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 .
This Office Action is in response to Request for Continued Examination filed on December 29, 2025.
Claims 1-2, 4-9, 11-16 and 18-22 are pending.
Claims 1, 4-8, 11-16 and 18-20 have been amended.
Claim 17 has been canceled.
Response to Amendment
Claim Objections
Claims 15-16 and 18-20 are objected to because of the following informalities:
Claim 15 uses the abbreviation “ML” in line 2. It is recommended that at the first instance of the abbreviation that it be spelled out such as “machine learning (ML)”.
Claims 16 and 18-20 depend on the objected to claim and do not resolve the deficiencies and thus, are objected to for at least the same reasons.
Appropriate correction is required.
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 1-2, 4-9, 11-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over John Cable (“AI Powers Windows 10 April 2018 Update Rollout”, Jun 14, 2018) in view of Wei et al. (US 2009/0070756).
With respect to Claim 1, John Cable discloses:
obtaining, by at least one processing device, input data comprising a set of parameters associated with a computing environment; (retrieving characteristics of devices (set of parameters) to help identify target device (computing environment), Page 1-2, lines 1-19 and 1-8 respectively)
determining, by the at least one processing device using at least one machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, (Our AI approach (at least one ML model) intelligently selects devices (due for an upgrade) that our feedback data indicate would have a great update experience and offers the April 2018 Update to these devices first, Page 1, lines 4-7) wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device; (2018 Update (version 1803) is fully available for all compatible devices (device type) wherein the update includes adjusting and preventing devices (device type) that are affected by an issue, throttle the update rollout and continuing again once issues have been resolved (sequence of OS upgrade steps), Page 2, AI means both safe AND fast, lines 6-10 and Page 4, Windows 10 April 2018 Update (1803) is now fully available, lines 1-6; OS upgrade can include work arounds and fixes that prevent issues that arise during an OS upgrade (sequence of OS upgrade steps customized for a device type), Pages 2-3, AI means both safe AND fast, lines 11-18 and 1-4 respectively)
in response to determining that the device is due for the OS upgrade, initiating, by the at least one processing device, the OS upgrade for the device; (using AI models to select devices that would have a great update experience (due for an OS upgrade) and offering/rolling out the update, Page 1, lines 4-12)
determining, by the at least one processing device, whether to continue the OS upgrade for the device, wherein determining whether to continue the OS upgrade for the device comprises determining, using the at least one ML model, [an issue]. (When our AI model (at least one ML model), feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices from being offered the update until we thoroughly investigate (determine whether to continue). Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
and completing, by the at least one processing device, the OS upgrade for the device. (completing rolling out of the update and collecting update experience data to retrain the AI models, Page 1, lines 7-10)
John Cable does not disclose:
[an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device;
However, Wei et al. disclose:
[an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device; (determining whether resources are available (resource consumption satisfies a threshold condition associated with a normal resource consumption pattern) to continue an update process and if not, suspend a software update until resources are available, Paragraph 64; resources may not be available due to network congestion, large pending document processing jobs, errors experienced by the document processing device or the like (normal/abnormal resource consumption pattern), Paragraph 64)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wei et al. into the teaching of John Cable to include [an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device in order to be able to throttle software updates based on resource utilization which can help complete software updates in a way that minimizes competition with other resources associated with device operation. (Wei et al., Abstract and Paragraph 1, lines 1-2 and 2-5 respectively)
With respect to Claim 2, all the limitations of Claim 1 have been addressed above; and John Cable further disclose:
wherein determining whether the device is due for the OS upgrade further comprises determining whether an upgrade flag for the device is set. (Based on the update quality and reliability we are seeing through our AI approach, we are now expanding the release broadly to make the April 2018 Update (version 1803) fully available for all compatible devices running Windows 10 worldwide. Full availability is the final phase of our rollout process. You don’t have to do anything to get the update; it will rollout automatically (upgrade flag for the device is set) to you through Windows Update, Page 4, Windows 10 April 2018 (1803) is now fully available, lines 1-6)
With respect to Claim 4, all the limitations of Claim 1 have been addressed above; and John Cable further disclose:
selecting, by the at least one processing device, an approved OS for the device; (using AI to select devices that would have a great update experience and offering the April 2018 update to these devices (an approved OS), Page 1, lines 4-7)
and performing, by the at least one processing device, a staging of the approved OS to obtain a staged OS; (commencing rollout of the update (staged OS), Page 1, lines 4-10)
wherein determining whether to continue the OS upgrade for the device further comprises determining whether to continue the OS upgrade for the device with the staged OS. (When our AI model, feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices from being offered the update (determine whether to continue the OS upgrade/staged OS) until we thoroughly investigate. Once issues are resolved we proceed again with confidence (continue with the OS upgrade when there are no longer any issues). This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
