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
Response to Arguments
Applicant’s arguments with respect to independent claim(s) 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.
Applicant states: Applicant submits that the amendments render the § 101 rejection moot. Furthermore, the claims are patentable under § 101 at least because the human mind is incapable of executing an explainable Al model or identifying which computing resources cause variances in the time required to log in. The human mind is not equipped to perform Al-based causal modeling. Accordingly, Applicant respectfully requests withdrawal of the § 101 rejection.
Examiner states: Examiner respectfully disagrees. The newly added limitations merely incorporate additional abstract concepts relating to mental concepts and math (identifying a configuration, determining a variance, correlating the variance). Applicant’s specification states duration information is merely information collected by an agent: [0063] The monitoring agents 120 and 197 may monitor, measure, collect, and/or analyze data on a predetermined frequency, based upon an occurrence of given event(s), or in real time during operation of network environment 100. The monitoring agents may monitor resource consumption and/or performance of hardware, software, and/or communications resources of clients 102, networks 104, appliances 200 and/or 205, and/or servers 106. For example, network connections such as a transport layer connection, network latency, bandwidth utilization, end-user response times, application usage and performance, session connections to an application, cache usage, memory usage, processor usage, storage usage, database transactions, client and/or server utilization, active users, duration of user activity, application crashes, errors, or hangs, the time required to log-in to an application, a server, or the application delivery system, and/or other performance conditions and metrics may be monitored. That is, the limitations as cited above as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental and math processes. Examiner maintains the 101 rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent claims the determining metrics, identifying a configuration, determining a variance, correlating information with the metrics, and generating a recommendation, as drafted, recites functions that, under its broadest reasonable interpretation, covers a function that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitations as cited above as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process.
Thus, these limitation falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, this judicial exception is not integrated into a practical application. The claim recites the following additional limitations: processor and AI model. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, MPEP 2106.05(f), and steps of receiving do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processor and AI model amount to no more than mere instructions, or generic computer/computer components to carry out the exception. Furthermore, the limitations directed to receiving, the courts have identified mere data gathering is well-understood, routine and conventional activity. See MPEP 2106.05(d).
The recitation of generic computer instruction and computer components to apply the judicial exception, and mere data gathering do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Regarding claim 2, 4, 6, 7, 11, 13, 15, 16 the limitations describing details of the actions, generating a user interface, describing parameters, applying metrics to a model are nothing more than insignificant extra solution activity which is not a practical application under prong 2.
Regarding claim 3, 5, 8, 12, 14, 17, 20, 21 the limitations of clustering and comparing metrics, generating a recommendation and based upon a comparison, what metrics comprise, are functions that can be reasonably performed in the human mind, thus, additional mental process defined in the claims. The limitations of receiving are nothing more than insignificant extra solution activity which is not a practical application under prong 2.
Regarding claim 9, 18 the limitation of receiving duration from an agent via a processor is considered mere instructions, or generic computer/computer components to carry out the exception Accordingly, the additional element recited in claim 3 fails to provide a practical application under prong 2, or amount to significantly more under step 2B.
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.
Claim/s 1, 2, 10, 11, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman (Pub. No. US 2019/0327159) in view of Mandal (Pub. No. US 2023/0105304).
