include an API 162 and the machine learning model 128 can be executed using different machine learning frameworks 158 by accessing the respective API of such frameworks.);
(Perform an action) via the at least one firmware service to firmware service API (Para [0049]. Para [0052]: the machine learning framework 158 can include an API 162 and the machine learning model 128 can be executed using different machine learning frameworks 158 by accessing the respective API of such frameworks.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews of firmware service with the teachings of Jacob to include the well-known technique of device configured to communicate via a communication bus through at least one firmware service to firmware service Application Programming Interface (API); (Perform an action) via the at least one firmware service to firmware service API because the results would have been predictable and resulted in ensuring secure communication between devices, especially when configuring a communication bus to connect through firmware services that expose Application Programming Interfaces (APIs).
Andrews in view of Jacob does not explicitly disclose determine a plurality of runtime resource utilization metrics based on context or telemetry data indicative of runtime resource utilization, wherein the plurality of runtime resource utilization metrics comprise at least a core utilization, a memory utilization, a network utilization, a battery utilization, and a peripheral device utilization; determine whether any of a plurality of runtime resource utilization metric threshold conditions are satisfied for the plurality of runtime resource utilization, and select, from among a plurality of versions of an Artificial Intelligence (AI) model each having metrics a different level of computational complexity, a selected version of the Al model based, at least in part, upon the determination, execute at least one instance of an Artificial Intelligence (AI) the Al model of the selected version; modify at runtime at least one parameter of the at least one instance of the AI model in execution; (perform an action) of the at least one instance of the AI model in execution.
He does disclose determine a plurality of runtime resource utilization metrics based on context or telemetry data indicative of runtime resource utilization, wherein the plurality of runtime resource utilization metrics comprise at least a core utilization, a memory utilization, a network utilization, a battery utilization, and a peripheral device utilization (Para [0109]-[0110]: device status may be determined (block 815). For example, smart engine 420 may obtain device status information from device status module 430, such as a device mode, a battery level value, a processor load value, a memory use value, a network connection quality value, one or more application criticality values for an application running on UE device 110, and/or other types of device status information. Additionally, smart engine 420 may obtain device data from data acquisition module 450.);
determine whether any of a plurality of runtime resource utilization metric threshold conditions are satisfied for the plurality of runtime resource utilization, and select, from among a plurality of versions of an Artificial Intelligence (AI) model each having metrics (Para [0112]: If it is determined that machine learning is to be performed (block 820—YES), a machine learning model may be selected based on the determined device status (block 830). Smart engine 420 may select a particular machine learning module 470 and/or one or more options for the particular machine learning module 470 based on the determined device status to match an expected resource use of the selected machine learning module 470 with the current resource capacity of the wireless communication device associated with the determined device status. For example, smart engine 420 may select a particular machine learning model/classifier/algorithm and/or a particular accuracy and resource use requirement for the particular machine learning model/classifier/algorithm.);
a different level of computational complexity, a selected version of the Al model based, at least in part, upon the determination, execute at least one instance of an Artificial Intelligence (AI) the Al model of the selected version (Para [0112]: If it is determined that machine learning is to be performed (block 820—YES), a machine learning model may be selected based on the determined device status (block 830). Smart engine 420 may select a particular machine learning module 470 and/or one or more options for the particular machine learning module 470 based on the determined device status to match an expected resource use of the selected machine learning module 470 with the current resource capacity of the wireless communication device associated with the determined device status. For example, smart engine 420 may select a particular machine learning model/classifier/algorithm and/or a particular accuracy and resource use requirement for the particular machine learning model/classifier/algorithm.);
modify at runtime at least one parameter of the at least one instance of the AI model in execution (Para [0113]: smart engine 420 may select one or more data inputs for the selected machine learning module 470 based on the determined device status. As an example, if the device status indicates a low resource availability for UE device 110 (e.g., low battery level, high processor load, high memory use, etc.) smart engine 420 may select data inputs corresponding to dominant and non-resource-intense factors.);
(perform an action) of the at least one instance of the AI model in execution (Para [0112]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob with the teachings of HE to include determine a plurality of runtime resource utilization metrics based on context or telemetry data indicative of runtime resource utilization, wherein the plurality of runtime resource utilization metrics comprise at least a core utilization, a memory utilization, a network utilization, a battery utilization, and a peripheral device utilization; determine whether any of a plurality of runtime resource utilization metric threshold conditions are satisfied for the plurality of runtime resource utilization, and select, from among a plurality of versions of an Artificial Intelligence (AI) model each having metrics a different level of computational complexity, a selected version of the Al model based, at least in part, upon the determination, execute at least one instance of an Artificial Intelligence (AI) the Al model of the selected version; modify at runtime at least one parameter of the at least one instance of the AI model in execution; (perform an action) of the at least one instance of the AI model in execution in order to manage the machine learning to improve the device performance .
