Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
Office Action is in response to the reply filed by Applicant on 5/7/2026. Claims 6 and 18 have been cancelled. Claims 1-5, 7-17, 19 and 20 are pending. This Office Action is Final.
Response to Arguments
A) Applicant’s arguments with respect to claim(s) 1, 13 and 20 have been considered but are moot because the new ground of rejection does not rely on the same combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 4, 5, 7-11, 13, 14, 16, 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752) in view of Featonby et al. (US 2020/0310852) and Malkov et al. (US 10,701,104).
As per claim 1, Cao teaches a method, comprising: monitoring an automotive computing environment executing a plurality of virtual machines (Cao, Paragraph 0008 recites “monitoring, by a computing device, performance of currently deployed virtual machines (VMs) that implement particular services;” And Paragraph 0057 recites “Referring now to FIG. 2, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate”);
responsive to a request to instantiate a new virtual machine (Cao, Paragraph 0107 recites “As shown in FIG. 6, process 600 may include receiving request for VM deployment (step 610).”),
monitoring communications to and from a virtual machine of the plurality of virtual machines, the monitored communications including a first communication (Cao, Paragraph 0110 recites “Process 600 may also include monitoring the performance and activity of the VM (step 660). For example, the optimal configuration and catalog component 96 may monitor the performance and the activity of the VM (e.g., as described above with respect to the performance and usage monitoring module 520).”).
But fails to teach inspecting a configuration file of the new virtual machine;
responsive to a configuration parameter of the configuration file violating a first predefined safety rule, modifying the configuration parameter.
However, in an analogous art Featonby teaches inspecting a configuration file of the new virtual machine; responsive to a configuration parameter of the configuration file violating a first predefined safety rule, modifying the configuration parameter (Featonby, Paragraph 0045 recites “In some examples, prior to recommending or automating the migration of workloads to new VM instance types, or the modification of configuration parameters, the optimization service may test the recommended changes on one or more “test” VM instances. That is, the optimization service may designate, or spin up, a VM instance type that is determined to be more optimized for a workload than a current VM instance type. The optimization service may then cause a workload to be hosted or supported by the proposed VM instance type and monitor the health or performance of the workload. If the workload does in fact perform better on the proposed VM instance type compared to the current VM instance type, then the optimization service may move forward with providing a recommendation to a user account, and/or automating the migration of the workloads for the user to the new VM instance type.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Featonby’s Compute Platform Recommendations For New Workloads In A Distributed Computing Environment with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of improving the performance of workloads by intelligently placing workloads on VM instance types that are computationally biases or optimized to support the workloads.
And fails to teach responsive to the first communication violating a second predefined safety rule, intercepting the first communication and modifying a content of the first communication by removing data from the first communication; and
transmitting a modified version of the first communication with the data
removed to or from the virtual machine of the plurality of virtual machines.
However, in an analogous Malkov teaches responsive to the first communication violating a second predefined safety rule, intercepting the first communication and modifying a content of the first communication by removing data from the first communication; and transmitting a modified version of the first communication with the data removed to or from the virtual machine of the plurality of virtual machines (Malkov, Fig. 2 Col. 8 Line 24 – Col. 10 Line 20, “Step 201, “Intercept Next Data Packet,” may be the first step, or function, in providing such an agentless, virtual firewall.” “In Step 203, “Obtain the Relevant Rules,” the virtual switch filtering extension, having determined the intended recipient virtual digital processing machine for the digital data packet, may consult a table, or rules database, containing predefined rules, to obtain the rules that may apply to that particular virtual digital processing machine.” “The packet may also be examined to see if it is within bandwidth quotas. The rules may, for instance, include a maximum bandwidth setting for each virtual digital processing machine (VM). The virtual switch filtering extension may, for instance, collect traffic statistics for each VM such as, but not limited to, the number of bytes sent and received by that VM in the past second. If an incoming packet is allowed by the other rules in the list, but the amount of incoming traffic for that VM over a predetermined prior time has exceeded that VM's allowed bandwidth, then that packet may be deferred by, for instance, being returned to the back of a queue of traffic. The predetermined prior time may, for instance, be as little as a millisecond or as long as five seconds, but is typically about 1 second.” “In Step 206, “Deliver/Forward,” if the digital data package intended for a particular virtual digital processing machine is found to be in compliance with the predefined rules associated with that particular virtual digital processing machine, the package may be transmitted, and delivered on to, or forwarded on to that virtual machine.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Malkov’s “Agentless Security Of Virtual Machines Using A Network Interface Controller” with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of permitting only allowed communications in a virtual machine network.
