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 Applicant’s Amendment filed 01/15/2026. Claims 1-3,5-7,10-13,16-17, 19, and 21 are pending. Claims 1, 10, and 16 have been amended; new Claim 21 has been added. Claims 4, 8-9, 14-15, 18 and 20 were previously canceled. Any examiner’s note, objection, or rejection not repeated is withdrawn due to Applicant’s amendment.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/25/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on foreign application no. PCT/CN2022/087590, filed on 04/19/2022. It is noted, however, that applicant has not filed a certified copy of the foreign application as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 20150341223 A1) in view of Gaber et al. (US 20200387584 A1), and further in view of Chan et al. (US 20140289382 A1) hereinafter referred to as Shen, Gaber, and Chan, respectively.
Regarding Claim 1, Shen discloses A computing device ([0027] physical computers) comprising: a memory ([0027] including at least memory) and a processor coupled to the memory ([0027] one or more processors) and configured to collect usage activity data across a plurality of different applications for a plurality of users ([0026] monitored configurations may also include client information. The client information may include […] software programs running on each of the clients […] monitoring the current usage of resources by the clients. Please note that monitoring client information including software programs and the current usage of resources by clients corresponds to Applicant’s collecting usage activity data across a plurality of different applications for a plurality of users.),
for each group of users: determine respective application priorities for the based upon the usage activity data for the group of users ([0048] ranking of the candidate clusters may involve using various information, such as resource utilization metrics. Please note that ranking candidate clusters based on resource utilization metrics corresponds to Applicant’s determining respective application priorities for the applications for each group of users based upon the usage activity data for the group, as the ranking system could be applied to applications in the clusters as well.),
and determine computing resource allocations for each of the prioritized applications based upon the respective application priority ([0026] cluster resource management module operates to allocate available resources among clients running in the cluster based on a number of parameters, which may include […] priorities. Please note that allocating available resources among clients running in the cluster based on priorities corresponds to Applicant’s determining computing resource allocations for each of the prioritized applications based upon the respective application priorities.),
and run applications for the users within a virtual workspace environment with the computing resource allocations for the respective group of users applied thereto ([0026] cluster management server includes a cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations […] among clients running in the cluster; [0027] In other embodiments, the cluster management servers may be implemented as software programs running on […] virtual computers. Please note that clients running in the cluster with resource allocations performed among them by the CRMM corresponds to Applicant’s running applications for the users with computing resource allocations for the respective group of users applied. Furthermore, the cluster management server running on virtual computers corresponds to Applicant’s applications for the users being run within a virtual workspace environment.).
Shen does not explicitly disclose determine different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data, wherein the usage activity data comprises at least one or more of user mouse clicks, keystrokes, CPU usage, network traffic, or application window activity.
However, Gaber discloses determine different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data .( [0027] The persona clustering module 320 may employ, for example, K-means clustering and unsupervised learning, in order to associate users into different usage categories.; [0028] In some embodiments, unsupervised learning techniques are employed by the persona clustering module 320 as the outcome is unknown but patterns can be found in the usage data. Please note that using K-means clustering and unsupervised learning techniques to associate users into different usage categories based on patterns found in the usage data corresponds to Applicant’s determining different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data.),
wherein the usage activity data comprises user mouse clicks, keystrokes, CPU usage, ([0026] the exemplary software license optimization system 300 processes a number of exemplary KPIs 310-1 through 310-j, such as CPU utilization 310-1 […] keyboard activity 310-4 […] mouse activity. The various KPIs 310 reflect actual usage of the software product for individual users. Please note that processing collected KPIs such as CPU utilization, keyboard activity, and mouse activity reflecting usage for individual users corresponds to Applicant’s usage activity data comprising user mouse clicks, keystrokes, and CPU usage, as the KPIs are then used in the clustering system.)
Shen and Gaber are both considered to be analogous to the claimed invention because they are in the same field of grouping users in computer systems based on usage activity data for optimization. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen to incorporate the teachings of Gaber to modify the scaling usage-based resource allocation system to group users based on unsupervised K-means clustering of the usage activity data, where the usage activity comprises user mouse clicks and keystrokes, allowing for efficient automated resource allocation to groups based on patterns of usage and detailed data, as described in Gaber.
Shen-Gaber does not explicitly disclose wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application.
