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
Claims 2-6, 8-11 and 14 are pending.
This action is response to the application filed on December 23, 2024.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 2-5, and 8-11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vladimirskiy et al (US 20220300305 A1).
With respect to claims 2 and 8, Vladimirskiy et al teaches
data saving system that executes save processing which is a process of saving data received from outside in a specific location ([0009] virtual machine (VM) saving all data and closing all applications scaling in (i.e., scaling down) capacity to make it as non-disruptive as possible to the end-users while at the same time optimizing cost savings. [0013] scaling in virtual desktop environments to provide savings in compute and storage in a public cloud environment); and
a cloud controller that executes scale-out processing which is a process of performing scale-out (in) of the data saving system as necessary ([0009] saving all data and closing all applications. scaling in (i.e., scaling down) capacity to make it as non-disruptive as possible to the end-users while at the same time optimizing cost savings. [0013] scaling in virtual desktop environments to provide costs savings in compute and storage costs in a public cloud environment),
wherein the cloud controller executes the scale-out processing before execution of the save processing by the data saving system for respective save processing ([0016] “Scale Out” means increasing capacity of a host pool by powering on adding additional members session host VMs to the host pool. After the scale out completes, the new virtual desktop sessions will be “landed” on the newly added capacity. “Scale In” means decreasing capacity of a host pool by powering off or removing session host VMs. “Scale Up” means increasing capacity by sizing a VM up (e.g., the number of CPUs and/or GB of RAM). A scale up requires a reboot in order to make the change in capacity. “Scale Down” means decreasing capacity by sizing a VM down), and
the scale-out (in) processing is a process in which the cloud controller does not perform the scale-out (in) when a number of the save processing being executed is more than a reference number of processing set as a reference for performing the scale-out ([0025] scaling logic may control any scale out that takes place in the pooled host pool based on various factors such as: (1) CPU usage; (2) average active sessions per host; (3) available sessions; (4) user experience; (5) event-triggered; (6) machine learning based artificial intelligence triggers; etc. user defined scaling logic may control any scale out in the personal host pool based on various factors such as: (1) work hours power on/off; (2) start VM on logon; etc. [0026] In a first example, the user-defined scaling logic may include an auto-scale trigger based on CPU usage within the system, the scaling logic may be defined such that the system creates a defined number of new hosts when CPU utilization across all hosts exceeds a defined percentage of capacity for a defined duration. Such a command may be “start or create (scale out) up to 1 host (s) if CPU utilization across all hosts exceeds 65% for 5 minutes.” [0027] This scaling logic allows the system to determine when the host pool is “getting busy”. The duration variable allows the system to avoid scaling out when there are momentary CPU spikes and only scale out when there is sustained high CPU activity).
With respect to claims 3 and 9, Vladimirskiy et al teaches scale-out (in) processing is a process in which the cloud controller does not perform the scale-out (in) when a number of the save processing waiting for execution is less than a scale-out number set as a number of the scale-out for one time ([0025] scaling logic may control any scale out that takes place in the pooled host pool based on various factors such as: (1) CPU usage; (2) average active sessions per host; (3) available sessions; (4) user experience; (5) event-triggered; (6) machine learning based artificial intelligence triggers; etc. user defined scaling logic may control any scale out in the personal host pool).
With respect to claims 4 and 10, Vladimirskiy et al teaches scale-out processing is a process in which the cloud controller performs the scale-out by the scale-out number when the number of the save processing being executed is not more than the reference number of processing and the number of the save processing waiting for execution is not less than the scale-out number ([0027] This scaling logic allows the system to determine when the host pool is “getting busy”. The duration variable allows the system to avoid scaling out when there are momentary CPU spikes and only scale out when there is sustained high CPU activity).
With respect to claim 5, Vladimirskiy et al teaches scale-out processing is a process in which the cloud controller does not perform the scale-out when a number of the save processing waiting for execution is less than a scale-out number set as a number of the scale-out for one time ([0016] “Scale Out” means increasing capacity of a host pool by powering on adding additional members session host VMs to the host pool. After the scale out completes, the new virtual desktop sessions will be “landed” on the newly added capacity).
With respect to claim 11, Vladimirskiy et al teaches scale-in processing is a process in which the cloud controller performs the scale-in by a scale-in number set as a number of the scale-in for one time when the number of the save processing being executed is not more than the reference number of processing, the save processing waiting for execution does not exist, and the elapsed time since the finish time of the save processing of which execution is finished last is not shorter than the scale-in reference time ([0025] scaling logic may control any scale out that takes place in the pooled host pool based on various factors such as: (1) CPU usage; (2) average active sessions per host; (3) available sessions; (4) user experience; (5) event-triggered; (6) machine learning based artificial intelligence triggers; etc. user defined scaling logic may control any scale out in the personal host pool based on various factors).
Allowable Subject Matter
Claims 6 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior arty made of record and not relied upon is considered pertinent to applicant’s disclosure.
AGARAWALA et al (US 20190313059 A1) AUGMENTED REALITY COMPUTING ENVIRONMENTS - COLLABORATIVE WORKSPACES.
Considered for teaching for identifying a first user that is participating in an augment reality (AR) meeting space from a first location. A second user participating in the AR meeting space from a second location is identified. A selection of a room configuration for the AR meeting space based on at least one of the first location or the second location is received. The digital canvas is configured in the AR meeting space for at least one of the first user or the second user based on the selected room configuration, wherein a size or shape of the digital canvas is adjusted based on either the first wall or the second wall corresponding to the selected room configurations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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