Claim 1, 10, 19 Baggerman teaches “a method comprising: receiving, by one or more processors, for a plurality of client devices of a tenant ([0025] Likewise, although only two of the user VMs (e.g., the user VMs 120, the user VMs 135, and the user VMs 150) are shown on each of the respective first node 105, the second node 110, and the third node 115, in other embodiments, the number of the user VMs on each of the first, second, and third nodes may vary to include either a single user VM or more than two user VMs.), a duration for performing a plurality of actions to log into a resource ([0049] The sub-events of the event stream 300 can each correspond to any process or task performed by the virtual machines 220 within the virtual environment 205 of FIG. 2. Accordingly, the event stream 300 can be used to visualize the process by which the logon event monitoring circuit 230 of FIG. 2 can determine a duration of a logon event. For example, within the event stream 300, a group of sub-events including sub-event 3, sub-event 4, sub-event 5, and sub-event 6 together can form a logon event 305.); determining, by the one or more processors, metrics for each action of the plurality of actions ([0050] The logon event monitoring circuit 230 can determine the duration of the logon event 305 by subtracting T2 from T6. In addition, the logon event monitoring circuit 230 can be configured to determine durations for any of the sub-events included within the logon event 305 (i.e., sub-event 3, sub-event 4, sub-event 5, and sub-event 6). The logon event monitoring circuit 230 can determine the duration of these subevents in a manner similar to that used to determine the duration of the overall logon event 305 by finding the difference between the time at which each sub-event begins and ends. In some embodiments, the data management circuit 240 can be configured to store information corresponding to the sub-events of the logon event 305 in the record that it generates for the logon event 305. For example, the data management circuit 240 can generate the record to include an identification of each sub-event, along with its respective duration, within the record for the logon event 305. The data management circuit 240 can also store all of this information in the database 250.); identifying an installation configuration of at least one client device of the plurality of client devices, the installation configuration comprising one or more computing resource availability metrics ([0021] The duration of a logon event can vary based on a variety of factors, such as the number and computational complexity of the computing processes that are performed during the logon event, as well as the capacity and performance of the computing resources (e.g., processors, memory, etc.) available for executing the processes.); and generating, by the one or more processors, one or more recommendations corresponding to at least one action of the plurality of actions, to reduce the duration to log into the resource ([0051] In some embodiments, having a record of the sub-events of a group of logon events 305 can be useful for a network administrator because it may provide insight into the components of logon events that contribute disproportionately to the overall duration of the logon events. For example, it may be discovered that a particular type of sub-event makes up a disproportionate amount of the overall duration of logon events. As a result, that sub-event may be modified to reduce the time it takes for the sub-event to be completed, or the sub-event may be removed entirely from the logon process (e.g., if it is an optional sub-event that is not required to complete a logon) in order to save time on logon events in the future.)”.
However, Baggerman may not explicitly teach the newly added limitations.
Mandal teaches “determining, by an explainable artificial intelligence (AI) model, a variance in the duration of actions … associated with the installation configuration ([0063] In step 240, PRT 150 detects the occurrence of outliers for a set of KPIs in associated components during processing of (current) user requests (i.e. logon events of Baggerman) received from end-user systems 110. As is well known, outliers for KPIs typically indicate over or under allocation of resources in corresponding components deployed in computing environment 135. The normal values and/or the extent of deviations forming the basis for outliers may be pre-specified as well, as is well known in the relevant arts. [0127] The rows in outlier table 620 indicates the details of the performance metrics in the corresponding block durations. It may be observed that rows 621 and 622 indicate the detection of outliers in the performance metrics CPU and DISK_IOREAD of component X1 in the corresponding block durations. Similarly, the performance metrics of other components of software application 300 deployed in computing environment 135 are processed to detect the outliers.); correlating, by the explainable Al model, the variance in the duration of actions … with at least one of the one or more computing resource availability metrics ([0061] In step 220, PRT 150 trains a probabilistic model with prior incidents to correlate incidents to outliers occurring in the components (deployed in nodes 160 of computing environment 135), the correlation determined based on the causal dependency graph. Specifically, the probabilistic model maps the prior occurrences of incidents in the components with the prior occurrences of outliers for KPIs observed in the same or other components deployed in computing environment 135. In one embodiment, the probabilistic model is a Markov network well known in the relevant arts.)”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Mandal with the teachings of Baggerman in order to provide a system that teaches analyzing of information. The motivation for applying Mandal teaching with Baggerman teaching is to provide a system that allows for additional analysis utilizing AI models. Baggerman, Mandal are analogous art directed towards analyzing resource usage. Together Baggerman, Mandal teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of Mandal with the teachings of Baggerman by known methods and gained expected results.
Claim 2, 11, the combination teaches the claim, wherein Baggerman teaches “the method of claim 1, wherein the plurality of actions comprises at least one of brokering a session to the resource ([0054] The method 400 also includes identifying an end time of the logon event (step 410). In some embodiments, the logon event monitoring circuit 230 can be configured to determine the end time based on the end time of an event or sub-event of the logon event that occurs at the end of the logon event. For example, the end of the logon event can be the time at which the user can interact with the operating system. A sub-event that coincides with this, such as a sub-event relating to rendering a desktop or other interface element for display to the user, be used to signal the end time of the logon event. [0045] The data management circuit 240 can also generate the record to include a unique identifier for the user who initiated the logon event, such as a username of the user. In some embodiments, the record may also include additional information, such as an identification of the operating system initialized during the logon event, or a description of the computing resources (e.g., processors or memory devices) used to execute the processes that make up the logon event.), a virtual machine start-up, establishing a connection between the respective client device and the virtual machine, application of a global policy, execution of log- on scripts, loading a profile, or handoff”.