With respect to Claim 5, all the limitations of Claim 4 have been addressed above; and John Cable further disclose:
wherein determining whether to continue the OS upgrade for the device further comprises executing a precheck process based on the staged OS. (When our AI model, feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices from being offered the update (precheck process) until we thoroughly investigate. Once issues are resolved we proceed again with confidence (continue with the OS upgrade when there are no longer any issues). This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
With respect to Claim 6, all the limitations of Claim 4 have been addressed above; and John Cable further disclose:
wherein completing the OS upgrade for the device further comprises:
reloading the device with the staged OS; (once issues are resolved, we proceed again (reloading the device) with the update with confidence, Page 2, AI means both safe AND fast, lines 6-10; continue safe rollout of the April 2018 update to devices using a fix/updated update (reloading the device with the staged OS), Pages 2-3, AI means both safe AND fast, lines 11-18 and 1-4 respectively)
and executing a post-check process to determine whether an issue with the OS upgrade for the device exists. (As our rollout progresses, we continuously collect update experience data (post-check process) and retrain our models to learn which devices will have a positive experience and where we may need to wait until we have higher confidence in a great experience (determine whether an issue with the OS upgrade for the device exists), Page 1, lines 7-10)
With respect to Claim 7, all the limitations of Claim 1 have been addressed above; and John Cable further disclose:
further comprising using, by the at least one processing device, the at least one ML model to make a prediction associated with [an issue]. (When our AI model, feedback or telemetry data indicate that there may be an issue (prediction associated with computing environment), we quickly adjust and prevent affected devices from being offered the update until we thoroughly investigate. Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
John Cable does not disclose:
[an issue] includes computing environment resource consumption
However, Wei et al. disclose:
[an issue] includes computing environment resource consumption (determining whether resources are available (computing environment resource consumption) to continue an update process and if not, suspend a software update until resources are available, Paragraph 64; resources may not be available due to network congestion, large pending document processing jobs, errors experienced by the document processing device or the like (computing environment resource consumption), Paragraph 64)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wei et al. into the teaching of John Cable to include [an issue] includes computing environment resource consumption in order to be able to throttle software updates based on resource utilization which can help complete software updates in a way that minimizes competition with other resources associated with device operation. (Wei et al., Abstract and Paragraph 1, lines 1-2 and 2-5 respectively)
Claims 8-9 and 11-14 are system claims corresponding to the method claims above (Claims 1-2 and 4-7) and, therefore, are rejected for the same reasons set forth in the rejections of Claims 1-2 and 4-7.
With respect to Claim 15, John Cable discloses:
obtaining, by a processing device, input data for training at least one ML model to
manage an operating system (OS) upgrade for a device of a computing environment, (continuously collect update experience data (input data) and retrain our models to learn which devices will have a positive update experience and whether we may need to wait until we have higher confidence in a great experience (manage an OS upgrade for a device of a computing environment), Page 1, lines 4-10) wherein the input data comprises a set of training parameters, (update experience data (set of training parameters), Page 1, lines 7-8) and wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device; (2018 Update (version 1803) is fully available for all compatible devices (device type) wherein the update includes adjusting and preventing devices (device type) that are affected by an issue, throttle the update rollout and continuing again once issues have been resolved (sequence of OS upgrade steps), Page 2, AI means both safe AND fast, lines 6-10 and Page 4, Windows 10 April 2018 Update (1803) is now fully available, lines 1-6; OS upgrade can include work arounds and fixes that prevent issues that arise during an OS upgrade (sequence of OS upgrade steps customized for a device type), Pages 2-3, AI means both safe AND fast, lines 11-18 and 1-4 respectively)
and training, by the processing device based on the input data, the at least one ML model to manage the OS upgrade for the device, (continuously collect update experience data (input data) and retrain our models to learn which devices will have a positive update experience and whether we may need to wait until we have higher confidence in a great experience (manage an OS upgrade for a device of a computing environment), Page 1, lines 4-10) wherein training the at least one ML model to manage the OS upgrade for the device comprises training the ML model to determine whether to continue the OS upgrade for the device, (When our AI model, feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices from being offered the update (determine whether to continue the OS upgrade/mange the OS upgrade) until we thoroughly investigate. Once issues are resolved we proceed again with confidence (continue with the OS upgrade when there are no longer any issues). This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10) and wherein training the ML model to determine whether to continue the OS upgrade for the device comprises training the ML model to determine, using the at least one ML model, [an issue]. (When our AI model (ML model), feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices from being offered the update until we thoroughly investigate (determine whether to continue). Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