However, Chan discloses wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application ([0045] the usage patterns of a group of users can be determined; [0045] information about this user's usage pattern can help prioritize tasks, e.g. which applications are less important; [0047] Another aspect of this technology is to utilize the usage patterns of a group of users to optimize performance at a holistic, service provider level. In one embodiment, when the version vectors data is generated for a group of users, a service provider can mine the data to obtain usage patterns of the group, which enables service optimization in many different ways. Please note that determining the usage patterns of a group of users, and using usage pattern information to prioritize tasks, such as which applications are less important, corresponds to Applicant’s higher application priority indicating a higher level of usage of the corresponding application by the group of users, as a more utilized application by a group of users would have a higher application priority in this system. )
Shen-Gaber and Chan are both considered to be analogous to the claimed invention because they are in the same field of computer resource management for users. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber to incorporate the teachings of Chan to modify the grouped user usage-based resource scaling system to utilize the usage patterns of groups of users for corresponding applications to indicate a higher application priority, allowing for service optimization and thus improved resource usage, as described in Chan.
Regarding Claim 2, Shen-Gaber-Chan as disclosed in Claim 1, Shen further discloses associate new users with an existing group of users based upon user job descriptions ([0047] a client placement algorithm to select a placement cluster from a list of candidate clusters, which are determined to satisfy [...] policy requirements; [0050] An initial client placement occurs when a client is first deployed in the distributed computer system 100. Please note that an initial client placement into a placement cluster from a list of candidate clusters based on policy requirements corresponds to Applicant’s associating new users with an existing group of users based upon user job descriptions, as a user job description being used for association is an instance of a policy requirement.).
Regarding Claim 3, Shen-Gaber-Chan as disclosed in Claim 1, Shen further discloses move the users between existing groups of users over time based upon usage activity ([0043] A subsequent client placement occurs when changes in policy or condition of a client or its environment necessitate a new placement location for the client. Please note that a subsequent client placement occurring based on changes in policy or condition of a client corresponds to Applicant’s moving users between existing groups of users over time based upon usage activity, as the condition of the client corresponds to Applicant’s user activity as a criterion to use in moving users between existing groups.).
Regarding Claim 7, Shen-Gaber-Chan as disclosed in Claim 1, Shen further discloses the computing resource allocations comprise at least one of random access memory (RAM), central processing unit (CPU) allocations ([0025] resources, e.g., CPU, memory; [0026] cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations. Please note that resource allocations being performed where resources can be CPU and/or memory corresponds to Applicant’s computing resource allocations comprising at least one of RAM, CPU allocations.).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 20150341223 A1) in view of Gaber et al. (US 20200387584 A1), and further in view of Chan et al. (US 20140289382 A1), as applied to Claim 1 above, and further in view of Argenti et al. (US 9690622 B1), hereinafter referred to as Shen, Gaber, Chan, and Argenti, respectively.
Regarding Claim 5, Shen-Gaber-Chan as disclosed in Claim 1 does not explicitly disclose prior to determining the different groups of users, determine a number of groups to divide the users into based upon a heuristic algorithm.
However, Argenti discloses prior to determining the different groups of users, determine a number of groups to divide the users into based upon a heuristic algorithm. (Col. 23, Lines 27-29- Placement schemes may include […] heuristics based on resource usage. Please note that placement schemes including heuristics based on resource usage corresponds to Applicant’s determining a number of groups to divide the users into based upon a heuristic algorithm, as it would be obvious to a person of ordinary skill in the art to apply this placement scheme to the user groupings to determine a number of groups to divide.)
Shen-Gaber-Chan and Argenti are both considered to be analogous to the claimed invention because they are in the same field of computer resource placement determination. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan to incorporate the teachings of Argenti to modify the grouped user usage-based resource scaling system to use a heuristic algorithm to determine the number of groups to divide the users into, allowing for better placement and thus improved resource usage, as described in Argenti.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 20150341223 A1) in view of Gaber et al. (US 20200387584 A1), and further in view of Chan et al. (US 20140289382 A1), as applied to Claim 1 above, and further in view of Boucher et al. (US 20170315979 A1), hereinafter referred to as Shen, Gaber, Chan, and Boucher, respectively.
Regarding Claim 21, Shen-Gaber-Chan as disclosed in Claim 1 does not explicitly disclose wherein an application priority comprises a thread priority of a corresponding application.
