DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/27/2023, the submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Cancelled Claims
It is noted claims 2-3, 10-11, 18-19, and 22-23 have been cancelled by the applicant and were not considered for examination.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim reads "devices so as to maximise a sum" Examiner used " devices so as to maximize a sum" for examination purposes . Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the claim reads "a problem to maximise" Examiner used " a problem to maximize" for examination purposes . Appropriate correction is required.
Claim 9 is objected to because of the following informalities: the claim reads "wherein one or both the first constraint comprises Σu bu,k ≤ 1, ∀k∈ N and the second constraint comprises mu≤bu,kmu,k+(1−bu,k)mM, ∀u, ∀k" examiner used "wherein the first constraint comprises …" for examination purposes . Appropriate correction is required.
Claim 20 is objected to because of the following informalities: the claim reads "devices so as to maximise a sum" Examiner used " devices so as to maximize a sum" for examination purposes . Appropriate correction is required.
Claim 21 is objected to because of the following informalities: the claim reads "devices so as to maximise a sum" Examiner used " devices so as to maximize a sum" for examination purposes . Appropriate correction is required.
Claim 25 is objected to because of the following informalities: the claim reads "a problem to maximise" Examiner used " a problem to maximize" for examination purposes . Appropriate correction is required.
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, 8, 12-16, 20-21 and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mathematical concept performed in a computer environment . This judicial exception is not integrated into a practical application because the claim recites the solution of a mathe. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the core process consist of running an algorithm to solve for its solution, similar to solving an equation or mathematical formula.
Regarding Claim 1, recites:
A method of determining allocation of resources in a wireless network, the method comprising: defining an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network, the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units, the utility function comprising an objective function for the ILP problem; expressing determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO, problem, comprising expressing the ILP problem as the QUBO problem; and executing the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula (ILP to QUBO conversion and solving the QUBO problem) in a computer environment, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 8, recites:
the method of claim 6, wherein bu,k, u=u1, . . . , uU, k=k1, . . . , kN indicates a solution to the ILP problem.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 12, recites:
The method of claim 9, wherein converting the objective function of the ILP problem into the expression containing only binary variables comprises expressing each mu as one of the binary variables
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment, a process that falls under mathematical concept and metal processes that would be performed with the aid of pen and paper solved in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 13, recites:
The method of claim 9, comprising transforming the constraint Eubu,k<1, ∀k∈N to a quadratic penalty function Σi=1,U≥j>i U−1P(xN(i−1)+1xN(j−1)+1).
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 14, recites:
The method of claim 9, wherein transforming the inequality constraint mu<bu,kmu,k + (1 - bu,k)mM, VU,Vkto a quadratic penalty function comprises: adding a slack variable si, i E [1, UN] to the left side of the inequality constraint and expressing slack variable in terms of binary variables to convert the inequality constraint to a matrix equation form; and converting the matrix equation form to a quadratic penalty function using the term (Ax - b)2
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 15, recites:
The method of claim 1, comprising allocating the resources in the wireless network according to a result of executing the QUBO problem on the quantum computing device.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 16, recites:
The method of claim 1, wherein executing the QUBO problem on the quantum computing device comprises performing a quantum annealing process.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a series of steps and therefore it is a process.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula (mathematical concept), therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation (QUBO problem) to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 20, recites:
A non-transitory computer readable media having stored thereon a computer program according to claim 18 comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method of determining allocation of resources in a wireless network, the method comprising: defining an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network, the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units, the utility function comprising an objective function for the ILP problem; expressing determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO, problem, comprising expressing the ILP problem as the QUBO problem; and executing the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites a non-transitory computer readable media containing a series of steps and therefore it is a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the instructions for the solution of a formula (ILP to QUBO conversion and solving the QUBO problem) in a computer environment, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 21, recites:
An apparatus for determining allocation of resources in a wireless network, the apparatus comprising a processor and a memory, the memory containing instructions executable by the processor such that the apparatus is operable to: define an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network, the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units, the utility function comprising an objective function for the ILP problem; express determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO problem, comprising expressing the ILP problem as the QUBO problem; and execute the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network.