John Cable does not disclose:
[an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device;
However, Wei et al. disclose:
[an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device; (determining whether resources are available (resource consumption satisfies a threshold condition associated with a normal resource consumption pattern) to continue an update process and if not, suspend a software update until resources are available, Paragraph 64; resources may not be available due to network congestion, large pending document processing jobs, errors experienced by the document processing device or the like (normal/abnormal resource consumption pattern), Paragraph 64)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wei et al. into the teaching of John Cable to include [an issue] includes whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device in order to be able to throttle software updates based on resource utilization which can help complete software updates in a way that minimizes competition with other resources associated with device operation. (Wei et al., Abstract and Paragraph 1, lines 1-2 and 2-5 respectively)
With respect to Claim 16, all the limitations of Claim 15 have been addressed above; and John Cable further disclose:
wherein training the ML model the at least one ML model to manage the OS upgrade further comprises training the ML model to make a prediction associated with [an issue]. (When our AI model (trained ML model), feedback or telemetry data indicate that there may be an issue (prediction associated with computing environment), we quickly adjust and prevent affected devices from being offered the update until we thoroughly investigate. Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
John Cable does not disclose:
[an issue] includes computing environment resource consumption
However, Wei et al. disclose:
[an issue] includes computing environment resource consumption (determining whether resources are available (computing environment resource consumption) to continue an update process and if not, suspend a software update until resources are available, Paragraph 64; resources may not be available due to network congestion, large pending document processing jobs, errors experienced by the document processing device or the like (computing environment resource consumption), Paragraph 64)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Wei et al. into the teaching of John Cable to include [an issue] includes computing environment resource consumption in order to be able to throttle software updates based on resource utilization which can help complete software updates in a way that minimizes competition with other resources associated with device operation. (Wei et al., Abstract and Paragraph 1, lines 1-2 and 2-5 respectively)
With respect to Claim 18, all the limitations of Claim 15 have been addressed above; and John Cable further disclose:
wherein training the ML model to manage the OS upgrade for the device further comprises training the ML model to cause the OS upgrade for the device to be halted in response to determining to discontinue the OS upgrade for the device. (When our AI model (trained ML model), feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices (halt the OS upgrade) from being offered the update until we thoroughly investigate (discontinue the OS upgrade). Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10; in cases where devices already offered the update may see issues, we immediately block all PCs that could be impacted by the issue from being updated (halt the OS upgrade/discontinue the OS upgrade), Page 2, AI means both safe AND fast, lines 11-16))
With respect to Claim 19, all the limitations of Claim 15 have been addressed above; and John Cable further disclose:
wherein training the ML model to manage the OS upgrade for the device further comprises training the ML model to determine whether an issue with the OS upgrade for the device exists. (When our AI model (trained ML model), feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices (determine whether an issue with the OS upgrade exists) from being offered the update until we thoroughly investigate. Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10)
With respect to Claim 20, all the limitations of Claim 19 have been addressed above; and John Cable further disclose:
wherein training the ML model to manage the OS upgrade for the device further comprises training the ML model to cause the OS upgrade for the device to be halted in response to determining that an issue with the OS upgrade for the device exists. (When our AI model (trained ML model), feedback or telemetry data indicate that there may be an issue, we quickly adjust and prevent affected devices (halt OS upgrade) from being offered the update until we thoroughly investigate. Once issues are resolved we proceed again with confidence. This allows us to throttle the update rollout to customers without them needing to take any action., Page 2, AI means both safe AND fast, lines 6-10; in cases where devices already offered the update may see issues, we immediately block all PCs that could be impacted by the issue from being updated (halt the OS upgrade), Page 2, AI means both safe AND fast, lines 11-16)
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over John Cable (“AI Powers Windows 10 April 2018 Update Rollout”, Jun 14, 2018) in view of Wei et al. (US 2009/0070756) and in further view of Martin Brinkmann ("Portable Update: Search, Download and Install All (Missing) Windows Update”, Jun 2, 2013).
With respect to Claim 21, all the limitations of Claim 1 have been addressed above; and John Cable and Wei et al. do not disclose:
wherein determining whether the device is due for the OS upgrade using the at least one ML model comprises determining whether a deviation from an OS upgrade history for the device exists.