However, Boucher discloses wherein an application priority comprises a thread priority of a corresponding application ([1468] threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. Please note that threads being executed based on priority from instructions provided in the program code corresponds to Applicant’s application priority comprising a thread priority of a corresponding application, as the corresponding application, containing the instructions, provides the thread priority.).
Shen-Gaber-Chan and Boucher are both considered to be analogous to the claimed invention because they are in the same field of computer resource management for users. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan to incorporate the teachings of Boucher to modify the grouped user usage-based resource scaling system to have the application priority comprise a thread priority of a corresponding application, allowing for improved system performance based on the priorities, as described in Boucher.
Claims 6, 10-12, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 20150341223 A1) in view of Gaber et al. (US 20200387584 A1), further in view of Chan et al. (US 20140289382 A1), and further in view of Calmon et al. (US 20210064436 A1), hereinafter referred to as Shen, Gaber, Chan, and Calmon, respectively.
Regarding Claim 6, Shen-Gaber-Chan as disclosed in Claim 1 does not explicitly disclose determine the computing resource allocations based upon a discriminative model and the usage activity data.
However, Calmon discloses determine the computing resource allocations based upon a discriminative model and the usage activity data ([0131] determines an initial allocation of an amount of a resource for the workload based on a regression model characterizing behavior of the workload, the data. Please note that a regression model corresponds to Applicant’s discriminative model, determining an allocation of an amount of a resource corresponds to Applicant’s determining computing resource allocations, and doing so based on data corresponds to Applicant’s doing so based upon usage activity data.).
Shen-Gaber-Chan and Calmon are both considered to be analogous to the claimed invention because they are in the same field of allocating computer resources. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan to incorporate the teachings of Calmon to modify the grouped user usage-based resource scaling system to use a discriminative model to determine the computing resource allocations, allowing for improved estimation of resource needs and thus improved resource usage, as described in Calmon.
Regarding Claim 10, Shen discloses A method comprising: at a computing device ([0027] physical computers), collecting usage activity data across a plurality of different applications for a plurality of users ([0026] monitored configurations may also include client information. The client information may include […] software programs running on each of the clients […] monitoring the current usage of resources by the clients. Please note that monitoring client information including software programs and the current usage of resources by clients corresponds to Applicant’s collecting usage activity data across a plurality of different applications for a plurality of users.);
for each group of users: determining respective application priorities for the applications based upon the usage activity data for the group of users, ([0048] ranking of the candidate clusters may involve using various information, such as resource utilization metrics. Please note that ranking candidate clusters based on resource utilization metrics corresponds to Applicant’s determining respective application priorities for the applications for each group of users based upon the usage activity data for the group, as the ranking system could be applied to applications in the clusters as well.);
and determining computing resource allocations for each of the prioritized applications based upon ([0026] cluster resource management module operates to allocate available resources among clients running in the cluster based on a number of parameters, which may include […] priorities. Please note that allocating available resources among clients running in the cluster based on priorities corresponds to Applicant’s determining computing resource allocations for each of the prioritized applications.),
wherein the computing resource allocations comprise at least one of RAM. CPU. or I/O port allocations ([0025] resources, e.g., CPU, memory; [0026] cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations. Please note that resource allocations being performed where resources can be CPU and/or memory corresponds to Applicant’s computing resource allocations comprising at least one of RAM, CPU allocations.);
and running applications for the users within a virtual workspace environment with the computing resource allocations for the respective group of users applied thereto ([0026] cluster management server includes a cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations […] among clients running in the cluster. Please note that clients running in the cluster with resource allocations performed among them by the CRMM corresponds to Applicant’s running applications for the users with computing resource allocations for the respective group of users applied.).
Shen does not explicitly disclose determining different groups of users based upon cluster modeling of the usage activity data;
However, Gaber discloses determining different groups of users based upon cluster modeling of the usage activity data ([0027] The persona clustering module 320 may employ, for example, K-means clustering and unsupervised learning, in order to associate users into different usage categories.; [0028] In some embodiments, unsupervised learning techniques are employed by the persona clustering module 320 as the outcome is unknown but patterns can be found in the usage data. Please note that the persona clustering module 320 using K-means clustering, a form of cluster modeling known in the art, to associate users into different usage categories based on patterns found in the usage data corresponds to Applicant’s determining different groups of users based upon cluster modeling of the usage activity data.),
Shen and Gaber are both considered to be analogous to the claimed invention because they are in the same field of grouping components of computer systems based on criteria for performance benefits. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen to incorporate the teachings of Gaber to modify the scaling usage-based resource allocation system to group users based on cluster modeling of the usage activity data, allowing for efficient automated resource allocation to groups based on patterns of usage, as described in Gaber.
