Detailed Office 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 .
Status of the Claims
Claims 1-19 and 21 are rejected under 35 U.S.C. 103
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Golubev et al. (U.S. Publication No. 2021/0081294 A1), hereinafter referred to as Golubev in view of Jreij et al. (U.S. Publication No. 2012/0158890), hereinafter referred to as Jreij.
With regards to Claim 1, Golubev teaches:
A method for managing a process performed by a host device, the method comprising:
obtaining, by a management controller of the host device, a screenshot of a graphical user interface displayed on a display of the host device, ([0057]; regarding, “display processed test results to the developer user 152 via interface 153… test results may include screen captures of the GUI…”);
the screenshot being obtained while the host device is performing the process, ([0054]; regarding, “obtaining… screenshots of the GUI of the application 133 before, during, and/or after interaction with the various GUI elements.”);
the process only providing information regarding a progress of the process via the graphical user interface, ([0043]; regarding, “test executor 314… configured to process screenshots… as part of an automated test to detect errors… with a target application 132.”)
and the process only being locally manageable using hardware resources of the host device, ([0028]; “End users 142 may access the functionally of target application 232 locally”);
and in a first instance of the identification where the process is not progressing as expected:
performing an action set to manage progression of the process. ([0060]; regarding, “a captured screenshot may be presented to a developer user 152…along with a visual augmentation that highlights a portion of the GUI that is associated with an error detected using the machine learning diagnostic model 604.”).
initiating, by the management controller and using the screenshot and while the hardware resources are unable to report an operating state of the hardware resources due to performance of the process identification of whether the process is progressing as expected for an instance of the process using a decision model; ([0044]; regarding, “screenshot analyzer module 318 may apply machine learning to process screenshots”; [0065]; regarding, “screenshot analyzer 318 to process any captured screenshots of the GUI of the target application 133 using one or more machine learning diagnostic models, for example, as described with respect to FIG. 6. In some embodiments, operation 812 may include sending an image (i.e., a screenshot) to screenshot analyzer 318 for processing and then receiving a diagnostic output based on the processing from the screenshot analyzer 318. As previously mentioned, the diagnostic output may include, for example, detected features, a diagnostic classification (e.g., error detected vs. no error detected), a reason for the classification (e.g., an analysis of detected features), as well as other information such as a confidence metric indicative of a level of confidence that the classification is accurate.”);
Golubev fails to explicitly disclose but Jreij teaches:
the management controller being an independently operating computing device that is hosted by the host device, ([0013]; regarding, “IHS 102 further includes a management controller 124, which may be a system-on-chip on a main circuit board (e.g., a baseboard, a motherboard, or any combination thereof), integrated onto another component such as chipset 106, a separate add-in card, or any combination thereof. Management controller 124 may be a baseboard management controller (BMC), an integrated Dell remote access controller (iDRAC), another out-of-band (OOB) controller, or any combination thereof”);
the host device being a data processing system, ([0012]; regarding, “FIG. 1 is a functional block diagram of an exemplary embodiment of a managed system 100 including an information handling system (IHS) 102.”);
the management controller being operably connected to the hardware resources via a side band channel that is distinguishable from other channels to the hardware resources that facilitate performance of the process, ([0025]; regarding, “the management controller 124 monitors the hardware components of IHS 102 by collecting data from a multitude of internal sensors.”; [0014]; regarding, “These interfaces physically connect management controller 124 to a number of data busses in IHS 102. Specifically, the MAC interface connects to a network controller side band interface (NC-SI) bus 126, thereby coupling management controller 124 to LOM 120… the management controller's KCS interface connects to a KCS interface bus 127, thereby coupling management controller 124 to chipset 106… While a particular management controller has been described, one of ordinary skill in the art will recognize that the management controller may include additional and/or different data interfaces without departing from the scope of the present disclosure. And one of ordinary skill in the art may also recognize that additional and/or different internal data buses may couple management controller 124 to other components in IHS 102.”);
and the management controller being operably connected to a remote management system via an out of band channel that bypasses the hardware resources; ([0013]; regarding, “Management controller 124 may be a baseboard management controller (BMC), an integrated Dell remote access controller (iDRAC), another out-of-band (OOB) controller, or any combination thereof.”; [0021]; regarding, “Management information passed between the operating system 150 and management controller 124 may also be available to system administrators using management consoles on management stations 134… management controller 124 may expose an MC management console 162 for remote administration”)
wherein the data processing system further comprises a single network module shared by both the hardware resources and the management controller, the single network module being separate from both the hardware resources and the management controller and being a sole component within the data processing system that provides network access to both the hardware resources and the management controller; (Fig. 1, [0012]; regarding, “IHS 102 further includes a LAN-on-motherboard (LOM) network device 120 coupled to chipset 106 via a Peripheral Component Interconnect Express (PCIe) connection 122. LOM 120 provides further network connectivity to IHS 102.”; [0014]; regarding, “the MAC interface connects to a network controller side band interface (NC-SI) bus 126, thereby coupling management controller 124 to LOM 120.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to have modified Golubev with Jreij. Doing so could save processor cycles and reduce system downtime (Jreij, [0028]).
