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 .
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 1-9 are rejected under 35 U.S.C. 101 because:
At step 1:
Claims 1-9 is directed to a “variable allocation device and variable allocation method” and thus directed to a statutory category.
At step 2A, Prong One:
The claim recites the following limitation directed to an abstract ideas:
“A variable allocation method for allocating variables of a combinatorial optimization problem to ordered multiple parallel processing devices which are allocated the variables of the combinatorial optimization problem, and perform a process of obtaining values of allocated variables in parallel” recites a mental process as allocating variables of a combinatorial optimization problem to ordered multiple parallel processing devices which are allocated the variables of the combinatorial optimization problem, and perform a process of obtaining values of allocated variables in parallel
“allocating, for each of sets of variables for which a constraint is defined, variables belonging to the set to any one of the parallel processing devices, and after allocating all variables to any parallel processing converting indices of the variables allocated to parallel processing devices according to order of the parallel processing devices” recites a mental process as allocating, for each of sets of variables for which a constraint is defined, variables belonging to the set to any one of the parallel processing devices according to order of the parallel processing devices, .
“converting a matrix used in an evaluation function of the combinatorial optimization problem according to converted indices of the variables” recites the mental process converting a matrix used in an evaluation function of the combinatorial optimization problem according to converted indices of the variables
With respect to claims 2-8, claims 2-8 recites all abstract ideas as and can be processing as mental process.
At step 2A, Prong Two:
The claims recite the following additional elements:
That the content management system includes “memory” “processor”, which are high level recitation of generic computer component s and functions and represent mere instruction to apply to a computer as in MPEP 2106.05 (f) which does not provide integration into a practical application.
At step 2B
The conclusions for the mere implementation using a generic computer and mere field of use are carried over and to not provide significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claim 4 the phrase " can be allocated " renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "or the like"), thereby rendering the scope of the claim(s) unascertainable.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5 and 8-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (U.S. pat. 9,325,585 B1).
With respect to claims 1, and 8-9. Wang et al. discloses a variable allocation device for allocating variables of a combinatorial optimization problem to ordered multiple parallel processing devices which are allocated the variables of the combinatorial optimization problem (i.e.,” One or more objective functions, one or more constraints and one or more optimization algorithms may be utilized to achieve optimal allocation of resources to maximize the delivered value of an objective function and/or the system.”(abstract) and “Although techniques exist to allocate shared resources to meet concurrent demands of applications and/or processes and/or clients, there are several disadvantages to current techniques”(col. 4,lines 62-67) and “The term QoS contract may generally refer to a defined or agreed-upon set of constraints on QoS parameters, such as response time, throughput, and availability of services and/or resources”(col. 6, lines 17-20)), and perform a process of obtaining values of allocated variables in parallel (i.e., “The goal of an autonomous and adaptive resource management system (QoS-managed system) may be to optimize the objective function to achieve the highest delivered value, based on concurrent demands, system performance, available resources and mission objectives”(col. 16, lines 7-13)),
wherein the variable allocation device comprises (i.e., “The variable ω denotes the total available fraction of resources to be allocated to all demands whose indices are in set S.”(col. 16, lines 61-65)):
a memory configured to store instructions; and
a processor configured to execute the instructions to:
allocate, for each of sets of variables for which a constraint is defined variables belonging to the set to any one of the parallel processing devices (“The term QoS contract may generally refer to a defined or agreed-upon set of constraints on QoS parameters, such as response time, throughput, and availability of services and/or resources”(col. 6, lines 17-20) and “The goal of an autonomous and adaptive resource management system (QoS-managed system) may be to optimize the objective function to achieve the highest delivered value, based on concurrent demands, system performance, available resources and mission objectives”(col. 16, lines 7-13), and after allocating all variables to any parallel processing devices (i.e., “in a cloud computing application, thousands or even millions of clients may be requesting resources concurrently.”(col. 28, lines 15-17) and “After formulating an objective function and related constraints, the goal of an autonomous and adaptive resource management system (QoS-managed system) may be to optimize the objective function to achieve the highest delivered value, based on concurrent demands, system performance, available resources and mission objectives. In some embodiments of the present disclosure, a resource manager, for example the Resource Manager Service module 212 of FIG. 2A, may find resource allocations, for example by finding values to w.sub.i in Eq 9, that optimize a relative-to-best-effort objective function, for example using an iterative approach, sometimes referred to as the core algorithm”(col. 16, lines 6-17)) convert indices of the variables allocated to parallel processing devices according to order of the parallel processing devices (i.e., “ The variable ω denotes the total available fraction of resources to be allocated to all demands whose indices are in set S. The value of ω may change during the execution of the core algorithm, when and/or if members are removed from set S.”(col. 16, lines 60-65)); and
convert a matrix used in an evaluation function of the combinatorial optimization problem according to converted indices of the variables (i.e., “At step 506, the algorithm may receive input on the characteristics of client demands including types, to which partitions they are allocated, and QoS characteristics (importance and urgency). At step 508, the demands may be sorted by partitions and by types, for example, organized in a matrix (Eq. 23) [(λ.sub.ij I.sub.ij U.sub.ij)] for i=1, 2, . . . , n, j=1, 2, . . . , p. At step 510, the algorithm may aggregate demands for each of the p partitions of the server cluster by summing up the demands (λ.sub.ij I.sub.ij U.sub.ij) for i=1, 2, . . . , n for each partition j. This can be achieved by adding all elements in each column of the matrix (Eq. 23) to determine aggregated arrival rate, importance, and urgency values for each partition. The resulting matrix is [(λ.sub.1 I.sub.1 U.sub.1), (λ.sub.2 I.sub.2 U.sub.2), . . . , (λ.sub.p I.sub.p U.sub.p)] for demands on partition 1 through partition p. At this point, the first step of the cloud center resource allocation problem may be reduced to a problem formulation that is similar to the relative-to-best-effort problem formulation described earlier in this disclosure.”(col. 22, lines 2-20) and fig. 6B shows step 662 determine optimal resource allocation by solving objective function99999 the total delivered value by the µ and this allocation or fig. 3 shows step 308 evaluation function of the combination optimization problem as claimed invention).