Shen-Gaber does not explicitly disclose wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application
However, Chan discloses wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application ([0045] the usage patterns of a group of users can be determined; [0045] information about this user's usage pattern can help prioritize tasks, e.g. which applications are less important; [0047] Another aspect of this technology is to utilize the usage patterns of a group of users to optimize performance at a holistic, service provider level. In one embodiment, when the version vectors data is generated for a group of users, a service provider can mine the data to obtain usage patterns of the group, which enables service optimization in many different ways. Please note that determining the usage patterns of a group of users, and using usage pattern information to prioritize tasks, such as which applications are less important, corresponds to Applicant’s higher application priority indicating a higher level of usage of the corresponding application by the group of users, as a more utilized application by a group of users would have a higher application priority in this system. )
Shen-Gaber and Chan are both considered to be analogous to the claimed invention because they are in the same field of computer resource management for users. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber to incorporate the teachings of Chan to modify the grouped user usage-based resource scaling system to utilize the usage patterns of groups of users for corresponding applications to indicate a higher application priority, allowing for service optimization and thus improved resource usage, as described in Chan.
Shen-Gaber-Chan does not explicitly disclose a discriminative model trained using historical user feedback data and the application usage activity data
However, Calmon discloses a discriminative model trained using historical user feedback data and the application usage activity data([0131] determines an initial allocation of an amount of a resource for the workload based on a regression model characterizing behavior of the workload, the data. Please note that a regression model corresponds to Applicant’s discriminative model, determining an allocation of an amount of a resource corresponds to Applicant’s determining computing resource allocations, and doing so based on data, with the regression model characterizing behavior of the workload, corresponds to Applicant’s doing so based upon historical user feedback data and the application usage activity data, as they are data characterizing the behavior of the workload.)
Shen-Gaber-Chan and Calmon are both considered to be analogous to the claimed invention because they are in the same field of allocating computer resources. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan to incorporate the teachings of Calmon to modify the grouped user usage-based resource scaling system to use a discriminative model to determine the computing resource allocations, allowing for improved estimation of resource needs and thus improved resource usage, as described in Calmon.
Regarding Claim 11, Shen-Gaber-Chan-Calmon as disclosed in Claim 10, Shen further discloses associating new users with an existing group of users based upon user job descriptions ([0047] a client placement algorithm to select a placement cluster from a list of candidate clusters, which are determined to satisfy [...] policy requirements; [0050] An initial client placement occurs when a client is first deployed in the distributed computer system 100. Please note that an initial client placement into a placement cluster from a list of candidate clusters based on policy requirements corresponds to Applicant’s associating new users with an existing group of users based upon user job descriptions, as a user job description being used for association is an instance of a policy requirement.); and moving the new users between existing groups of users over time based upon usage activity for the new users ([0043] A subsequent client placement occurs when changes in policy or condition of a client or its environment necessitate a new placement location for the client. Please note that a subsequent client placement occurring based on changes in policy or condition of a client corresponds to Applicant’s moving users between existing groups of users over time based upon usage activity, as the condition of the client corresponds to Applicant’s user activity as a criterion to use in moving users between existing groups.).
Regarding Claim 12, Shen-Gaber-Chan-Calmon as disclosed in Claim 10, Gaber further discloses the cluster modeling comprises K-means clustering modeling ([0027] The persona clustering module 320 may employ, for example, K-means clustering and unsupervised learning, in order to associate users into different usage categories. Please note that K-means clustering being used by the persona clustering module 320 to associate users into different usage categories corresponds to Applicant’s cluster modeling comprising K-means cluster modeling.)