With regards to Claim 2, Golubev in view of Jreij teaches the method of Claim 1 as referenced above Golubev in view of Jreij further teaches:
wherein the decision model is an inference model, the inference model being trained to generate inferences indicating an expected progression status of the process, (Golubev, [0044]; regarding, “test generator module 310 may apply machine learning when generating a test scenario to apply to a target application 132.” and [0045]; regarding, “the machine learning module 320 may facilitate the generation, training, deployment, management and/or evaluation of one or more machine learning models that are applied by the various components of automated testing platform 300.”);
the expected progression status indicating whether progression of the process is nominal, (Golubev, [0061]; regarding, “inputting screenshot 702 into the machine learning diagnostic model 604 may produce a first diagnostic output indicative that the target application is operating normally.”);
and a progress status of the process indicates how much of the process has been completed. (Golubev, [0044, 0112]; regarding, “As shown in FIG. 16, screen 1610 includes interactive elements 1612a-c corresponding to various test scenarios performed as part of an automated test of a target website. The interactive elements 1612a-c include information regarding the corresponding test scenario such as… a status of the test scenario, and duration of the test scenario.).
With regards to Claim 3, Golubev in view of Jreij teaches the method of Claim 2 as referenced above. Golubev in view of Jreij further teaches:
wherein initiating identification of whether the process is progressing comprises:
obtaining an inference for the process using: the inference model; and the screenshot, wherein the inference indicates whether the process is progressing as expected for the instance of the process using the decision model. (Golubev, [0044]; regarding, “the screenshot analyzer module 318 may apply machine learning to process screenshots of GUI of a target application 132 to detect errors or other issues with the target application 132.”).
With regards to Claim 7, Golubev in view of Jreij teaches the method of Claim 1 as referenced above. Golubev in view of Jreij further teaches:
identifying areas of interest in the screenshot; (Golubev, [0061]; regarding, “FIGS. 7A and 7B shows example screenshots 702a and 702b (respectively) of a target application GUI (specifically a web page) that illustrates how the visual presentation of the GUI can indicate errors or other issues in the target application.”);
segmenting the screenshot into segments to obtain screenshot segments; (Golubev, [0060]; regarding, “In some embodiments, the diagnostic output may include visualizations that indicate detected features indicative of the classification. For example, a captured screenshot may be presented to a developer user 152, via interface 153, along with a visual augmentation that highlights a portion of the GUI that is associated with an error detected using the machine learning diagnostic model 604.”);
and classifying the screenshot segments based on the areas of interest in the screenshot to obtain screenshot segment classifications corresponding to the screenshot segments. (Golubev, [0086]; regarding, “As previously discussed, the diagnostic output of a machine learning diagnostic model can include, for example, detected features, a diagnostic classification (e.g., error detected vs. no error detected), a reason for the classification (e.g., an analysis of detected features), as well as other information such as a confidence metric indicative of a level of confidence that the classification is accurate.”).