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites an apparatus, that follows a series of steps therefore it is a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the instructions for the solution of a formula (ILP to QUBO conversion and solving the QUBO problem) in a computer environment, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Regarding Claim 28, recites:
The apparatus of claim 26, wherein bu,k,u-u1,...,uu, k =ki, ..., kN indicates a solution to the ILP problem
Step 1: Do the claims fall within one of the statutory categories? Yes, the claim recites an apparatus that follows a series of steps, therefore it is a machine.
Step-2A (Prong-1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim recites the solution of a formula, therefore it is an abstract idea.
Step-2A (prong-2): Does the claim recite additional element that integrate the judicial exception into a practical application? No, the claim amounts to no more than solving an equation to determine a solution in a computer environment.
Step-2B: Does the claim provide an inventive concept? No, as discussed with Step-2A (prong-2), the elements of the claim amount to no more than instructions to apply the exception in a computer environment.
Therefore, the claim is not patent eligible as the method is not tied a hardware improvement or to the function of the quantum annealer, only solves for the solution of the equation. See, Digitech Image Techs., LLC v. Electronics for Imaging and Gottschalk v. Benson where courts recognized that a mathematical formula (namely an algorithm) on a physical machine was not a patentable application of that principle.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-5, 9, 12-17, 20-21, and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (WO-2020227721-A1) hereinafter Kim, in view of Glover et al. (arXiv:1811.11538) hereinafter Glover and further in view of Davydov et al. (US-20200351927-A1) hereinafter Davydov.
Regarding Claim 1, Kim discloses a method of determining allocation of resources in a wireless network (Kim, par. 6; a MIMO system comprises a receiver operable to receive a plurality of spatially multiplexed data streams), the method comprising: defining an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network (Kim, par. 8; to decode the spatially multiplexed data streams via the embedded ML to detect data bits of a plurality of users), the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units the utility function comprising an objective function for the ILP problem (Kim, par. 25; A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)); expressing determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO, problem, comprising expressing the ILP problem as the QUBO problem; and executing the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network (Kim, par. 35; This objective function may comprise a quadratic polynomial binary variables and exists in two equivalent forms - an Ising spin glass form and a quadratic unconstrained binary optimization (QUBO) form).
Kim does not explicitly disclose an integer liner programming (ILP) to convert to QUBO form, however, Glover discloses a method for converting linear constraints to QUBO form (Glover, pg. 18, lns. 18-20; Transformation #1 can be employed whenever we need to convert linear constraints of the form 𝐴𝑥=𝑏 into usable quadratic penalties in our efforts to re-cast a given problem with equality constraints into the QUBO form).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form to enhance wireless throughput in a MU-MIMO network.
Kim does not explicitly disclose a method for allocation of resources in a wireless method, however, Davidov discloses a method for scheduling resource allocation in a MIMO network (Davydov, par. 25; RNC (radio network controller functions) such as radio bearer management, uplink and downlink dynamic radio resource management and data packet scheduling, and mobility management).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form and Davydov wireless resource scheduling allocation method to provide bandwidth and acceptable response times to users of the wireless network and enhance wireless resource allocation in a MU-MIMO network.
Regarding Claim 4, the combination of Kim, Glover and Davydov further teach, the method of claim 1, wherein the value of the utility function for a wireless device for a subset of the available scheduling units is based on a maximum modulation and coding scheme (Davydov par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) throughput for the wireless device (Kim, par. 25; A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)) for the subset of available scheduling units (Kim, par. 127; the resource allocation size may be the number of scheduled resource blocks. In some embodiments, the resource allocation size for TBS calculation may be changed depending on the number of the CSI-RS antenna ports).