However, Martin Brinkmann discloses:
wherein determining whether the device is due for the OS upgrade using the at least one ML model comprises determining whether a deviation from an OS upgrade history for the device exists. (search Microsoft’s update repository (OS upgrade history) and determine any missing updates (deviation) and download/install those only on the PC in question, Page 1, lines 10-13)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Martin Brinkmann into the teaching of John Cable and Wei et al. to include determining whether a deviation from an OS upgrade history for the device exists in order to determine, download and install any missing updates on specific computers. (Martin Brinkmann, Page 1, lines 10-13)
Claim 22 is a system claim corresponding to the method claim above (Claim 21) and, therefore, is rejected for the same reasons set forth in the rejection of Claim 21.
Response to Arguments
Applicant's arguments filed December 5, 2025 have been fully considered but they are not persuasive.
In the Remarks, Applicant argues:
Anticipation of a claim requires the presence, in a single prior art reference, of disclosure of each element of the claim. Applicant respectfully submits that Cable does not expressly or inherently disclose every element of amended claim 1.
For example, claim 1 recites, inter alia, "determining, by the at least one processing device using at least one machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device."
In rejecting previously presented claim 1, the Office Action at page 3 alleges: "Further, Windows updates are designed specifically for the given device being updated, thus an OS upgrade will comprise a sequence of steps customized for each device and/or device type."
However, even assuming, arguendo, that Cable generally teaches the concept of determining, using at least one machine learning (ML) model, whether a device of a computing environment is due for an operating system (OS) upgrade, Cable does not expressly disclose that its update comprises a sequence of OS upgrade steps customized for a device type of the device.
During the interview on November 6, 2025, the Examiner clarified that his position is that Cable inherently teaches that an OS upgrade comprises a sequence of OS upgrade steps customized for each device and/or device type. Therefore, the Examiner acknowledges that Cable does not expressly disclose that its update comprises a sequence of OS upgrade steps customized for a device type of the device.
To establish inherency, there must be factual support and reasoning to show that the missing element is necessarily present. Applicant notes that it may be possible for an OS upgrade to be designed to be uniform for at least two device types to ensure consistency, reliability, and ease of support. Such an OS upgrade would not be customized for a device type.
Thus, it is not inherent for an OS upgrade to comprise a sequence of OS upgrade steps customized for a device type of a device, as recited in amended claim 1. In the event that the amendments and remarks made herein do not place the application in condition for allowance,
Applicant respectfully requests that any subsequent Office Action provide evidentiary support for the assertion that the update of Cable must comprise a sequence of steps customized for a device type of a device.
During the interview on November 6, 2025, the Examiner also confirmed that he did not take Official Notice that an OS upgrade comprises a sequence of OS upgrade steps customized for each device and/or device type. However, to the extent that there is an implication of Office
Notice, Applicant respectfully traverses the taking of such Official Notice. Official Notice is only appropriate for facts that are capable of instant and unquestionable demonstration as well- known. For at least the reasons described above, a proposition that an OS upgrade must involve a sequence of OS upgrade steps customized for a device type of a device is not such a fact.
In summary, Applicant respectfully submits that an OS upgrade for a device does not inherently comprise a sequence of steps customized for a device type of the device. Therefore, Cable does not teach or suggest, at least, "determining, by the at least one processing device using at least one machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device," as recited in amended claim 1.
Examiner’s Response:
The Examiner respectfully disagrees. As can be seen in the updated §103 rejection above, it is the Examiner’s position that Cable discloses the Applicant’s claim language of "determining, by the at least one processing device using at least one machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device”. Specifically, Cable discloses using AI models to selectively determine devices that would have a great update experience based on experience data and characteristics of the devices (see Page 1-2, lines 1-19 and 1-8 respectively). The April 2018 update includes operations (steps) adjusting and prevent specific devices (device type) from being offered the update. This can reasonably be considered “a sequence of OS upgrade steps customized for a device type of the device” (see Page 2, AI means both safe AND fast, lines 6-10). The claims do not provide any additional detail on the sequence of steps. The OS upgrade includes a check to determine if it should or should not be offered to a specific device (device type) before proceeding or halting the OS upgrade (sequence of upgrade steps). Further, Cable discloses that if the update has already been offered, the upgrade can include work arounds and/or fixes to prevent the issue from arising again during the continuation of the update. These fixes/work arounds could also be considered “a sequence of OS upgrade steps” which are ”customized” for devices where an issue was identified (device type) (see Pages 2-3, AI means both safe AND fast, lines 11-18 and 1-4 respectively)
In the Remarks, Applicant argues:
As another example, amended claim 1 further recites, inter alia, "determining, by the at least one processing device, whether to continue the OS upgrade for the device, wherein determining whether to continue the OS upgrade for the device comprises determining, using the at least one ML model, whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device."
