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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over LU et al (Fairness-Efficiency Allocation of CPU-GPU Heterogeneous Resources).
As per claim 1, Lu teaches the claimed “real-time method” for “controlling rendering performance and quality of a 3-D software run on a user device by making quantifiable choices by a user” (Lu, Abstract - there is a new challenge in the allocation problem, which is quantifying and optimizing the fairness and efficiency of heterogeneous resources (CPUs and GPUs) required by applications such as cloud gaming… We design an iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences), said user device having at least one graphics processing unit (GPU) and at least one central processing unit (CPU), the method comprising: “launching, a game by the user, to initiate a hardware scan process of the user device in which the user wants to play” (Lu, 1.1 Motivation - Supposing that there is a practical cloud gaming platform, it must include a huge number of users playing different games. To provide satisfying game experience for these users, each client may submit different continuous computation tasks to the cloud system. This large-scale system must include a lot of CPUs, GPUs and other hardware resources to support different kinds of computation requirements. All the submitted tasks may have heterogeneous resource requirements, that is, different task may need different amounts of CPU and GPU resources for its execution); “scanning, a current running setup to assess game features performance on the installed user device hardware” (Lu, 2.1 Scalarization Method and Fairness Metrics - Let the amount of resource i be Ci, and the amount of resource i required by a task in workload be Ri) (The work load Ri with i=1, 2 is game features performance on the installed user equivalent to the claimed sources GPU and CPU); “obtaining a total performance budget for both the GPU and the CPU, reflecting a maximum capacity of the hardware” (Lu, 2.1 Scalarization Method and Fairness Metrics - Let the amount of resource i be Ci, and the amount of resource i required by a task in workload be Ri… Let Ri,j denotes the requirement for the resource i of a task in workload j, and we define the constraints to be
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) (the claim has only two (2) resources CPU and GPU then m=2, Ci where i=1, 2 represent the maximum capacity of the hardware CPU and GPU; furthermore, only one game or workload, then j=1, and the constraints A.xT <= B means the resource performance allocation A.xT is less than or equal the total performance budget B); “checking a current budget allocation for GPU and CPU to see how resources are being used” (Lu, 2.1 Scalarization Method and Fairness Metrics - the constraint
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in which
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); “displaying and running the game with high frame rates and smooth gameplay to enhance user experience, if the allocation is less than the total budget” which is inherent because the required management power A.xT is less than the capacity of the budget resource B then the system performance is at its best, i.e., high frame rate and smooth gameplay; “displaying and running the game with reduced frame rates and performance, as visual fidelity increases, if the allocation is more than the total budget” which is inherent because the required management power A.xT is greater than the capacity of the total budget resource B then the system performance is not at its optimal, i.e., low frame rate and fluctuated gameplay; and “adjusting, by the user, the graphics settings to increase or decrease the allocation” (Lu, 3.1 Algorithm Design - Algorithm 1. The Approximate Allocation Algorithm FEA). It is noted that according to the Fairness-Efficiency Allocation (FEA) algorithm (e.g.,
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), by adjusting different weights of fairness β and efficiency λ respectively, the management power allocation will change accordingly. Thus, it would have been obvious to configure Lu’s method as claimed by adjusting the inputs (e.g., weights of fairness β and efficiency λ) to increase or decrease the allocation. The motivation is to choose an optimal allocation to improve the fairness and efficiency (Lu, 1.2 Contribution).
Claim 2 claims a system based on the method of claim 1; therefore, it is rejected under a similar rationale.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
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/PHU K NGUYEN/ Primary Examiner, Art Unit 2616