Andrews in view of Jacob in view of HE does not explicitly disclose independent of any host processor; (Perform an action) without any involvement by the IHS OS or any host processor.
Thornley teaches independent of any host processor (Para [0007]: out-of-band processing device is a processing device separate and independent from any in-band host processing device such as a central processing unit (CPU) that executes a host operating system (OS) of an information handling system, and without management of any application executing with a host OS on the host processing device.);
(Perform an action) without any involvement by the IHS OS or any host processor (Para [0007]: out-of-band processing device is a processing device separate and independent from any in-band host processing device such as a central processing unit (CPU) that executes a host operating system (OS) of an information handling system, and without management of any application executing with a host OS on the host processing device.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob in view of HE of system host processor with the teachings of Thornley to include the well-known technique of independent of any host processor; (Perform an action) without any involvement by the IHS OS or any host processor because the results would have been predictable and resulted in that the device can detect and correct faults fully on its own which provides host processor independence.
Andrews in view of Jacob in view of HE in view of Thornley does not explicitly disclose execute or instruct at least one device selected among the plurality of devices to execute an Artificial Intelligence (Al) model in the at least one selected device memory space (perform an action) based, at least in part, upon context or telemetry data input to the Al model, wherein the context or telemetry data is received from at least a subset of the plurality of devices, and wherein the context or telemetry data comprises data indicative of an intruder detection event comprising an unrecognized voice or face detected near a user by the Al model; while the at least one instance of the AI model is in execution on at least a first device among the plurality of devices.
CHAKRADHAR does disclose execute or instruct at least one device selected among the plurality of devices to execute an Artificial Intelligence (Al) model in the at least one selected device memory space (perform an action) based, at least in part, upon context or telemetry data input to the Al model, wherein the context or telemetry data is received from at least a subset of the plurality of devices, and wherein the context or telemetry data comprises data indicative of an intruder detection event comprising an unrecognized voice or face detected near a user by the Al model (Fig. 2. Para [0025]: the mediator chops up the data from webcams, microphones and lockdown browsers into equal or variable-sized fragments. Then, a series of AI and machine learning techniques are employed to alter personally identifiable information like the face of the test-taker/user, or the routine background of the test-taker/user using computer vision techniques. Such altering preserves the privacy of the test-taker/user, while not affecting the efficacy of proctoring. However, any unusual activity (like another person entering the field of view of the camera, or objects being moved into or out of the field of view, etc.) is retained in the video data.);
while the at least one instance of the AI model is in execution on at least a first device among the plurality of devices (Fig. 2. Para [0025]: the mediator chops up the data from webcams, microphones and lockdown browsers into equal or variable-sized fragments. Then, a series of AI and machine learning techniques are employed to alter personally identifiable information like the face of the test-taker/user, or the routine background of the test-taker/user using computer vision techniques. Such altering preserves the privacy of the test-taker/user, while not affecting the efficacy of proctoring. However, any unusual activity (like another person entering the field of view of the camera, or objects being moved into or out of the field of view, etc.) is retained in the video data.).
It would have been obvious to one having ordinary skill in the art to execute an Artificial Intelligence (AI) model usable to perform an action based on collected data because Andrews in view of Jacob in view of HE in view of Thornley and CHAKRADHAR are analogous art involving collecting and analyzing context information to perform an action based on the collected data. The motivation to combine would be to execute an AI model to perform a selection operation based on the received frame and field of view of the visible area captured by the camera at any given moment.
Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR does not explicitly disclose determine updated runtime resource utilization metrics from updated context or telemetry data and evaluate the updated runtime resource utilization metrics against the plurality of runtime resource utilization metric threshold conditions and, in response to a determination that at least one of: (a) a utilization metric for a high- performance AI device exceeds a first threshold, or (b) a battery utilization metric falls below a second threshold, and instruct migration of execution from the first device to a second device among the plurality of devices.
KELLY does disclose determine updated runtime resource utilization metrics from updated context or telemetry data and evaluate the updated runtime resource utilization metrics against the plurality of runtime resource utilization metric threshold conditions and, in response to a determination that at least one of: (a) a utilization metric for a high- performance AI device exceeds a first threshold, or (b) a battery utilization metric falls below a second threshold, and instruct migration of execution from the first device to a second device among the plurality of devices (Claim 1: determining, by a controller, based on a monitored resource utilization, a constraining resource of a selected server that is performing one of storage services and network services from among the one or more physical servers of the IHS, wherein the constraining resource is a resource from among available resources, including (i) processor capacity, (ii) memory capacity, (iii) network bandwidth, and (iv) storage input/output latency; assigning a Quality of Service (QoS) threshold to the selected server based on the constraining resource; assigning at least one Virtual Machine (VM) workload, from among compute workloads, to a VM, based on requirements of the VM for resource utilization; comparing resource utilization of the selected server to the QoS threshold during operation of the assigned VM workloads; determining whether the selected server fails to satisfy the QoS threshold a pre-defined number of times over a monitoring time interval; and in response to determining that the selected server has failed to satisfy the QoS threshold the predefined number of times over the monitoring interval, migrating at least one of the assigned VM workloads away from the selected server to another server of the IHS.).
It would have been obvious to one having ordinary skill in the art to select an AI from a plurality of AI models to execute an Artificial Intelligence (AI) model usable to perform an action based on collected data because Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR and KELLY are analogous art involving determine updated runtime resource utilization metrics from updated context or telemetry data and evaluate the updated runtime resource utilization metrics against the plurality of runtime resource utilization metric threshold conditions and, in response to a determination that at least one of: (a) a utilization metric for a high- performance AI device exceeds a first threshold, or (b) a battery utilization metric falls below a second threshold, and instruct migration of execution from the first device to a second device among the plurality of devices. The motivation to combine collect real-time data to ensure they are matched to throughput and accuracy demands of the assigned AI model.
Regarding claim 2, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the heterogeneous computing platform comprises: a System-On-Chip (SoC), a Field-Programmable Gate Array (FPGA), or an Application-Specific Integrated Circuit (ASIC) (Andrews Para [0032], The one or more processing devices 401 shown in FIG. 2 may be configured to execute program instructions stored within system memory 403 and/or computer readable storage medium 417. Examples of processing device(s) 401 include various types of programmable integrated circuits (e.g., a processor, a controller, microcontroller, microprocessor, ASIC, etc.) and programmable logic devices (such as a field programmable gate array “FPGA”.).)
Regarding claim 3, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the orchestrator comprises at least one of: a sensing hub, an Embedded Controller (EC), or a Baseboard Management Controller (BMC) (Andrews Para [0039], controller 410 includes an embedded controller (EC).).
Regarding claim 4, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the context or telemetry data further comprises data indicative of at least one of: a presence of a user, or a level of engagement of the user (Andrews Para [0058]— [0059], The management service may receive user access patterns in the telemetry data from the IHS (or from another source) indicating that the user is continuously trying to access a blocked resource. That information may be provided to the policy engine from an IT source or from the IHS. The attempted access to a blocked website or other resource may occur, e.g., because the user is working on a task involving a company associated with the blocked website or other resource. In 803 the policy engine determines whether the user should be entitled to the blocked resource. The decision is based on, e.g., job function, title, active directory group, etc. Thus, the decision to allow access depends on such factors as the user role within the organization and the security posture that needs to be maintained with respect to the access. The policy engine 201 (FIG. 2) receives data periodically from the IHS 203 (or about the IHS 203 from another source). The data includes, e.g., the user role within the organization. The user role may be developer, manager, a member of a particular department, or any of a large number of functions within a particular organization. In addition, the seniority of the person within the organization can be determinative of whether to grant access to the resource.).
