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 instant Application 18/388,675 filed on 11/10/2023. Claims 1-20 are pending. This Office Action is Non-Final.
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, 8-11, 13, 14, 16, 17 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).
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).”);
and responsive to the first communication violating a second predefined safety rule, modifying a parameter of the automotive computing environment, or intercepting the first communication and modifying a content of the first communication (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.”).
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, at 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 2, Cao in combination with Featonby teaches the method of claim 1, Cao further teaches wherein the first predefined safety rule is a resource usage limit, an access permission, or a network restriction (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 4, Cao in combination with Featonby 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 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 8, Cao in combination with Featonby 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 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, the parameter of the automotive computing environment, 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, at 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 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 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 or the parameter of the automotive computing environment 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.
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752) and Featonby et al. (US 2020/0310852) and in further view of El-Moussa et al. (US 2021/0286873).
As per claim 3, Cao in combination with Featonby 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, at 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) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752) and Featonby et al. (US 2020/0310852) and in further view of Lyu (US 2024/0179178).
As per claim 6, Cao in combination with Featonby teaches the method of claim 1, but fails to teach wherein the parameter of the automotive computing environment is a communication rate limit, a network bandwidth, or a communication encryption setting.
However, in an analogous art Lyu teaches wherein the parameter of the automotive computing environment is a communication rate limit, a network bandwidth, or a communication encryption setting (Lyu, Paragraph 0089 recites “Therefore, in some embodiments, the at least one back pressure field may include a processing manner field, a rate limit type field, and a back pressure object field; the filling the back pressure indication information into the at least one back pressure field, may include: determining at least one processing manner of a rate limit processing manner and an alarm processing manner which correspond to the source virtual machine, and writing a parameter value identifying the at least one processing manner into the processing manner field; for example, the value 0 indicates the rate limit processing manner, and the value 1 indicates the alarm processing manner; in a case where the at least one processing manner includes the rate limit processing manner, determining a rate limit type corresponding to the source virtual machine, and writing a parameter value identifying the rate limit type into the rate limit type field; for example, the value 0 indicates the pps rate limit, the value 1 indicates the bps rate limit, and the value 2 indicates performing the pps rate limit and the bps rate limit at the same time; and determining a back pressure object in the source virtual machine, determining valid information in the inner-layer quintuple information based on the back pressure object, and writing a parameter value identifying the valid information into the back pressure object field; for example, the value 0 indicates that the source IP address in the inner-layer quintuple information is valid, the value 1 indicates that the triple, that is, the source IP address, the destination IP address and the transport layer protocol, in the inner-layer quintuple information are valid, and the value 2 indicates that all inner-layer quintuple are valid.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Lyu’s control method and apparatus, computing device, and computer-readable storage medium with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of performing back pressure on the source virtual machine, to solve the traffic attack problem.
Regarding claim 18, claim 18 is directed to a similar system associated with the method of claim 6 respectively. Claim 18 is similar in scope to claim 6, respectively, and are therefore rejected under similar rationale.
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752) and Featonby et al. (US 2020/0310852) and in further view of Chari et al. (US 2015/0033223).
As per claim 7, Cao in combination with Featonby teaches the method of claim 1, but fails to teach wherein modifying a content of the first communication comprises removing sensitive data, and wherein the sensitive data comprises at least one of a resource limit, a runtime state, or a geographical position.
However, in an analogous art Chari teaches wherein modifying a content of the first communication comprises removing sensitive data, and wherein the sensitive data comprises at least one of a resource limit, a runtime state, or a geographical position (Chari, Paragraph 0041 recites “Sanitizer 224 is a software module that sanitizes labeled sensitive data contained within virtual machines. Sanitization is the process of removing the labeled sensitive data from the virtual machines so that the labeled sensitive data is no longer available or retrievable within the virtual machines. Sanitizer 224 includes sanitization scripts 234, sanitization policies 236, and sanitization script execution polices 238.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Chari’s Sanitization of virtual machine images with Cao’s Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment because it offers the advantage of preventing data leakage.
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) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 2017/0199752) and Featonby et al. (US 2020/0310852) and in further view of Goto (US 2022/0171612).
As per claim 12, Cao in combination with Featonby 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, at 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
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RODERICK . TOLENTINO
Examiner
Art Unit 2439
/RODERICK TOLENTINO/Primary Examiner, Art Unit 2439