Regarding Claim 16, Shen discloses A non-transitory computer-readable medium having computer-executable instructions for causing a computing device to perform steps ([0062] computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system) comprising: collecting usage activity data across a plurality of different applications for a plurality of users ([0026] monitored configurations may also include client information. The client information may include […] software programs running on each of the clients […] monitoring the current usage of resources by the clients. Please note that monitoring client information including software programs and the current usage of resources by clients corresponds to Applicant’s collecting usage activity data across a plurality of different applications for a plurality of users.);
for each group of users: determining respective application priorities for the applications based upon the usage activity data for the group of users ([0048] ranking of the candidate clusters may involve using various information, such as resource utilization metrics. Please note that ranking candidate clusters based on resource utilization metrics corresponds to Applicant’s determining respective application priorities for the applications for each group of users based upon the usage activity data for the group, as the ranking system could be applied to applications in the clusters as well.);
and determining computing resource allocations for each of the prioritized applications based upon ([0026] cluster resource management module operates to allocate available resources among clients running in the cluster based on a number of parameters, which may include […] priorities. Please note that allocating available resources among clients running in the cluster based on priorities corresponds to Applicant’s determining computing resource allocations for each of the prioritized applications.),
wherein the computing resource allocations comprise at least one of RAM,CPU, or I/O port allocations([0025] resources, e.g., CPU, memory; [0026] cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations. Please note that resource allocations being performed where resources can be CPU and/or memory corresponds to Applicant’s computing resource allocations comprising at least one of RAM, CPU allocations.);
and running applications for the users within a virtual workspace environment with the computing resource allocations for the respective group of users applied thereto ([0026] cluster management server includes a cluster resource management module (CRMM) 118, which can be enabled by a user, to perform resource allocations […] among clients running in the cluster; [0027] In other embodiments, the cluster management servers may be implemented as software programs running on […] virtual computers. Please note that clients running in the cluster with resource allocations performed among them by the CRMM corresponds to Applicant’s running applications for the users with computing resource allocations for the respective group of users applied. Furthermore, the cluster management server running on virtual computers corresponds to Applicant’s applications for the users being run within a virtual workspace environment.).
Shen does not explicitly disclose determining different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data, wherein the usage activity data comprises at least one or more of user mouse clicks, keystrokes, CPU usage, network traffic, or application window activity.
However, Gaber discloses determining different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data ( [0027] The persona clustering module 320 may employ, for example, K-means clustering and unsupervised learning, in order to associate users into different usage categories.; [0028] In some embodiments, unsupervised learning techniques are employed by the persona clustering module 320 as the outcome is unknown but patterns can be found in the usage data. Please note that using K-means clustering and unsupervised learning techniques to associate users into different usage categories based on patterns found in the usage data corresponds to Applicant’s determining different groups of users based upon unsupervised cluster modeling comprising K-means clustering of the usage activity data.),
wherein the usage activity data comprises user mouse clicks, keystrokes, CPU usage, ([0026] the exemplary software license optimization system 300 processes a number of exemplary KPIs 310-1 through 310-j, such as CPU utilization 310-1 […] keyboard activity 310-4 […] mouse activity. The various KPIs 310 reflect actual usage of the software product for individual users. Please note that processing collected KPIs such as CPU utilization, keyboard activity, and mouse activity reflecting usage for individual users corresponds to Applicant’s usage activity data comprising user mouse clicks, keystrokes, and CPU usage, as the KPIs are then used in the clustering system.)
Shen and Gaber are both considered to be analogous to the claimed invention because they are in the same field of grouping users in computer systems based on usage activity data for optimization. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen to incorporate the teachings of Gaber to modify the scaling usage-based resource allocation system to group users based on unsupervised K-means clustering of the usage activity data, where the usage activity comprises user mouse clicks and keystrokes, allowing for efficient automated resource allocation to groups based on patterns of usage and detailed data, as described in Gaber.
Shen-Gaber does not explicitly disclose wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application
However, Chan discloses wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application ([0045] the usage patterns of a group of users can be determined; [0045] information about this user's usage pattern can help prioritize tasks, e.g. which applications are less important; [0047] Another aspect of this technology is to utilize the usage patterns of a group of users to optimize performance at a holistic, service provider level. In one embodiment, when the version vectors data is generated for a group of users, a service provider can mine the data to obtain usage patterns of the group, which enables service optimization in many different ways. Please note that determining the usage patterns of a group of users, and using usage pattern information to prioritize tasks, such as which applications are less important, corresponds to Applicant’s higher application priority indicating a higher level of usage of the corresponding application by the group of users, as a more utilized application by a group of users would have a higher application priority in this system. )
Shen-Gaber and Chan are both considered to be analogous to the claimed invention because they are in the same field of computer resource management for users. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber to incorporate the teachings of Chan to modify the grouped user usage-based resource scaling system to utilize the usage patterns of groups of users for corresponding applications to indicate a higher application priority, allowing for service optimization and thus improved resource usage, as described in Chan.
