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 .
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.
Election/Restriction
Claims 1-8 and 15-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 2/18/2026.
Disposition of the Claims
Claims 1-20 are pending. Claims 1-8 and 15-20 withdrawn from consideration. Claims 9-14 are examined on the merits.
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 9 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nguyen (A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D‑Wave quantum annealer1).
Regarding claim 9, Nguyen teaches an annealer or quantum computer system for solving a sparse coding problem (Abstract, “Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem”; “Sparse coding refers to a class of unsupervised learning algorithms for finding an optimized set of basis vectors, or dictionary …”), the system comprising:
a computing device (it is considered implicit from the disclosure that a classical computer is used to control the operation of a D-Wave Quantum Annealer, see e.g. p. 2, “hi and Jij are the qubit biases and coupling strengths that can be controlled by a user”), the computing device comprising:
a processor (of the controlling computer, see below that a classical CPU is used to optimize the dictionary matrix), the processor configured to:
receive a first set of values for a measurement vector (p. 2, Pre-Training, “Consider N sets of training data {X(i), y(i)}Ni=1, and M sets of the test data {X(j)}Mj=1,where X(i) ≡ {x(i)1 , x(i)2 , ... , x(i)D } is an input vector known as the independent variable, and y(i) is an output variable known as the dependent variable.”) and a second set of values for a dictionary matrix, based on an input (id. “Using X in the test dataset (M) or those in the combined training and test datasets (N + M), perform sparse coding training and obtain the dictionary φ for X.”; p. 1, “Recently, we developed a mapping [conversion] of the a(k)-optimization in Eq. (1) to the quadratic unconstrained binary optimization (QUBO) problem that can be solved on a quantum annealer and demonstrated its feasibility on the D-Wave systems”; p. 3, Method, “First, our optimization for a [first set of values of measurement] is performed using the D-Wave 2000Q at a given φ [the second set of values for a dictionary], whose initial guess [the second set of values] is given, in general, by random numbers or via imprinting technique [inferred to come from a classical computer at the outset of annealing]. Then, the optimization for φ is performed on classical CPUs”);
convert, using at least one conversion formula (the mapping discussed supra, see p. 2, Equation 4), the measurement vector and the dictionary matrix into a quadratic unconstrained binary optimization (QUBO) matrix) (Equation 4 being in terms of the dictionary Phi and the input data i.e. measurement vector X);
an annealer or quantum computer (D-Wave) to:
receive, from the computing device, the QUBO matrix (“Recently, we developed a mapping of the a(k)-optimization in Eq. (1) to the quadratic unconstrained binary optimization (QUBO) problem that can be solved on a quantum annealer and demonstrated its feasibility on the D-Wave systems”); and
minimize a function of the QUBO matrix and a generalized spin vector, by altering states of the annealer or quantum computer, the states being indicative of values of the generalized spin vector, to obtain a minimizing spin vector (“The quantum processing unit of the D-Wave systems realizes the quantum Ising spin system in a transverse field and finds the lowest or the near-lowest energy states of the classical Ising model … using quantum annealing. Here si = ±1 is the binary spin variable, hi and Jij are the qubit biases and coupling strengths that can be controlled by a user, and optimization for the Ising model is isomorphic to a QUBO problem with ai = (si + 1)/2. By mapping the sparse coding to a QUBO structure, the sparse coefficients are restricted to binary variables ai ∈ {0, 1}, and it makes the L0-norm equivalent to the L1-norm”); and
output the minimizing spin vector (sequitur, see e.g. p. 3 “Application to Lattice QCD”);
wherein the processor of the computing device is further configured to:
convert using the at least one conversion formula, the spin vector into a solution vector (“By performing the quantum annealing for a given dictionary φ and input data vector X with the transformations given in Eq. (4), one can obtain the optimal sparse representation a.”), wherein the solution vector is indicative of a relationship between the values for a measurement vector and the values for a dictionary matrix (Training, “Through this procedure, φ- will encode the correlation between x(i)d and y(i).”, and Prediction, “Using the dictionary φ- obtained in (4), find a sparse representation a(j) for X-(j)o and calculate reconstruction as X-′(j) = φ- a(j). This replaces the outlier components, including y¯(j), in X-(j)o by the values that can be described by φ- … After inverse-normalization, the (D + 1)th component of X-′(j) is the prediction of y(j): (X-′(j))D+1 = ˆy(j) ≈ y(j).”; see also Application to lattice Quantum Chromodynamics resulting in a computed solution).