Regarding claim 5, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the context or telemetry data further comprises data indicative of at least one of: a location of the IHS, or a network connection type selected from the group consisting of: home network, corporate network, public network, and private network (Andrews Para [0040], sensors 425 may be configured to detect a communication state of a network interface device 411 (e.g., detecting network connectivity, or connection to a particular network), a connection state of a network interface device 411 (e.g., detecting the presence of a USB drive attached to a USB port), a connection state of a I/O device 415 (e.g., detecting a connection to a printer, camera, microphone, etc.), a physical location of the IHS (e.g., detecting a GPS location of the HIS).).
Regarding claim 6, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the context or telemetry data further comprises data indicative of a chassis intrusion detection event (Andrews Para [0040], sensors 425 may be configured to detect a communication state of a network interface device 411 (e.g., detecting network connectivity, or connection to a particular network), a connection state of a network interface device 411 (e.g., detecting the presence of a USB drive attached to a USB port), a connection state of a I/O device 415 (e.g., detecting a connection to a printer, camera, microphone, etc.), a physical location of the IHS (e.g., detecting a GPS location of the IHS), a power or docking state of the IHS, and/or an intrusion into a chassis of the IHS. Para [0043], data collection module(s) 409 receives data from sensors 425 and controller 410. Para 0045], the agent or other component in the IHS causes the data collected by the data collection module(s) 409 to be sent as telemetry information to the policy engine on a periodic basis over network 205.).
Regarding claim 7, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein to receive the context or telemetry data, the orchestrator is configured to send a message to one or more firmware services executed by the subset of the plurality of devices to collect the context or telemetry data (Para [0031], the IHS further includes a data collection module 409 that collects and supplies telemetry information to the management system policy engine 201.) via one or more Application Programming Interfaces (APIs) without any involvement by any host Operating System (OS) (Jacob Para [0049]. Para [0052]: the machine learning framework 158 can include an API 162 and the machine learning model 128 can be executed using different machine learning frameworks 158 by accessing the respective API of such frameworks. Thornley [0007]).
Regarding claim 8, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches modify at runtime further comprises adjust at least one of model weights or biases to change model performance, power consumption, inference accuracy, and/or speed of execution to a model performance level based at least in part on a model type and a model version specified by a policy (Jacob Para [0049]. Para [0052]: the machine learning framework 158 can include an API 162 and the machine learning model 128 can be executed using different machine learning frameworks 158 by accessing the respective API of such frameworks.).
Regarding claim 10, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the one or more IHS settings comprise: a microphone setting, or a camera setting (Andrews Para [0029], policy rules may be used to control access to data stored within a computing resource of the IHS, and/or to control the decryption of encrypted data stored within the computing resource. Other policy rules may be used to control or manage logon security features (e.g., multifactor authentication, user password, etc.), control network access (e.g., to a secure or public Wi- Fi), and control access to local and/or network storage device. In other examples, policy rules may control access to input/output devices of the IHS (e.g., a camera or microphone) or control access to certain applications or websites.).
Regarding claim 12, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the one or more IHS settings comprise: a peripheral device setting, or a wireless adaptor setting (Andrews Para [0029]-[0030], the policies stored within policy rules may specify one or more actions that are taken by an IHS. Example actions include, but are not limited to, enabling/disabling I/O devices of the IHS (e.g., blocking phone calls or disabling a camera or microphone), enabling/disabling access to one or more local or remote computing resources of the IHS (e.g., enabling/blocking access to a network interface device, a computer readable memory or storage device, a processing device, etc.), enabling/disabling network access (e.g., blocking access to a secure or public Wi-Fi network).).