With regards to Claim 8, Golubev in view of Jreij teaches the method of Claim 7 as referenced above. Golubev in view of Jreij further teaches:
wherein each of the areas of interest in the screenshot define a group of pixels of the screenshot comprising informational content useable to infer the information regarding the progress of the process. (Golubev, [0088]; regarding, “For example, if the error is a broken button in the GUI (e.g., inoperable, functioning incorrectly, mislabeled, etc.), the visual output may include a screenshot of a page of the GUI that includes the button along with a visual augmentation such as a highlighted or otherwise emphasized border around the button, an arrow pointing to the button, etc. In some embodiments, the output may include information about the detected error such as a description of the error, an identifier associated with the interactive element causing the error, recommended solutions to fix the error, a link to the actual page in the target application that includes the error, etc.”).
With regards to Claim 9, Golubev in view of Jreij teaches the method of Claim 1 as referenced above. Golubev in view of Jreij further teaches:
wherein performing the action set comprises:
obtaining, using the identification, management actions for the management controller, (Golubev, [0026]; regarding, “…developer user 152 can utilize interface 153 presented at a developer user device 150, for example, to configure an automated test, initiate the automated test, and view results of the automated test.”);
the management actions being actions performable by the management controller to modify operation of the hardware resources; (Golubev, [0078]; regarding, “Further, the operations depicted in example process 1000 may be performed in a different order than is shown.”);
and performing, by the management controller, the management actions. (Golubev, [0039]; regarding, “the test manager may obtain a generated test scenario from storage module 308, identify tasks associated with the test scenario, assign the tasks to one or more test executors 314 to perform the automated test, and direct test results received from the test executors 314 to a test results generator for processing. ").
With regards to Claim 10, Golubev in view of Jreij teaches the method of Claim 1 as referenced above. Golubev in view of Jreij further teaches:
in a second instance of the identification where the process is progressing as expected:
providing a message via the management controller to an external device indicating the progression of the process. (Golubev, [0061]; regarding, “For example, inputting screenshot 702 into the machine learning diagnostic model 604 may produce a first diagnostic output indicative that the target application is operating normally.”; [0089]; regarding, output to developer user; and [0027]; regarding, “Developer users signing up for the automated testing services may access such services by connecting, for example, via network 110 to the automated testing platform 120.”);
Claims 11-13 and 16-18 are rejected under 35 U.S.C 103 under the same grounds of rejection as claims 1-3.
Claims 4-6, 14-15, and 19 are rejected under 35 U.S.C 103 as being unpatentable over Golubev et al. (U.S. Publication No. 2021/0081294 A1), hereinafter referred to as Golubev in view of Jreij et al. (U.S. Publication No. 2012/0158890), hereinafter referred to as Jreij, in further view of Arora et al. (U.S. Publication No. 2024/0037368 A1), hereinafter referred to as Arora.
With regards, to Claim 4, Golubev in view of Jreij teaches the method of Claim 3 as referenced above. Golubev in view of Jreij further teaches:
wherein initiating identification of whether the process is progressing further comprises:
obtaining telemetry data for the hardware resources via the side band channel, and the characteristics of the hardware resources comprising health information and temperature information, (Jreij, [0025]; regarding, “the management controller 124 monitors the hardware components of IHS 102 by collecting data from a multitude of internal sensors.”; [0014]; regarding, “These interfaces physically connect management controller 124 to a number of data busses in IHS 102. Specifically, the MAC interface connects to a network controller side band interface (NC-SI) bus 126, thereby coupling management controller 124 to LOM 120… the management controller's KCS interface connects to a KCS interface bus 127, thereby coupling management controller 124 to chipset 106… While a particular management controller has been described, one of ordinary skill in the art will recognize that the management controller may include additional and/or different data interfaces without departing from the scope of the present disclosure. And one of ordinary skill in the art may also recognize that additional and/or different internal data buses may couple management controller 124 to other components in IHS 102.”; [0017]; regarding, “A core function of management controller 124 is the collection of data from a variety of sensors within chassis 130. For example, management controller 124 may collect hardware operational data such as temperature, fan speed, current, voltage, memory status”);
Golubev in view of Jreij fails to explicitly disclose but Arora teaches:
wherein initiating identification of whether the process is progressing further comprises: …
the telemetry data comprising measurements of characteristics of the hardware resources while the host device is performing the process, ([0099]; regarding, “the telemetry can indicate information about the state of the display device, such as amount of uptime, software applications running and the versions of those applications, sensor data indicating characteristics of an environment of the display device, or other context information. In addition, The telemetry can include state data for the device, such as log entries, error codes or error messages that were issued, and so on. In some cases, the telemetry maybe used by the computer system 110 to determine a state of the display device or a classification representing the ground truth state of the display device, which may then be used as a label for training.”; [0072]; regarding, “When display devices 130a-130b provide their screenshot images 202, they can also provide other information 203 indicating their status at the time the screenshot was captured.”);
wherein the inference is also obtained using the telemetry data. ([0080]; regarding, “additional information can be provided as input to the machine learning model 111 to increase accuracy. For example, one or more elements of the device information 203 can be provided as input…”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to have modified Golubev and Jreij with Arora. Doing so would allow for better predicting a classification represented by the screenshot (Arora, [0080]).