Regarding Claim 5, the combination of Kim, Glover and Davydov further teach the method of claim 4, wherein the maximum modulation and coding scheme throughput for the wireless device for the subset of available scheduling units is selected from a
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wherein mu,k indicates a maximum modulation and coding scheme throughput for wireless device u and scheduling unit k, u=u1, . . . , uU and k=k1, . . . , kN (Kim, par. 46-19; for BPSK modulation and given a channel matrix and vector of received signals, the quantum annealer 16 may obtain the following Ising model parameters … The Ising spin glass form may be generalized using Ising model parameters. In this regard, the quantum optimizer 16 may insert the given channel H and the signal y received by the receiver 12 without requiring any computationally expensive operations), examiner notes, Glover further teaches the use of binary matrix factorization with a D-Wave quantum annealer to facilitate the solution of QUBO models (Glover, pg. 35; investigates nonnegative/binary matrix factorization with a D-Wave quantum annealer. An application of QUBO to unsupervised machine learning in Glover et al. (2018) provides an approach that can be employed either together with quantum computing or independently).
Regarding Claim 9, the combination of Kim, Glover and Davydov further teach the method of claim 1, wherein expressing the ILP problem as the QUBO problem comprises: negating coefficients of the objective function of the ILP problem; converting the objective function of the ILP problem into an expression containing only binary variables; transforming one or more constraints of the objective function into an unconstrained form using quadratic penalty functions, including converting any constraints in the form of linear inequalities to a general matrix equation form Ax= b (Glover, pg. 17; These constrained quadratic optimization models are converted into equivalent unconstrained QUBO models by converting the constraints 𝐴𝑥 = 𝑏 (representing slack variables as x variables) into quadratic penalties to be added to the objective function, following the same re-casting as we illustrated in section 4), where A is a matrix (Glover, pg. 17; This model accommodates both quadratic and linear objective functions since the linear case results when C is a diagonal matrix (observing that 𝑥 = 𝑥 when 𝑥 is a 0-1 variable)) containing coefficients of binary variables in the column vector x, and b is a column vector containing the constants in the system of linear equations (Glover, pg. 17; This model accommodates both quadratic and linear objective functions since the linear case results when C is a diagonal matrix), wherein one or both the first constraint comprises Σ u b u,k ≤1, ∀k∈ and the second constraint comprises m u ≤b u,k m u,k +(1−b u,k )m M , ∀u, ∀k (Glover, pg. 17; Under the assumption that A and b have integer components, problems with inequality constraints can always be put in this form by including slack variables and then representing the slack variables by a binary expansion. (For example, this would introduce a slack variable s to convert the inequality 4𝑥 + 5𝑥 −𝑥 ≤ 6 into 4𝑥+ 5𝑥 −𝑥 +𝑠 =6 , and since clearly 𝑠 ≤ 7 (in case 𝑥 = 1), 𝑠 could be represented by the binary expansion 𝑠 + 2𝑠 + 4𝑠 where 𝑠 ,𝑠 and 𝑠 are additional binary variables); combining the expression containing only binary variables and the penalty functions into a single quadratic expression equivalent to the form x T Qx. determining the matrix Q from the quadratic expression; wherein the binary variables in the expression containing only binary variables represent logical qubits in a problem graph for the quantum computing device (Glover, pg. 17; Specifically, for a positive scalar P, we add a quadratic penalty 𝑃(𝐴𝑥 − 𝑏)(𝐴𝑥 − 𝑏) to the objective function to get y = x’Cx + P(Ax-B)’(AX-b) = x’Cx+x’Dx+C = x’Qx+ c where the matrix D and the additive constant c result directly from the matrix multiplication indicated. Dropping the additive constant, the equivalent unconstrained version of the constrained problem becomes QUBO:min x’Qx, x binary).