Claim/s 3, 4, 5, 12, 13, 14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal, in view of Ravi (Pub. No. US 2015/0106499).
Claim 3, 12, 20, Baggerman teaches “the method of claim 1, wherein the plurality of client devices for the tenant comprises a first plurality of client devices for a first tenant, the method further comprising ([0022] Referring now to FIG. 1, a virtual computing system 100 is shown, in accordance with some embodiments of the present disclosure. The virtual computing system 100 includes a plurality of nodes, such as a first node 105, a second node 110, and a third node 115. The first node 105 includes user virtual machines (“user VMs”) 120A and 120B (collectively referred to herein as “user VMs 120”), a hypervisor 125 configured to create and run the user VMs, and a controller/service VM 130 configured to manage, route, and otherwise handle workflow requests between the various nodes of the virtual computing system 100.): receiving, by the one or more processors, for a second plurality of clients for a plurality of second tenants, a second duration for performing the plurality of actions to log into the resource ([0040] Together, the logon event monitoring circuits 230 and data management circuits 240 can monitor and record information related to the duration of logon events that occur on their respective virtual machines 220. In some embodiments, a virtual machine 220 may be configured to be accessible to only a single user, and the logon event monitoring circuit 230 and data management circuit 240 of the virtual machine 220 may record information relating to logons by that user. In some other embodiments, a virtual machine 220 may be configured to provide access to a group of users, and the logon event monitoring circuit 230 and data management circuit 240 may be configured to record information relating to logons by any user of the group of users. In addition, future logon events for the same user, as well as logon events for different users, can also be measured to determine their respective durations. Such information can be aggregated and provided for display (e.g., to a network administrator) to provide insight into the time required for various users to successfully logon to a virtual machine at various times and under various conditions. In some embodiments, additional metrics can also be generated, such as durations for individual processes or sub-events that make up a logon event, or metrics relating to a group of logon events (e.g., average duration for a group of logon events). These and other aspects of the disclosure are described in greater detail below.); clustering, by the one or more processors, the first tenant and the plurality of second tenants into a plurality of clusters according to one or more parameters of the first tenant and the plurality of second tenants ([0046] In some embodiments, the data management circuits 240 can be configured to determine additional metrics related a group of logon events. For example, by referring to the stored records of a group of past logon events, a data management circuit 240 can determine an average, a minimum, or a maximum logon duration for the group of logon events. The data management circuit 240 can also be configured to store information corresponding to these metrics (i.e. parameters) in the database 250.)”.
However, Baggerman may not explicitly teach comparing.
Ravi teaches “comparing, by the one or more processors, the metrics identified for the first tenant to metrics for each action of the plurality of actions of a subset of the plurality of second tenants, the first tenant clustered with the subset of the plurality of second tenants ([0074] The embodiment of FIG. 5 implements a portion of a computer system, shown as system 500, comprising a computer processor to execute a set of program code instructions (see module 510) and modules for accessing memory to hold program code instructions to perform: monitoring, using a computer, a plurality of applications running in a cloud service computing environment to capture a first set of metrics and a second set of metrics of a respective at least two cloud service tenants (see module 520); comparing a first one of the first set of metrics with respect to a second one of the second set of metrics (see module 530); and ranking a first cloud service tenant with respect to a second cloud service tenant where the ranking is based at least in part on the compared metrics (see module 540) [0061] As shown, a particular canonical form of computations as used in systems for transforming cloud service measurements into anonymized extramural business rankings is given by enumeration of each combination of tenant, application and computation (see operation 310) (i.e. action), then for each enumerated combination, decision 312 is entered, and for a particular enumerated combination, the computation formula of the combination is evaluated (see operation 314) and the result saved in a relation such as a table of normalized business metrics (see operation 316) before continuing. If there remain more entries in the enumerated combinations, then processing proceeds to labeling steps.)”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Ravi with the teachings of Baggerman, Mandal in order to provide a system that teaches comparing of information of tenants. The motivation for applying Ravi teaching with Baggerman, Mandal teaching is to provide a system that allows for additional analysis of data as taught by Baggerman, Mandal. Baggerman, Mandal, Ravi are analogous art directed towards providing recommendations. Together Baggerman, Mandal, Ravi teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of Ravi with the teachings of Baggerman, Mandal by known methods and gained expected results.