Shen-Gaber-Chan does not explicitly disclose a discriminative model trained using historical user feedback data and the application usage activity data
However, Calmon discloses a discriminative model trained using historical user feedback data and the application usage activity data([0131] determines an initial allocation of an amount of a resource for the workload based on a regression model characterizing behavior of the workload, the data. Please note that a regression model corresponds to Applicant’s discriminative model, determining an allocation of an amount of a resource corresponds to Applicant’s determining computing resource allocations, and doing so based on data, with the regression model characterizing behavior of the workload, corresponds to Applicant’s doing so based upon historical user feedback data and the application usage activity data, as they are data characterizing the behavior of the workload.)
Shen-Gaber-Chan and Calmon are both considered to be analogous to the claimed invention because they are in the same field of allocating computer resources. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan to incorporate the teachings of Calmon to modify the grouped user usage-based resource scaling system to use a discriminative model to determine the computing resource allocations, allowing for improved estimation of resource needs and thus improved resource usage, as described in Calmon.
Regarding Claim 17, Shen-Gaber-Chan-Calmon as disclosed in Claim 16, Shen further discloses associating new users with an existing group of users based upon user job descriptions ([0047] a client placement algorithm to select a placement cluster from a list of candidate clusters, which are determined to satisfy [...] policy requirements; [0050] An initial client placement occurs when a client is first deployed in the distributed computer system 100. Please note that an initial client placement into a placement cluster from a list of candidate clusters based on policy requirements corresponds to Applicant’s associating new users with an existing group of users based upon user job descriptions, as a user job description being used for association is an instance of a policy requirement.); and moving the new users between existing groups of users over time based upon usage activity for the new users ([0043] A subsequent client placement occurs when changes in policy or condition of a client or its environment necessitate a new placement location for the client. Please note that a subsequent client placement occurring based on changes in policy or condition of a client corresponds to Applicant’s moving users between existing groups of users over time based upon usage activity, as the condition of the client corresponds to Applicant’s user activity as a criterion to use in moving users between existing groups.).
Claims 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 20150341223 A1) in view of Gaber et al. (US 20200387584 A1), further in view of Chan et al. (US 20140289382 A1), and further in view of Calmon et al. (US 20210064436 A1), as applied to Claims 10 and 16 above, and further in view of Argenti et al. (US 9690622 B1), hereinafter referred to as Shen, Gaber, Chan, Calmon, and Argenti, respectively.
Regarding Claim 13, Shen-Gaber-Chan -Calmon as disclosed in Claim 10 does not explicitly disclose prior to determining the different groups of users, determining a number of groups to divide the users into based upon a heuristic algorithm.
However, Argenti discloses prior to determining the different groups of users, determining a number of groups to divide the users into based upon a heuristic algorithm. (Col. 23, Lines 27-29- Placement schemes may include […] heuristics based on resource usage. Please note that placement schemes including heuristics based on resource usage corresponds to Applicant’s determining a number of groups to divide the users into based upon a heuristic algorithm, as it would be obvious to a person of ordinary skill in the art to apply this placement scheme to the user groupings to determine a number of groups to divide.)
Shen-Gaber-Chan-Calmon and Argenti are both considered to be analogous to the claimed invention because they are in the same field of computer resource placement determination. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan -Calmon to incorporate the teachings of Argenti to modify the grouped user usage-based resource scaling system to use a heuristic algorithm to determine the number of groups to divide the users into, allowing for better placement and thus improved resource usage, as described in Argenti.
Regarding Claim 19, Shen-Gaber-Chan-Calmon as disclosed in Claim 16 does not explicitly disclose prior to determining the different groups of users, determining a number of groups to divide the users into based upon a heuristic algorithm.
However, Argenti discloses prior to determining the different groups of users, determining a number of groups to divide the users into based upon a heuristic algorithm. (Col. 23, Lines 27-29- Placement schemes may include […] heuristics based on resource usage. Please note that placement schemes including heuristics based on resource usage corresponds to Applicant’s determining a number of groups to divide the users into based upon a heuristic algorithm, as it would be obvious to a person of ordinary skill in the art to apply this placement scheme to the user groupings to determine a number of groups to divide.)