Regarding Claim 12, the combination of Kim, Glover and Davydov further teach the method of claim 9 wherein converting the objective function of the ILP problem into the expression containing only binary variables comprises expressing each mu as one of the binary variables (Glover, pg. 17; Under the assumption that A and b have integer components, problems with inequality constraints can always be put in this form by including slack variables and then representing the slack variables by a binary expansion).
Regarding Claim 13, the combination of Kim, Glover and Davydov further teach the method of claim 9 comprising transforming the constraint E u b u,k ⟨1, ∀k∈N to a quadratic penalty function Σ i=1,U≥j⟩i U−1 P(x N(i−1)+1 x N(j−1)+1 ) (Glover, pgs. 17-18; for a positive scalar P, we add a quadratic penalty 𝑃(𝐴𝑥 − 𝑏)(𝐴𝑥 − 𝑏) to the objective function … A suitable choice of the penalty scalar P, as we commented earlier, can always be chosen so that the optimal solution to QUBO is the optimal solution to the original constrained problem. Solutions obtained can always be checked for feasibility to confirm whether or not appropriate penalty choices have been made … another constraint/penalty pair for special recognition that we worked with before in section 4: (xi +xj ≤1)→P(xi xj)).
Regarding Claim 14, the combination of Kim, Glover and Davydov further teach the method of claim 9, wherein transforming the inequality constraint m u ⟨b u,k m u,k +(1−b u,k )m M , ∀u, ∀k to a quadratic penalty function comprises: adding a slack variable s i , i∈[1, UN] to the left side of the inequality constraint and expressing slack variable in terms of binary variables to convert the inequality constraint to a matrix equation form; and converting the matrix equation form to a quadratic penalty function using the term (Ax−b)2 (Glover, pgs. 17-18; for a positive scalar P, we add a quadratic penalty 𝑃(𝐴𝑥 − 𝑏)(𝐴𝑥 − 𝑏) to the objective function … A suitable choice of the penalty scalar P, as we commented earlier, can always be chosen so that the optimal solution to QUBO is the optimal solution to the original constrained problem. Solutions obtained can always be checked for feasibility to confirm whether or not appropriate penalty choices have been made … another constraint/penalty pair for special recognition that we worked with before in section 4: (xi +xj ≤1)→P(xi xj)).
Regarding Claim 15, the combination of Kim, Glover and Davydov further teach the method of claim 1, comprising allocating the resources in the wireless network (Kim, par. 87; the UE 102 may determine the size of the transport block taking into account the number of allocated PRBs (physical resource blocks) and scaling factor configured for the UE) according to a result of executing the QUBO problem (Kim, par. 45; the quantum annealer 16 may decode the ML QUBO equation ) on the quantum computing device (Kim, par 25; A MIMO decoding problem with an optimal solution is called the “ML solution” and comprises a search over the sets of transmitted symbols, looking for the set that minimizes the error with respect to what has been received by the WCP 20. The solution may be represented as:
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The processor 14 de-maps the decoded symbols v to decoded bits
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H1 + jHQ is the wireless channel on each OFDM subcarrier and y is a
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received set of symbols perturbed by
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(i.e., additive white Gaussian noise, or “AWGN”). A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)).
Regarding Claim 16, the combination of Kim, Glover and Davydov further teach the method of claim 1, wherein executing the QUBO problem on the quantum computing device comprises performing a quantum annealing process (Glover, pg. 2; The QUBO model has emerged as an underpinning of the quantum computing area known as quantum annealing and Fujitsu's digital annealing, and has become a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and neuromorphic computers developed by IBM).
Regarding Claim 17, the combination of Kim, Glover and Davydov further teach the method of claim 1, wherein the resources in the wireless network comprise resources for wireless communication between the plurality of wireless communication devices and one or more base stations (Kim, par. 21; a receiver 12 that is operable to receive a plurality of spatially multiplexed data streams. For example, the receiver 12 may be part of an MU-MIMO communication system configured with a WCP (e.g., a WAP, a RAN, a cloud RAN, an eNodeB, or the like) to receive such signaling. Thus, the receiver 12 may be configured with or communicatively coupled to a plurality of antennas (e.g., configured as an array) Examiner notes, Kim refers to eNodeBs as base stations, see par. 3.