Claim 4, 13 the combination teaches the claim, wherein Ravi teaches “the method of claim 3, further comprising: generating, by the one or more processors, a user interface for display on a device associated with the first tenant, the user interface including one or more graphical representations corresponding to the plurality of actions based on the comparison. ([0029] In exemplary scenarios described herein, users 106 interact with applications 126. During the course of execution of the applications 126, a monitor engine 130 captures aspects of performance and behavior occurring in the cloud infrastructure, possibly including traffic measurements, timing measurements, application behaviors, etc. Capture of certain application behaviors can be facilitated by instrumentation code (e.g., instrumentation 127.sub.1, instrumentation 127.sub.3, etc.) that can be included within and/or adjacent to the applications. The monitor engine 130 can be configured by an analyst 105.sub.1. For example, an analyst can use a graphical user interface (GUI) to interact with the monitor engine 130 and/or the instrumentation code in order to define a set of measurements (e.g., the herein-mentioned cloud service measurements), which measurements can be taken continuously during the operation of the applications. In some cases, the continuous capture of a particular measurement might demand high-frequency capture and storage, and in any such cases an event cache 131 might be used. [0032] As can be understood, the cooperation of the elements within environment 1A00 provide a means by which cloud service measurements can be continuously collected in real-time or near-real time, then transformed into reports suited for further analysis (e.g., human analysis and/or computer-aided analysis, etc.))”.
Rationale to claim 3 is applied here.
Claim 5, 14 the combination teaches the claim, wherein Baggerman teaches “The method of claim 3, wherein generating the one or more recommendations corresponding to the at least one action is based on the comparison of the metrics for the first tenant to the metrics for each action of the subset of the plurality of second tenants ([0047] The GUI circuit 260 can be configured to retrieve information from the database 250, generate display information based on the information retrieved from the database 250, and provide the display information to the display 270 for presentation to a user. In some embodiments, a user may interact with the GUI circuit 260 to request certain information to be displayed based on one or more search parameters. For example, the GUI circuit 260 can be configured to retrieve logon event duration records from the database according to parameters such as a range of dates or times during which the logon events took place, a subset of the virtual machines 220 that experienced logon events, a user or group of users who initiated logon events. The GUI circuit 260 can manipulate the records it retrieves from the database 250, for example to format the information in a manner that allows it to be displayed by the display 270. In some embodiments, the GUI circuit 260 can format the information for display as part of an HTML5 interface. In some embodiments, the GUI circuit 260 can format the information to be displayed in the form of graphs, charts, tables alphanumeric characters, or any combination of these. The GUI circuit 260 can transmit the formatted information to the display 270, which can be configured to display the formatted information to a viewer such as a network administrator. In some embodiments, the display 270 can be a television a computer monitor, or an electronic display of a mobile computing device, tablet computing device, or laptop computer. [0051] In some embodiments, having a record of the sub-events of a group of logon events 305 can be useful for a network administrator because it may provide insight into the components of logon events that contribute disproportionately to the overall duration of the logon events. For example, it may be discovered that a particular type of sub-event makes up a disproportionate amount of the overall duration of logon events. As a result, that sub-event may be modified to reduce the time it takes for the sub-event to be completed, or the sub-event may be removed entirely from the logon process (e.g., if it is an optional sub-event that is not required to complete a logon) in order to save time on logon events in the future.)”.
Claim/s 6, 15, is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal, Ravi, in view of Aoki (Pub. No. US 2023/0123212).
Claim 6, 15 the combination may not explicitly teach the claim.