With regards, to Claim 5, Golubev in view of Jreij in further view of Arora teaches the method of Claim 4 as referenced above. Golubev in view of Jreij in further view of Arora further teaches:
wherein initiating identification of whether the process is progressing further comprises:
obtaining hardware data for the hardware resources, (Arora, [0072]; regarding, “The additional device information 203 can include context information, status information, telemetry, and so on.”);
the hardware data specifying the hardware resources that are contributing to performance of the process, (Arora, [0072]; regarding, “For example, device information 203 can include an identifier for a particular device to uniquely identify that device, and identifier for the organization or network associated with the display device, a current or recent amount of CPU processing utilization, an amount of available memory, an indication whether in error state is detected, a software version or firmware version executing on the display device, an indication of hardware capabilities of the device…”);
wherein the inference is also obtained using the hardware data. (Arora, [0080]; regarding, “additional information can be provided as input to the machine learning model 111 to increase accuracy. For example, one or more elements of the device information 203 can be provided as input…”).
With regards, to Claim 6, Golubev in view of Jreij in further view of Arora teaches the method of Claim 5 as referenced above. Golubev in view of Jreij in further view of Arora further teaches:
wherein obtaining the inference comprises:
ingesting the screenshot, the telemetry data, and the hardware data into the inference model, the inference model generating the inference based on the screenshot, the telemetry data, and the hardware data. (Arora, [0080]).
Claims 14-15 and 19 are rejected under 35 U.S.C. 103 under the same grounds of rejection as claims 5-6 respectively.
Claim 21 is rejected under 35 U.S.C 103 as being unpatentable over Golubev et al. (U.S. Publication No. 2021/0081294 A1), hereinafter referred to as Golubev in view of Jreij et al. (U.S. Publication No. 2012/0158890 A1), hereinafter referred to as Jreij, in further view of Shah et al. (U.S. Publication No. 2010/0192218 A1), hereinafter referred to as Shah.
With regards to Claim 21, Golubev in view of Jreij teaches the method of Claim 16 as referenced above Golubev in view of Jreij fails to explicitly disclose but Shah teaches:
wherein the single network module is configured to direct network traffic of the management controller such that inbound network traffic directed to the management controller never flows through the hardware resources and outbound network traffic from the management controller never flows through the hardware resources. ([0016]; regarding, “Communication between the network controller and the management controller may be performed based on the Network Controller Sideband Interface (NC-SI) protocol... The network controller may be operable to utilize, via a plurality of filters for example, packet filtering of packets received via the network controller during the pass-through routing… The packet filtering may be performed on inbound packets received from external network links supported via the network controller, on outbound packets communicated via the management controller, and/or on outbound packets communicated via the local host… The packet filters in the network controller may be configured to perform packet filtering of received packets based on filtering rules. The filtering rules may specify processing and/or forwarding actions by the network controller based on one or more specified conditions.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to have modified Golubev and Jreij with Shah. Doing so could allow for improved security, reliability, and OS-independence (Shah, [0027]).
Response to Arguments
Applicant’s arguments, filed 01/16/2026, have been fully considered.
Applicant’s arguments with respect to claim(s) 1, 11, and 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Further, applicant argues that Golubev fails to teach the claimed management controller. Examiner agrees, Golubev fails to explicitly disclose the claimed management controller, however, Golubev in view of Jreij disclose the claimed management controller.
Conclusion
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/M.D.G./Examiner, Art Unit 2113
/BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113