Regarding Claim 20, Kim discloses a non-transitory computer readable media having stored thereon a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method of determining allocation of resources in a wireless network (Kim, par. 6; a MIMO system comprises a receiver operable to receive a plurality of spatially multiplexed data streams), the method comprising: defining an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network (Kim, par. 8; to decode the spatially multiplexed data streams via the embedded ML to detect data bits of a plurality of users), the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units, the utility function comprising an objective function for the ILP problem (Kim, par. 25; A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)); expressing determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO, problem, comprising expressing the ILP problem as the QUBO problem; and executing the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network (Kim, par. 35; This objective function may comprise a quadratic polynomial binary variables and exists in two equivalent forms - an Ising spin glass form and a quadratic unconstrained binary optimization (QUBO) form).
Kim does not explicitly disclose an integer liner programming (ILP) to convert to QUBO form, however, Glover discloses a method for converting linear constraints to QUBO form (Glover, pg. 18, lns. 18-20; Transformation #1 can be employed whenever we need to convert linear constraints of the form 𝐴𝑥=𝑏 into usable quadratic penalties in our efforts to re-cast a given problem with equality constraints into the QUBO form).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form to enhance wireless throughput in a MU-MIMO network.
Kim does not explicitly disclose a method for allocation of resources in a wireless method, however, Davidov discloses a method for scheduling resource allocation in a MIMO network (Davydov, par. 25; RNC (radio network controller functions) such as radio bearer management, uplink and downlink dynamic radio resource management and data packet scheduling, and mobility management).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form and Davydov wireless resource scheduling allocation method to provide bandwidth and acceptable response times to users of the wireless network and enhance wireless resource allocation in a MU-MIMO network.
Regarding Claim 21, Kim discloses an apparatus for determining allocation of resources in a wireless network, the apparatus comprising a processor and a memory, the memory containing instructions executable by the processor such that the apparatus is operable to (Kim, par. 6; a MIMO system comprises a receiver operable to receive a plurality of spatially multiplexed data streams): define an integer linear programming, ILP, problem for allocation of wireless resources to a plurality of wireless communication devices in the wireless network (Kim, par. 8; to decode the spatially multiplexed data streams via the embedded ML to detect data bits of a plurality of users), the ILP problem comprising a maximization problem of determining a respective subset of available scheduling units for each of the wireless communication devices so as to maximise a sum of values of a utility function for the plurality of wireless devices, the value of the utility function for a wireless device indicating a throughput for that wireless device for the respective subset of available scheduling units, the utility function comprising an objective function for the ILP problem (Kim, par. 25; A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)); express determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization, QUBO, problem, comprising expressing the ILP problem as the QUBO problem; and execute the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network (Kim, par. 35; This objective function may comprise a quadratic polynomial binary variables and exists in two equivalent forms - an Ising spin glass form and a quadratic unconstrained binary optimization (QUBO) form).
Kim does not explicitly disclose an integer liner programming (ILP) to convert to QUBO form, however, Glover discloses a method for converting linear constraints to QUBO form (Glover, pg. 18, lns. 18-20; Transformation #1 can be employed whenever we need to convert linear constraints of the form 𝐴𝑥=𝑏 into usable quadratic penalties in our efforts to re-cast a given problem with equality constraints into the QUBO form).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form to enhance wireless throughput in a MU-MIMO network.
Kim does not explicitly disclose a method for allocation of resources in a wireless method, however, Davidov discloses a method for scheduling resource allocation in a MIMO network (Davydov, par. 25; RNC (radio network controller functions) such as radio bearer management, uplink and downlink dynamic radio resource management and data packet scheduling, and mobility management).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective date of the claimed invention to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form and Davydov wireless resource scheduling allocation method to provide bandwidth and acceptable response times to users of the wireless network and enhance wireless resource allocation in a MU-MIMO network.