Cable describes, in part:
Artificial Intelligence (AI) continues to be a key area of investment for Microsoft,
and we're pleased to announce that for the first time we've leveraged AI at scale
to greatly improve the quality and reliability of the Windows 10 April 2018
Update rollout. Our AI approach intelligently selects devices that our feedback
data indicate would have a great update experience and offers the April 2018
Update to these devices first. As our rollout progresses, we continuously collect
update experience data and retrain our models to learn which devices will have a
positive update experience, and where we may need to wait until we have higher
confidence in a great experience. Our overall rollout objective is for a safe and
reliable update, which means we only go as fast as is safe.
Our AI/Machine Learning approach started with a pilot program during the
Windows 10 Fall Creators Update rollout. We studied characteristics of devices
that data indicated had a great update experience and trained our model to spot
and target those devices. In our limited trial during the Fall Creators Update
rollout, we consistently saw a higher rate of positive update experiences for
devices identified using the AI model, with fewer rollbacks, uninstalls, reliability
issues, and negative user feedback. For the April 2018 Update rollout, we
substantially expanded the scale of AI by developing a robust AI machine
learning model to teach the system how to identify the best target devices based
on our extensive listening systems.
When our AI model, feedback or telemetry data indicate that there may be an
issue, we quickly adjust and prevent affected devices from being offered the
update until we thoroughly investigate. Once issues are resolved we proceed
again with confidence. This allows us to throttle the update rollout to customers
without them needing to take any action.
Even if, arguendo, a device update described by Cable is analogous to an OS upgrade for a device, Cable describes collecting update experience data and using feedback and telemetry to identify devices likely to have a good or successful update experience, and blocking updates to devices with known issues until they are resolved, before initiating the update. Nowhere does Cable describe collecting any sort of data or identifying issues during an update for a device in order to determine whether to continue the update for the device. Therefore, Cable does not teach or suggest, at least, "wherein determining whether to continue the OS upgrade for the device comprises determining, using the at least one ML model, whether a resource consumption of the
device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device," as recited in amended claim 1.
Examiner’s Response:
As can be seen in the updated §103 rejection to claim 1 above, the Examiner has not relied solely on Cable to disclose “wherein determining whether to continue the OS upgrade for the device comprises determining, using the at least one ML model, whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device”. The Examiner has used newly cited prior art Wei along with Cable to disclose the this limitation. Therefore, Applicant’s arguments 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 above.
Applicant argues that Cable does not “collecting any sort of data or identifying issues during an update for a device in order to determine whether to continue the update for the device.” The Examiner respectfully disagrees. Cable discloses that “in cases where devices already offered the update may see issues… [w]e immediate block all PCs that could be impacted by this issue from being updated…” This disclosure can be reasonably interpreted as “identifying issues during an update for a device in order to determine whether to continue the update for the device”.
In the Remarks, Applicant argues:
In view of the above, Cable does not teach or suggest, at least, "determining, by the at least one processing device using at least one machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, wherein the OS upgrade for the device comprises a sequence of OS upgrade steps customized for a device type of the device" and/or "determining, by the at least one processing device, whether to continue the OS upgrade for the device, wherein determining whether to continue the OS upgrade for the device comprises determining, using the at least one ML model, whether a resource consumption of the device during the OS upgrade satisfies a threshold condition associated with a normal resource consumption pattern of the device," as recited in
amended claim 1. For at least these reasons, Cable does not disclose every feature of claim 1. Similar elements are present in independent claims 8 and 15. Therefore, Cable does not anticipate claims 1, 8, and 15, or any claims dependent therefrom. Accordingly, Applicant respectfully requests that the rejections of the claims under 35 U.S.C. § 102(a)(1) be withdrawn.
Examiner’s Response:
The Examiner respectfully disagrees. Please see response to arguments above with respect to claim 1 and newly cited prior art Wei.
In the Remarks, Applicant argues:
Claims 21 and 22 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Cable in view of Brinkmann. As discussed above, Cable fails to teach or suggest all of the features of claims 1, 8, and 15. Brinkmann fails to cure at least these deficiencies of claims 1, 8 and 15. Therefore, Applicant respectfully submits that claims 21 and 22 are patentable over the combination of Cable and Brinkmann at least by virtue of their respective dependencies from claims 1 and 8. Accordingly, Applicant requests that the rejections of claims 21 and 22 under 35 U.S.C. § 103 be withdrawn.
Examiner’s Response:
The Examiner respectfully disagrees. Please see response to arguments above with respect to claims 1, 8 and 15.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LANNY N UNG whose telephone number is (571)270-7708. The examiner can normally be reached Mon-Thurs 6am-4pm.
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/LANNY N UNG/ Examiner, Art Unit 2197