Shen-Gaber-Chan-Calmon and Argenti are both considered to be analogous to the claimed invention because they are in the same field of computer resource placement determination. Therefore, it would have been obvious to someone of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Shen-Gaber-Chan-Calmon to incorporate the teachings of Argenti to modify the grouped user usage-based resource scaling system to use a heuristic algorithm to determine the number of groups to divide the users into, allowing for better placement and thus improved resource usage, as described in Argenti.
Response to Arguments
Applicant's arguments filed 01/15/2026 have been fully considered but they are not persuasive.
Applicant’s arguments are summarized as follows:
Regarding amended Claim 1, Shen-Gaber does not disclose “determine respective application priorities for the applications based upon the usage activity data for the group of users, wherein a higher application priority indicates a higher level of usage, by the group of users, of the corresponding application.” Shen describes ranking candidate clusters, which is not equivalent to the ranking of applications, wherein a higher application priority indicates a higher level of usage of the corresponding application by the group of users. Shen’s cluster has nothing to do with application priority or the level of usage of the application. Gaber does not cure this deficiency. Therefore, Claim 1 is allowable under 35 U.S.C. 103.
Shen-Gaber additionally fails to disclose “determine computing resource allocations for each of the prioritized applications based upon the respective application priority” as stated by Claim 1. Shen does not disclose ranking (prioritizing) applications, nor does it disclose determining computing resource allocations for each of the prioritized applications, because it merely discloses allocating available resources among clients, not for each of the prioritized applications. Gaber does not cure this deficiency. Therefore, Claim 1 is allowable under 35 U.S.C. 103.
Independent Claims 10 and 16 contain similar limitations to Claim 1, and are therefore allowable for similar reasons.
Dependent Claims 2, 3, 5-7, 11-13, 17, and 19 depend from respective allowable independent Claims, and are therefore allowable.
Regarding A), Applicant’s arguments with respect to the rejection of independent Claim 1 under 35 U.S.C. 103 has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made with Shen-Gaber in view of Chan et al., applied to teach the amended matter specifically challenged in the argument.
As stated above, the combination of references addressing the amended claims discloses all of the recited features mentioned above. Chan discloses determining a higher application priority based on a higher level of usage of the corresponding application by the group of users. The cited references are in the same field of endeavor, making it obvious for a person of ordinary skill in the art to arrive at a system combining their teachings to contain the recited features.
Regarding B), Applicant’s arguments with respect to the rejection of independent Claim 1 under 35 U.S.C. 103 has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made with Shen-Gaber in view of Chan et al.
As stated above, the combination of references addressing the amended claims discloses all of the recited features mentioned above. Chan discloses the prioritization of applications based on user usage activity data, which may be used in the system in combination with the teachings of Shen to achieve computing resource allocation determination for each of the prioritized applications. The cited references are in the same field of endeavor, making it obvious for a person of ordinary skill in the art to arrive at a system combining their teachings to contain the recited features.
Regarding C), the examiner respectfully disagrees. Independent Claim 1 remains rejected for the reasons stated above, and the combinations cited would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the application. Therefore, contrary to Applicant’s arguments, because independent Claims 10 and 16 contain similar limitations to unpatentable Claim 1 and do not add limitations that overcome the rejection, they likewise remain rejected, and the application is not in condition for allowance.
Regarding D), the examiner respectfully disagrees. Independent claims 1, 10, and 16 remain rejected for the reasons stated above, and the combinations cited would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the application. Therefore, contrary to Applicant’s arguments, because the dependent claims 2, 3, 5-7, 11-13, 17, and 19 depend on unpatentable claims and do not add limitations that overcome the rejection, they likewise remain rejected, and the application is not in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ma et al. (US20200206920) discloses recording user interactions with applications such as UI actions including mouse clicks, keyboard strokes, interactions with a GUI, and the content of an application, clustering users based on the interactions, and prioritizing processes based on the clusters (see [0020-0023, 0030, 0066, 0072]).
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 FARAZ T AKBARI whose telephone number is (571)272-4166. The examiner can normally be reached Monday-Thursday 9:30am-7:30pm ET.
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/FARAZ T AKBARI/ Examiner, Art Unit 2196
/APRIL Y BLAIR/ Supervisory Patent Examiner, Art Unit 2196