Regarding claim 13, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein to modify the one or more IHS settings, the orchestrator is configured to send a message to one or more firmware services executed by another device among the plurality of devices (Andrews Para [0025], A dynamically created policy may modify a current policy being applied to an IHS to try and reduce impediments to user productivity while maintaining appropriate security postures. Para [0027]-[0028], management service policy engine 201 generates a custom policy (or a set of policies) for respective ones of the IHSs in response to information received from or about the individual IHSs. Custom policy rules according to embodiments are applied to individual IHSs, or to groups of IHSs operating within the managed environment based on information supplied about the individual IHSs. Jacob Para [0049]. Para [0052]: the machine learning framework 158 can include an API 162 and the machine learning model 128 can be executed using different machine learning frameworks 158 by accessing the respective API of such frameworks.), while the at least one instance of the AI model is in execution, the operations performed by the orchestrator further comprise:
in response to a determination that at least one updated runtime resource utilization metric changed by at least a threshold value relative to a previously determined runtime resource utilization metric, select a different device, AI model, AI model version, and/or model parameter(s) (KELLY Claim 1: determining, by a controller, based on a monitored resource utilization, a constraining resource of a selected server that is performing one of storage services and network services from among the one or more physical servers of the IHS, wherein the constraining resource is a resource from among available resources, including (i) processor capacity, (ii) memory capacity, (iii) network bandwidth, and (iv) storage input/output latency; assigning a Quality of Service (QoS) threshold to the selected server based on the constraining resource; assigning at least one Virtual Machine (VM) workload, from among compute workloads, to a VM, based on requirements of the VM for resource utilization; comparing resource utilization of the selected server to the QoS threshold during operation of the assigned VM workloads; determining whether the selected server fails to satisfy the QoS threshold a pre-defined number of times over a monitoring time interval; and in response to determining that the selected server has failed to satisfy the QoS threshold the predefined number of times over the monitoring interval, migrating at least one of the assigned VM workloads away from the selected server to another server of the IHS.) to identify the one of the plurality of privacy modes and trigger the migration of execution of the at least one instance of the AI model in execution (Kelly Claim 1); and
in response to a determination that no updated runtime resource utilization metric changed by at least the threshold value relative to a previously determined runtime resource utilization metric, refrain from migration of execution of the at least one instance of the AI model in execution (KELLY Claim 1).
As per claim 19, the claim claiming a memory coupled to a heterogeneous computing platform essentially corresponding to claim 1 above, and they are rejected, at least for the same reasons.
As per claim 20, the claim claiming method essentially corresponding toclaim 1 above, and they are rejected, at least for the same reasons.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 20200244704 A1) in view of Jacob (US 20200379609 A1) in view of He (US 20190086988 A1) in view of Thornley (US 20190278651 A1) in view of CHAKRADHAR (US 20230067473 A1), and in view of KELLY (US 20170346760 A1), and in view of Castro (US 20200090662 A1).
Regarding claim 9, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1.
Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY does not explicitly disclose wherein the plurality of privacy modes comprises: an enhanced mode, a standard mode, and a safe mode.
Castro does disclose wherein the plurality of privacy modes comprises: an enhanced mode, a standard mode, and a safe mode (Para [0108]- [0111]).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY with the teachings of Castro to include wherein the plurality of privacy modes comprises: an enhanced mode, a standard mode, and a safe mode in order to add a customized level of security for each privacy mode.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 20200244704 A1) in view of Jacob (US 20200379609 A1) in view of He (US 20190086988 A1) in view of Thornley (US 20190278651 A1) in view of CHAKRADHAR (US 20230067473 A1), and in view of KELLY (US 20170346760 A1), and in view of Robertson (US 20090164528 A1).
Regarding claim 11, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1.
Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY does not explicitly disclose wherein the one or more IHS settings comprise: a display setting, or a watermark setting.
Robertson does disclose wherein the one or more IHS settings comprise: a display setting, or a watermark setting (Para [0017], the preferences 302c include settings on the customer IHS 302a such as, for example, display settings.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY and in view of Robertson of modify one or more IHS settings based on the identified privacy mode with the teachings of Varadaraj to include the well-known technique of wherein the one or more IHS settings comprise: a display setting, or a watermark settings because the results would have been predictable and resulted in adjust the HIS settings to add a privacy mode to the IHS.
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 20200244704 A1) in view of Jacob (US 20200379609 A1) in view of He (US 20190086988 A1) in view of Thornley (US 20190278651 A1) in view of CHAKRADHAR (US 20230067473 A1), and in view of KELLY (US 20170346760 A1), and in view of Kamal (US 20160162893 A1).
Regarding claim 14, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 1, wherein the orchestrator is configured to (Perform an action) (Andrews Para [0026]- [0027]).
Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY does not explicitly disclose receive a policy from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM).