Claim 12 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nguyen (A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D‑Wave quantum annealer2), evidenced by Glover (A Tutorial on Formulating and Using QUBO Models3).
Regarding claim 12, Nguyen teaches the method of claim 11, but does not explicitly show wherein the function of the QUBO matrix and the generalized spin vector is given by: q^T W q wherein q represents the generalized spin vector and W represents the QUBO matrix.
Glover provides evidence that the general form of QUBO models is that which is claimed (p. 5, Basic QUBO Problem Formulation, y = xT Q x ) by definition.
Thus it is considered inherently disclosed by Nguyen that the QUBO problem thus formulated for the annealer is expressed in the way evidenced by Glover.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen as applied to claim 9 above, and further in view of Syed (US 12422882 B2).
Regarding claim 10, Nguyen teaches the system of claim 9, but does not explicitly show wherein the annealer comprises an optical annealer.
Syed provides optical systems for performing matrix-matrix operations and thus capable of performing the operations disclosed by Nguyen. For example, Syed discloses a photonic crossbar for carrying out an improvement of simulated annealing (C. 12, ll. 7-27, matrix-matrix and matrix-vector operations to minimize energy i.e. objective function; n.b. as discussed above there is an isomorphism between an Ising Hamiltonian and the QUBO objective function such that Syed is considered compatible with the method of Nguyen).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have performed matrix operations known from Nguyen using the device of Andregg and carried out the claimed optimization, i.e. optical annealing, in a predictable fashion based on the disclosed method.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen as applied to claim 9 above, and further in view of Macready (US 20140025606 A1).
Regarding claim 11, Nguyen teaches the system of claim 9, and explicitly shows the sparse coding problem minimization problem in Equation 1, which appears merely to be a re-expression of the claimed equation in explicit L2 norm notation for its first term.
Nguyen does not explicitly show the claimed formulation of the QUBO sparse coding problem.
Macready explicitly shows this formulation is known in the field of objective function minimization (¶35-36, defining the objective function to be minimized and expressing it as Equation 2, which corresponds to the claimed equation differing only by a sign in the first term L2 norm, i.e. gives the same result, then goes on with respect to the second term inner product: “In some instances, casting the weights w.sub.i as Boolean values [n.b. as in a quadratic binary problem, which is immediately discussed in the subsequent paragraphs] realizes a kind of 0-norm sparsity penalty. For many problems, the 0-norm version of the problem is expected to be sparser than the 1-norm variant. Historically, the 0-norm variation has been less studied as it can be more difficult to solve”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the objective function studied by Nguyen, identically disclosed to correspond to a sparse coding training as identified by Macready, thus corresponds to the more difficult 0-norm sparsity objective function and thus the claimed sparsed coding problem objective function.
Allowable Subject Matter
Claims 13 and 14 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 art considered pertinent to the instant application but not relied upon generally discloses the general framework for formulating a problem on a D-Wave quantum annealing computer by conversion between QUBO and Ising problems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COLLIN X BEATTY whose telephone number is (571)270-1255. The examiner can normally be reached M - F, 10am - 6pm.
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/COLLIN X BEATTY/Primary Examiner, Art Unit 2872
1 Nguyen et Al. A regression algorithm for accelerated lattice QCDthat exploits sparse inference on the D‑Wave quantum annealer. Scientific Reports | (2020) 10:10915 | https://doi.org/10.1038/s41598-020-67769-x
2 Nguyen et Al. A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D‑Wave quantum annealer. Scientific Reports | (2020) 10:10915 | https://doi.org/10.1038/s41598-020-67769-x
3 Glover et Al. A Tutorial on Formulating and Using QUBO Models. 2019.