Regarding Claim 24, the combination of Kim, Glover and Davydov further teach, the apparatus of claim 21, wherein the value of the utility function for a wireless device for a subset of the available scheduling units is based on a maximum modulation and coding scheme (Davydov par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) throughput for the wireless device (Kim, par. 25; A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)) for the subset of available scheduling units (Kim, par. 127; the resource allocation size may be the number of scheduled resource blocks. In some embodiments, the resource allocation size for TBS calculation may be changed depending on the number of the CSI-RS antenna ports).
Regarding Claim 25, the combination of Kim, Glover and Davydov further teach the apparatus of claim 24, wherein the maximum modulation and coding scheme throughput for the wireless device for the subset of available scheduling units is selected from a
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wherein mu,k indicates a maximum modulation and coding scheme throughput for wireless device u and scheduling unit k, u=u1, . . . , uU and k=k1, . . . , kN (Kim, par. 46-19; for BPSK modulation and given a channel matrix and vector of received signals, the quantum annealer 16 may obtain the following Ising model parameters … The Ising spin glass form may be generalized using Ising model parameters. In this regard, the quantum optimizer 16 may insert the given channel H and the signal y received by the receiver 12 without requiring any computationally expensive operations), examiner notes, Glover further teaches the use of binary matrix factorization with a D-Wave quantum annealer to facilitate the solution of QUBO models (Glover, pg. 35; investigates nonnegative/binary matrix factorization with a D-Wave quantum annealer. An application of QUBO to unsupervised machine learning in Glover et al. (2018) provides an approach that can be employed either together with quantum computing or independently).
Claims 6-8 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (WO-2020227721-A1) hereinafter Kim, in view of Glover et al. (arXiv:1811.11538) hereinafter Glover and further in view of Davydov et al. (US-20200351927-A1) hereinafter Davydov further in view of You et al. (US-11769070-B2) hereinafter You.
Regarding Claim 6, the combination of Kim, Glover and Davydov further teach the method of claim 5, wherein the ILP (Glover, pg. 17; This model accommodates both quadratic and linear objective functions since the linear case results when C is a diagonal matrix) problem is a problem to maximise
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(k∈
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|bu,k=1, mu), where
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is a set comprising the plurality of wireless communication devices,
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is a set of available scheduling units, φu is the utility function for wireless device u, mu indicates a maximum modulation and coding scheme throughput for the wireless device (Davydov par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) for the subset of available scheduling units where k∈
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|bu,k=1, and bu,k is a binary variable that is 1 if scheduling unit k is allocated to wireless device u and a value other than 1 if scheduling unit k is not allocated to the wireless device u (Kim par. 23; The MIMO processing system 10 also includes a processor 14 that is operable to embed a maximum likelihood (ML) detection algorithm onto a quantum annealer or otherwise quantum optimizer 16, and to decode the spatially multiplexed data streams via the embedded ML to detect a plurality of streams of data bits of a plurality of users (i.e., user data streams 1 - N, where the reference “N” indicates an integer greater than“1” and not necessarily equal to any other “N” reference designated herein) examiner notes, Glover discloses methods to transform linear problems to QUBO for minimization problems, it would be obvious for a person of ordinary skill in the art to attempt to solve for maximize problems.
The combination of Kim, Glover and Davydov does not explicitly teach a method to solve for maximize problem, however, You discloses a method for quantum based hybrid solutions for large scale problem optimization with the objective of minimizing or maximizing a function to optimize the problem using QUBO and linear programming (You, pars. 6-7; he first sub-problem is selected from a mixed-integer linear programming (MILP) problem, a relaxed mixed-integer linear programming (MILP) problem, a mixed-integer non-linear programming (MINLP) problem, a linear programming (LP) …. and the second algorithm is selected from algorithms for a quadratic unconstrained binary optimization (QUBO) problem or for an optimization problem solvable by a quantum computer or quantum processor directly and/or independently … The objective of the optimization problem is to minimize or maximize a function comprising a set of variables and a set of parameter).
Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filling date of the claimed invention to combine to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form, Davydov wireless resource scheduling allocation method
Regarding Claim 7, the combination of Kim, Glover, Davydov and You further teach the method of claim 6, wherein the utility function includes one or more constraints including one or both of a first constraint (Davydov, par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) whereby each available scheduling unit may be allocated to a maximum of one UE (Davydov, par. 127; the resource allocation size may be the number of scheduled resource blocks. In some embodiments, the resource allocation size for TBS calculation may be changed depending on the number of the CSI-RS antenna ports) and a second constraint whereby each wireless device may use a single modulation and coding scheme for the subset of available scheduling units (Kim, par. 20; the quantum annealer may achieve a target bit error rate (BER) (e.g., 10-6 ) and a frame error rate (FER) (e.g., 10-4 ) in a computation time limit (e.g., 10 to 20 ms of computation time) for a specific number of users and antennas (e.g., 48 user by 48 access point antenna (48x48) MIMO system) employing a modulation scheme (e.g., binary phase shift keyed (BPSK) modulation)).
Regarding Claim 8, the combination of Kim, Glover, Davydov and You further teach the method of claim 6, wherein bu,k, u=u1, . . . , uU, k=k1, . . . , kN indicates a solution to the ILP problem (Kim, par 25; A MIMO decoding problem with an optimal solution is called the “ML solution” and comprises a search over the sets of transmitted symbols, looking for the set that minimizes the error with respect to what has been received by the WCP 20. The solution may be represented as:
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The processor 14 de-maps the decoded symbols v to decoded bits
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H1 + jHQ is the wireless channel on each OFDM subcarrier and y is a
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received set of symbols perturbed by
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(i.e., additive white Gaussian noise, or “AWGN”). A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)).
Regarding Claim 26, the combination of Kim, Glover and Davydov further teach the apparatus of claim 25, wherein the ILP (Glover, pg. 17; This model accommodates both quadratic and linear objective functions since the linear case results when C is a diagonal matrix) problem is a problem to maximise
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(k∈
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|bu,k=1, mu), where
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is a set comprising the plurality of wireless communication devices,
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is a set of available scheduling units, φu is the utility function for wireless device u, mu indicates a maximum modulation and coding scheme throughput for the wireless device (Davydov par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) for the subset of available scheduling units where k∈
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|bu,k=1, and bu,k is a binary variable that is 1 if scheduling unit k is allocated to wireless device u and a value other than 1 if scheduling unit k is not allocated to the wireless device u (Kim par. 23; The MIMO processing system 10 also includes a processor 14 that is operable to embed a maximum likelihood (ML) detection algorithm onto a quantum annealer or otherwise quantum optimizer 16, and to decode the spatially multiplexed data streams via the embedded ML to detect a plurality of streams of data bits of a plurality of users (i.e., user data streams 1 - N, where the reference “N” indicates an integer greater than“1” and not necessarily equal to any other “N” reference designated herein) examiner notes, Glover discloses methods to transform linear problems to QUBO for minimization problems, it would be obvious for a person of ordinary skill in the art to attempt to solve for maximize problems.
The combination of Kim, Glover and Davydov does not explicitly teach a method to solve for maximize problem, however, You discloses a method for quantum based hybrid solutions for large scale problem optimization with the objective of minimizing or maximizing a function to optimize the problem using QUBO and linear programming (You, pars. 6-7; he first sub-problem is selected from a mixed-integer linear programming (MILP) problem, a relaxed mixed-integer linear programming (MILP) problem, a mixed-integer non-linear programming (MINLP) problem, a linear programming (LP) …. and the second algorithm is selected from algorithms for a quadratic unconstrained binary optimization (QUBO) problem or for an optimization problem solvable by a quantum computer or quantum processor directly and/or independently … The objective of the optimization problem is to minimize or maximize a function comprising a set of variables and a set of parameter).
Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filling date of the claimed invention to combine to combine Kim’s quantum annealing methods for maximizing throughput in a wireless network with Glover’s method to convert linear constraints to QUBO form, Davydov wireless resource scheduling allocation method
Regarding Claim 27, the combination of Kim, Glover, Davydov and You further teach the apparatus of claim 26, wherein the utility function includes one or more constraints including one or both of a first constraint (Davydov, par. 91; the scaling parameter may be equal to: a higher layer parameter altMCS-Table-scaling, if a modulation coding scheme (MCS) index of the PDSCH is greater than or equal to 44 and is less than or equal to 58; and 1.0 otherwise) whereby each available scheduling unit may be allocated to a maximum of one UE (Davydov, par. 127; the resource allocation size may be the number of scheduled resource blocks. In some embodiments, the resource allocation size for TBS calculation may be changed depending on the number of the CSI-RS antenna ports) and a second constraint whereby each wireless device may use a single modulation and coding scheme for the subset of available scheduling units (Kim, par. 20; the quantum annealer may achieve a target bit error rate (BER) (e.g., 10-6 ) and a frame error rate (FER) (e.g., 10-4 ) in a computation time limit (e.g., 10 to 20 ms of computation time) for a specific number of users and antennas (e.g., 48 user by 48 access point antenna (48x48) MIMO system) employing a modulation scheme (e.g., binary phase shift keyed (BPSK) modulation)).
Regarding Claim 28, the combination of Kim, Glover, Davydov and You further teach the apparatus of claim 26, wherein bu,k, u=u1, . . . , uU, k=k1, . . . , kN indicates a solution to the ILP problem (Kim, par 25; A MIMO decoding problem with an optimal solution is called the “ML solution” and comprises a search over the sets of transmitted symbols, looking for the set that minimizes the error with respect to what has been received by the WCP 20. The solution may be represented as:
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The processor 14 de-maps the decoded symbols v to decoded bits
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H1 + jHQ is the wireless channel on each OFDM subcarrier and y is a
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received set of symbols perturbed by
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(i.e., additive white Gaussian noise, or “AWGN”). A solution thus minimizes detection errors and maximizes throughput (e.g., via throughput optimal decoding)).
It is noted that any citations to specific pages, columns, lines or figures in the prior art
references and any interpretation of the reference should not be considered limiting in any way. A
reference is relevant for all it contains and may be relied upon for all that it would have reasonably
suggested to a person of ordinary skill in the art. See MPEP 2123
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (arXiv:2001.04014v1), Leveraging Quantum Annealing for Large MIMO Processing in Centralized Radio Access Networks, 2020.
Hui (US-8374137-B2), System and method of resource allocation and scheduling among base stations, 2013.
Majid (US-10713582-B2), Methods and systems for quantum computing, 2020.
Macready et al. (US-10,467,543-B2), Quantum processor based systems and methods that minimize an objective function, 2019.
Israel et al. (US-20150205759-A1), SYSTEMS AND METHODS FOR FINDING QUANTUM BINARY OPTIMIZATION PROBLEMS, 2015.
Kim et al. (arXiv:2010.00682v1), Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems, 2020.
Hahn et al. (DOI: 10.1109/ICRC.2017.8123654), Reducing Binary Quadratic Forms for More Scalable Quantum Annealing, 2017.
Hahn et al (DOI: 10.1109/ICRC.2019.8914719 ), Optimizing the spin reversal transform on the D-Wave 2000Q, 2019.
Kizilirmak (DOI: 10.1109/CSNDSP49049.2020.9249501), Quantum Annealing Approach to NOMA Signal Detection, 2020.
Rose (US-20120045136-A1), SYSTEMS, METHODS, AND APPARATUS FOR SOLVING PROBLEMS, 2012.
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/MRCM/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649