Kamal does disclose receive a policy from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM) (Para [0028], note that appropriate CVM policies may be received by the device from the OEM and/or an open, on-device CVM controller (when the user activates the application).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY of collect the context data with the teachings of Kamal to include the well-known technique of receive a policy from an Information Technology Decision Maker (ITDM) or Original Equipment Manufacturer (OEM) because the results would have been predictable and resulted in receive some polices form Original Equipment Manufacturer (OEM).
Regarding claim 15, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY in view of Kamal teaches the IHS of claim 14, wherein the policy identifies at least one of: the context or telemetry data, the subset of the plurality of devices, the instructed device, the AI model, or the plurality of privacy modes, or the one or more IHS settings (Andrews Para [0025], The manage service 201 receives telemetry information from the IHSs 203, and based on information received from each of the IHSs, supplies dynamically created policies based on the information received from or about the IHSs. Para [0027]. Para [0031], the IHS further includes a data collection module 409 that collects and supplies telemetry information to the management system policy engine 201. As discussed further herein, the data collection module 409 interact with various portions of the operating system and other software and/or hardware in the IHS to obtain and provide relevant telemetry information to the management service policy 201.).
Regarding claim 16, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 15, wherein the policy comprises one or more rules that associate the identified privacy mode with the one or more IHS settings (Andrews Para [0025]. [0027]-[0031]: Para [0025]. Para [0027]-[0028]. Para [0038]. Para [0046]. Para [0052]. Para [0054]: a dynamically created policy may modify a current policy being applied to an IHS. Management service policy engine 201 generates a custom policy for respective ones of the IHSs in response to information received from or about the individual IHSs. The customized policy controls IHS privacy settings (e.g., control or manage logon security features (e.g., multifactor authentication, user password, etc.), control access (e.g., to a secure or public Wi-Fi, local and/or network storage device, to input/output devices of the IHS (e.g., a camera or microphone).).
Claims 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Andrews (US 20200244704 A1) in view of Jacob (US 20200379609 A1) in view of He (US 20190086988 A1) in view of Thornley (US 20190278651 A1) in view of CHAKRADHAR (US 20230067473 A1), and in view of KELLY (US 20170346760 A1), and in view of Kamal (US 20160162893 A1), and in view of VIDA (US 20160188145 A1).
Regarding claim 17, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 16, wherein at least one of the one or more rules associates at least one of: (a) the instructed device, or (b) the AI model with predetermined context or telemetry data (Andrews Para [0027], according to embodiments of the present disclosure, management service policy engine 201 generates a custom policy (or a set of policies) for respective ones of the IHSs in response to information received from or about the individual IHSs.).
Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY does not explicitly disclose wherein the orchestrator is further configured to enforce the at least one rule based, at least in part, upon a comparison between current context or telemetry data and the predetermined context or telemetry data.
VIDA does disclose wherein the orchestrator is further configured to enforce the at least one rule based, at least in part, upon a comparison between current context or telemetry data and the predetermined context or telemetry data (claims 6-8, the steps of varying the predetermined portion of the context rules and determining that the varied context rule at least one of meets the predetermined condition and is closer to the predetermined condition are replaced by a process comparing the current context data with previous context data stored within a memory, the previous context data relating to previous occurrences when none of the context rules met the predetermined condition, and establishing a new context rule based upon the current context data when the comparison of the current context data and the previous context data meets a predetermined criteria.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY of update the policy rules with the teachings of VIDA to include the well-known technique of wherein the orchestrator is further configured to enforce the at least one rule based, at least in part, upon a comparison between current context or telemetry data and the predetermined context or telemetry data because the results would have been predictable and resulted in establishing a new context rule based upon the current context data when the comparison of the current context data and the previous context data meets a predetermined criteria (VIDA claim 6-8).
Regarding claim 18, Andrews in view of Jacob in view of HE in view of Thornley in view of CHAKRADHAR in view of KELLY teaches the IHS of claim 17, wherein the orchestrator is configured to select at least one of: (a) another AI model, or (b) another device configured to execute the AI model or the other AI model based, at least in part, upon a change in the current context or telemetry data (VIDA claims 6-8).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JUDY BAZNA/ Examiner, Art Unit 2495
/FARID HOMAYOUNMEHR/ Supervisory Patent Examiner, Art Unit 2495
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