As per claim 2, Cao in combination with Featonby and Malkov teaches the method of claim 1, Malkov further teaches wherein the first predefined safety rule is a resource usage limit, an access permission, or a network restriction (Malkov, Col. 9 Lines 63-65 recites “Outgoing packets may similarly be deferred if the outbound traffic bandwidth limit for that VM has been exceeded in a predetermined, preceding time.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Malkov’s “Agentless Security Of Virtual Machines Using A Network Interface Controller” with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of permitting only allowed communications in a virtual machine network.
As per claim 4, Cao in combination with Featonby and Malkov teaches the method of claim 1, Cao further teaches wherein the configuration parameter is compared against a known configuration parameter in a database (Cao, Paragraph 0072 recites “The VM configurations repository 510 may include a data storage device (e.g., storage system 34 of FIG. 1) that stores information regarding the configurations of VMs currently deployed to users. For example, the VM configurations repository 510 may store metadata or information regarding the configuration, characteristics, or attributes of the VMs. Examples of the information regarding the configuration, characteristics, or attributes of a VM may include”).
As per claim 5, Cao in combination with Featonby and Malkov teaches the method of claim 1, Cao further teaches wherein the second predefined safety rule is a latency limit, a content restriction, or a communication security requirement (Cao, Paragraph 0037 recites “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.”).
As per claim 7, Cao in combination with Featonby and Malkov teaches the method of claim 1, Malkov further teaches wherein the data comprises at least one of a resource limit, a runtime state, or a geographical position (Malkov, Col. 9 Lines 63-65 recites “Outgoing packets may similarly be deferred if the outbound traffic bandwidth limit for that VM has been exceeded in a predetermined, preceding time.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Malkov’s “Agentless Security Of Virtual Machines Using A Network Interface Controller” with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of permitting only allowed communications in a virtual machine network.
As per claim 8, Cao in combination with Featonby and Malkov teaches the method of claim 1, Cao further teaches wherein the inspecting is abridged responsive to the new virtual machine being a previously encountered virtual machine (Cao, Paragraph 0008 recites “monitoring, by a computing device, performance of currently deployed virtual machines (VMs) that implement particular services;”).
As per claim 9, Cao in combination with Featonby and Malkov teaches the method of claim 1, Featonby further teaches training a machine learning model with violations of at least one of the first predefined safety rule or the second predefined safety rule; employing the machine learning model to predict that a safety rule violation is going to occur; and based upon the prediction, modifying the configuration parameter or the first communication to prevent the safety rule violation (Featonby, Paragraph 0038 recites “To determine a VM instance type that is optimized for a workload, the optimization service may have generated a predefined set of workload categories that generally represent or group the workloads supported by the service provider network into categories based on the “shape” of the utilization characteristics of the workloads. The shape of utilization characteristics can refer to the amount of usage across each different compute dimension—processing, memory, storage, networking, and optionally graphics processing—which may be visualized as a plot having a number of axes corresponding to the number of compute dimensions. The plotting of utilization along each axis can result in a specific shape, for example a quadrilateral or other polygon formed by connecting the plotted points. This may be a static shape representing an average or mean utilization, a set of shapes representing minimum, maximum, average, or other statistical analyses of utilization over time, or a dynamic shape representing utilization across the compute dimensions over time. Certain utilization shapes (or ranges of similar utilization shapes) may be determined (manually or by application of suitable machine learning analysis) to represent particular types of workloads.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Featonby’s Compute Platform Recommendations For New Workloads In A Distributed Computing Environment with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of improving the performance of workloads by intelligently placing workloads on VM instance types that are computationally biases or optimized to support the workloads.
As per claim 10, Cao in combination with Featonby and Malkov teaches the method of claim 1, Cao further teaches modifying the first predefined safety rule or the second predefined safety rule responsive to a change in an external environment of the automotive computing environment (Cao, Paragraphs 0111 recites “Process 600 may further include periodically determining optimal configuration options based on the stored VM configurations and the VM performance and activity information (step 670). For example, the optimal configuration and catalog component 96 may periodically determining optimal configuration options based on the stored VM configurations and the VM performance and activity information (e.g., as described above with respect to the optimal configuration determination module 530).” And Paragraph 0123 teaches “Process 800 may further include generating or updating an automated action based on the VM modification activity and the techniques used to implement the modification (step 830). For example, the optimal configuration and catalog component 96 may generate or update an automated action based on the VM modification activity and the techniques used to implement the modification as described above with respect to the automated services module 560.”).
As per claim 11, Cao in combination with Featonby and Malkov teaches the method of claim 1, Cao further teaches determining that a cause of a violation is no longer applicable; and modifying the configuration parameter back to an initial state responsive to the determining (Cao, Paragraphs 0111 recites “Process 600 may further include periodically determining optimal configuration options based on the stored VM configurations and the VM performance and activity information (step 670). For example, the optimal configuration and catalog component 96 may periodically determining optimal configuration options based on the stored VM configurations and the VM performance and activity information (e.g., as described above with respect to the optimal configuration determination module 530).” And Paragraph 0123 teaches “Process 800 may further include generating or updating an automated action based on the VM modification activity and the techniques used to implement the modification (step 830). For example, the optimal configuration and catalog component 96 may generate or update an automated action based on the VM modification activity and the techniques used to implement the modification as described above with respect to the automated services module 560.”).