Aoki teaches “the method of claim 3, wherein the one or more parameters comprise at least one of a tenant domain, a number of users per tenant, a number of applications, an average number of users per a time period, an average specification and usage of a virtual delivery agent, an average number of sessions per the time period, a number of virtual delivery agents, a number of users ([0022] The parameters include, but not limited to, for example, the number of users who utilize a service), or an average number of applications used per the time period”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Aoki with the teachings of Baggerman, Mandal, Ravi in to provide a system that teaches different parameters. The motivation for applying Aoki teaching with Baggerman, Mandal, Ravi teaching is to provide a system that allows for other forms of analysis when providing recommendations. Baggerman, Mandal, Ravi, Aoki are analogous art directed towards resource management. Together Baggerman, Mandal, Ravi, Aoki teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of Aoki with the teachings of Baggerman, Mandal, Ravi by known methods and gained expected results.
Claim/s 7, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal, in view of Singh (Pub. No. US 2021/0224178).
Claim 7, 16 Baggerman may not explicitly teach the limitation.
Singh teaches “the method of claim 1, further comprising: applying, by the one or more processors, the metrics for each action to a machine learning model trained to generate the one or more recommendations based on training metrics and corresponding recommendations ([0101] Notably, the techniques herein may employ any number of machine learning techniques, such as to classify the collected data, cluster the data, and provide recommendations, as described herein. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., collected metric/event data from agents, sensors, etc.) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function is a function of the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization/learning phase, the techniques herein can use the model M to classify new data points. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.)”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Singh with the teachings of Baggerman, Mandal in order to provide a system that teaches learning models. The motivation for applying Singh teaching with Baggerman, Mandal teaching is to provide a system that allows for training data to improve system recommendations over time. Baggerman, Mandal, Singh are analogous art directed towards providing recommendations. Together Baggerman, Mandal, Singh teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of Singh with the teachings of Baggerman, Mandal by known methods and gained expected results.
Claim/s 8, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal in view of Doddaiah (Pub. No. US 2021/0342245).
Claim 8, 17 Baggerman may not explicitly teach the limitation.
Doddaiah teaches “the method of claim 1, wherein generating the one or more recommendations is based on a comparison of an average of the metrics corresponding to the tenant for the plurality of actions over a first time period to metrics corresponding to the tenant for the plurality of actions over a second time period ([0060] In some embodiments, the QOS recommendation engine 160 does a live technical analysis on time series charts for host IOPS, host response time, host FC bandwidth, storage system 100 internal fabric bandwidth, storage system 100 memory consumption, prefetch CPU cycle consumption, prefetch fabric bandwidth consumption, asynchronous replication CPU cycle consumption, asynchronous replication fabric bandwidth consumption, and storage system 100 CPU cycle consumption, per storage group 170, to find the current storage burst (trend) activities on a given storage group 170. In some embodiments, this analysis is implemented using exponential moving averages for every time window. The time windows may be, for example, ten minutes in length or some other amount of time depending on the implementation. Lower and upper bound values from these time series values are compared with customer set host QOS metrics 155 to find average percentage of over utilization or underutilization of storage system 100 resources. The QOS recommendation engine 160 then provides a set of recommended changes to the host QOS metrics 155. The recommended changes to host QOS metrics 155 may be output as normalized values or as in percentages. In some embodiments, the time series values per storage group 170 are saved for subsequent historical analysis.)”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Doddaiah with the teachings of Baggerman, Mandal in order to provide a system that teaches details of recommendations. The motivation for applying Doddaiah teaching with Baggerman, Mandal, teaching is to provide a system that allows for training data to improve system recommendations over time. Baggerman, Mandal, Doddaiah are analogous art directed towards providing recommendations. Together Baggerman, Mandal, Doddaiah teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of Doddaiah with the teachings of Baggerman, Mandal, by known methods and gained expected results.
Claim/s 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal, in view of VIJAYVARGIYA (Pub. No. US 2021/0019414)
Claim 9 Baggerman teaches “the method of claim 1, wherein receiving the duration for performing the plurality of actions to log into the resource comprises receiving, by the one or more processors, from a respective a … delivery agent of each client device of the plurality of client devices, the duration for performing each action of the plurality of actions to log into the resource via the … delivery agent ([0040] Together, the logon event monitoring circuits 230 and data management circuits 240 can monitor and record information related to the duration of logon events that occur on their respective virtual machines 220. In some embodiments, a virtual machine 220 may be configured to be accessible to only a single user, and the logon event monitoring circuit 230 and data management circuit 240 of the virtual machine 220 may record information relating to logons by that user. In some other embodiments, a virtual machine 220 may be configured to provide access to a group of users, and the logon event monitoring circuit 230 and data management circuit 240 may be configured to record information relating to logons by any user of the group of users.)”.