With respect to claim 3, Wang et al. discloses the variable allocation device according to wherein constraints defined for sets of variables have predefined priority on the type of the constraints (i.e., “Current techniques require administrator or user configurations and/or pre-configured application and/or demand profiles of resource utilization. For example, existing resource allocation algorithms use priorities to control the utilization of resources among concurrent applications and/or processes and/or clients.”(col. 4, lines 65-67)), and the processor allocates the variables belonging to the set of the variables for which the constraint is defined to any one of the parallel processing devices according to the priority of the constraints ((i.e., “Current techniques require administrator or user configurations and/or pre-configured application and/or demand profiles of resource utilization. For example, existing resource allocation algorithms use priorities to control the utilization of resources among concurrent applications and/or processes and/or clients.”(col. 4, lines 65-67)),).
With respect to claim 5, The variable allocation device according to any one of wherein the processor, when at least two variables among the variables belonging to the set of the variables for which the constraint is defined have already been allocated to different parallel processing devices, allows the variables belonging to the set to be allocated to different parallel processing devices (i.e., “, the SLA Formulation Service module 204 may formulate one or more objective functions that may allow for the autonomous determination of optimal allocation of resources. In some embodiments, an objective function may be formulated that allows for the determination of maximum resource allocation relative to a best effort resource allocation (sometimes referred to as the relative-to-best-effort objective function). ’(col. 12, lines 8-15) or “The objective function may be designed to handle variations in mission objectives, system architectures, and usage/performance scenarios. The objective function may utilize a tolerance factor α, which constrains desired delays of all applications/services to be less than the bounds defined as (1+α) times of the corresponding delays in a best effort approach”(col. 12, lines 17-22)).
Allowable Subject Matter
Claim 2 is 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 and rewritten to overcome the rejection under 35 USC § 101, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the wherein the processor, when the values of allocated variables are returned from the individual parallel processing devices, converts index of each variable back to index before conversion.
Claim 4 is 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 and rewritten to overcome the rejection under 35 USC § 101, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the wherein the processor allocates the variables belonging to the set of the variables for which the constraint is defined, to the parallel processing device with the largest difference between upper limit of number of variables that can be allocated to the parallel processing device which is defined for the parallel processing device and number of variables which have already been allocated to the parallel processing device.
Claim 6 is 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 and rewritten to overcome the rejection under 35 USC § 101, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the wherein when some of the variables belonging to the set of the variables for which the constraint is defined have already been allocated to only one parallel processing device, and when difference between upper limit of number of variables that can be allocated to the parallel processing device which is defined for the parallel processing device and number of variables which have already been allocated to the parallel processing device is greater than or equal to the number of variables other than some of the variables belonging to the set, the processor allocates the variables other than some of the variables belonging to the set to the parallel processing device.
Claim 7 is 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 and rewritten to overcome the rejection under 35 USC § 101, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the wherein when some of the variables belonging to the set of the variables for which the constraint is defined have already been allocated to only one parallel processing device, and when difference between upper limit of number of variables that can be allocated to the parallel processing means device which is defined for the parallel processing device and number of variables which have already been allocated to the parallel processing device is less than the number of variables other than some of the variables belonging to the set, the processor allows the variables belonging to the set to be allocated to different parallel processing devices.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNG T VY whose telephone number is (571)272-1954. The examiner can normally be reached M-F 8-5.
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/HUNG T VY/Primary Examiner, Art Unit 2163 June 27, 2026
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