Regarding claims 13 and 20, claims 13 and 20 are directed to a system and a non-transitory computer-readable medium associated with the method of claim 1. Claims 13 and 20 are of similar scope to claim 1, and are therefore rejected under similar rationale.
Regarding claim 14, claim 14 is directed to a similar system associated with the method of claim 2 respectively. Claim 14 is similar in scope to claim 2, respectively, and are therefore rejected under similar rationale.
Regarding claim 16, claim 16 is directed to a similar system associated with the method of claim 4 respectively. Claim 16 is similar in scope to claim 4, respectively, and are therefore rejected under similar rationale.
Regarding claim 17, claim 17 is directed to a similar system associated with the method of claim 5 respectively. Claim 17 is similar in scope to claim 5, respectively, and are therefore rejected under similar rationale.
Regarding claim 19, claim 19 is directed to a similar system associated with the method of claim 7 respectively. Claim 19 is similar in scope to claim 7, respectively, and are therefore rejected under similar rationale.
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752), Featonby et al. (US 2020/0310852) and Malkov et al. (US 10,701,104) and in further view of El-Moussa et al. (US 2021/0286873).
As per claim 3, Cao in combination with Featonby and Malkov teaches the method of claim 1, but fails to teach wherein the configuration parameter is an allocated quantity of memory, a port configuration, an indication of a dependency, or an indication of an access requirement. However, in an analogous art El-Moussa teaches wherein the configuration parameter is an allocated quantity of memory, a port configuration, an indication of a dependency, or an indication of an access requirement (El-Moussa, Paragraph 0093 recites “For example, each attack characteristic can have associated one or more protective measures such, inter alia: a configuration parameter or change to a configuration parameter for a VM to protect against attacks exhibiting a particular characteristic, such as disabling DNS redirection, restricting access to certain resources such as files or directories, closing certain network ports, and the like; and/or an additional function, routine, facility, service or other resource suitable for detecting and/or protecting against attacks exhibiting a particular characteristic, such as antimalware software, intrusion detection facilities, proxies and firewalls and the like.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use El-Moussa’s mitigating security attacks in virtualized computing environments with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of mitigating a security attack against a target virtual machine.
Regarding claim 15, claim 15 is directed to a similar system associated with the method of claim 3 respectively. Claim 15 is similar in scope to claim 3, respectively, and are therefore rejected under similar rationale.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752), Featonby et al. (US 2020/0310852) and Malkov et al. (US 10,701,104) and in further view of Goto (US 2022/0171612).
As per claim 12, Cao in combination with Featonby and Malkov teaches the method of claim 1, but fails to teach wherein the new virtual machine and each virtual machine of the plurality of virtual machines is assigned an automotive safety integrity level (ASIL), and wherein the first predefined safety rule or the second predefined safety rule is determined based at least in part upon the ASIL of the new virtual machine or of a virtual machine of the plurality of virtual machines.
However, in an analogous art Goto teaches wherein the new virtual machine and each virtual machine of the plurality of virtual machines is assigned an automotive safety integrity level (ASIL), and wherein the first predefined safety rule or the second predefined safety rule is determined based at least in part upon the ASIL of the new virtual machine or of a virtual machine of the plurality of virtual machines (Goto, Paragraph 0123 recites “According to the example in FIG. 5, the real storage 130 includes storage areas for QM, ASIL-A, ASIL-B, ASIL-C, and ASIL-D. The storage area in the real storage 130 used by the virtual machine included in the ECU 10 corresponds to the safety integrity of the virtual machine itself or that of the configuration given to the virtual machine. For example, when the virtual machine is assigned the safety integrity of ASIL-A, the virtual machine uses the storage area for ASIL-A in the real storage 130 as illustrated in FIG. 5. Even if the safety integrity of the virtual machine is ASIL-A, the virtual machine may include a highly independent configuration assigned ASIL-B. Then, the configuration may use the storage area for ASIL-B.”).
It would have been obvious to a person of ordinary skill in the art, before the earliest effective filing date to use Goto’s Electronic control unit, software update method, software update program product and electronic control system with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of virtualization technology that can suppress the total number of electronic control units by integrating multiple functions into one electronic control unit.
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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RODERICK . TOLENTINO
Examiner
Art Unit 2439
/RODERICK TOLENTINO/Primary Examiner, Art Unit 2439
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