However, Baggerman may not explicitly teach a virtual delivery agent.
VIJAYVARGIYA teaches virtual delivery agent ([0017] Each guest agent 126 running in each of the VMs 124 monitors and collects process behaviors and states in that VM. The guest agent 126 also enforces security policies rules on the VM. The collected information from each guest agent 126 in the VMs 124 in a particular host computer 104 is transmitted to the host agent 128 in that particular host computer 104, which may aggregate the collected information and send the aggregated information to the application security manager 108 via the security appliance 109.)”.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of VIJAYVARGIYA with the teachings of Baggerman, Mandal in order to provide a system that teaches virtual agent. The motivation for applying VIJAYVARGIYA teaching with Baggerman, Mandal teaching is to provide a system that allows for design choice. Baggerman, Mandal, VIJAYVARGIYA are analogous art directed towards virtualized systems. Together Baggerman, Mandal, VIJAYVARGIYA teaches every limitation of the claimed invention. Since the teachings were analogous art known at the filing time of invention, one of ordinary skill could have applied the teachings of VIJAYVARGIYA with the teachings of Baggerman, Mandal, by known methods and gained expected results.
Claim/s 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baggerman, Mandal in further view of Rosenberg (Pub. No. US 2021/0026707).
Claim 21, the combination may not explicitly teach metrics as free resources.
Rosenberg teaches “the method of claim 1, wherein the computing resource availability metrics comprise free disk space, free memory space, processor speed, and data link type ([0017] Host eligibility may depend, as alluded to above, on the target host having the minimum required available computing and networking resources to receive the migrated process, such as CPU (computer processing unit), memory, local disk, remote disk, and network resources. CPU resources may refer to processor type, clock speed, cores, threads, CPU cycles, or other indications of computer processing power. For example, a target host CPU running at 3.0 gigahertz with four available CPU cores would be eligible to host a process requiring a minimum clock speed of 1.8 gigahertz, and a maximum of two cores or a minimum of one core, Memory resources may refer to an amount of available free memory, memory access time, memory timing, or other indications of memory performance, usage or availability. For example, a target host with 16 gigabytes of free memory would be eligible to host a process requiring a recommended 4 gigabytes of free memory, with maximum and minimum memory usage (e.g., based on historical measurements) between 8 gigabytes and 0.5 gigabytes of free memory. Local disk may refer to an amount of local disk space available, input/output operations per second (IOPS), local disk access time, local disk seek time, or other indications of local disk availability, performance, or usage. For example, a target host with 1 terabyte of local disk space would be eligible to host a process requiring 12$ megabytes of disk space. Remote disk may refer to an amount of available network or cloud storage, input/output operations per second (IOPS), remote disk access time, remote disk seek time, remote disk latency, or other indications of remote disk availability, performance, or usage. For example, a target host with 100 terabytes of network or cloud storage would be eligible to host a process requiring 1 terabyte of disk space. Network resources may refer to a network connection type, bandwidth, average path distance, latency, or other indications of network availability or usage. For example, a target host with a T5 connection, offering 400 megabits per second (Mbps) of bandwidth, located 5 hops from the source host, and having 3 milliseconds of latency, would be eligible to host a process requiring a minimum of a T1 connection, 1.5 Mbps bandwidth, an average path distance of 10 hops, and 100 milliseconds of latency. Other parameters and qualifiers (other than minimum, average, maximum) may also be used to determine if a host is eligible to host a process. In some examples, eligibility may be determined based on passing or failing the requirements of a single parameter or a plurality of parameters (e.g., a filter). In some examples, eligibility may be determined based on a totality of parameters, such that even if the requirements of one or more parameters are not met, but other parameters are met or exceeded, the net result may be an eligible host (e.g., a net score).)”.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WYNUEL S AQUINO whose telephone number is (571)272-7478. The examiner can normally be reached 9AM-5PM EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached at 571-272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WYNUEL S AQUINO/Primary Examiner, Art Unit 2199
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