DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
2. The drawings are objected to because Figure 2 does not comply with 37 C.F.R. § 1.84(U)(1), which states that partial views of a drawing which are intended to form one complete view, whether contained on one or several sheets, must be identified by the same number followed by a capital letter. Figure 2 is presented on two separate sheets labeled "FIGURE 2" (first sheet) and "FIGURE 2 Continued" (second sheet). The two drawings should be renumbered "FIGURE 2A" through "FIGURE 2B," respectively.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
3. 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-7 are to a machine, claims 8-14 are to a process, and claims 15-19 are to a process. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts:
1. A hybrid system for L0 regularization-based compressed sensing of a source signal, the hybrid system comprising:
a quantum machine configured to optimize a first parameter of a source signal, the first parameter comprising a support vector indicating places of non-zero elements in the source signal to minimize a cost function; and a classical machine configured to optimize a second parameter of the source signal, the second parameter comprising real number values in the source signal to minimize the cost function.
8. A method of L0 regularization-based compressed sensing of a source signal, the method comprising: optimizing, by a quantum machine, a first parameter of a source signal, the first parameter comprising a support vector indicating places of non-zero elements in the source signal to minimize a cost function;
and optimizing, by classical machine, a second parameter of the source signal, the second parameter comprising real number values in the source signal to minimize the cost function.
15. A method of performing an L0 regularization-based compressed sensing, the method comprising:
injecting a plurality of pump pulses into a coherent Ising machine optical parametric oscillator with an optical parametric oscillator formed in a fiber ring cavity having an output coupler and an input coupler, the output coupler in communication with a homodyne detection output and a second harmonic generation (SHG) crystal;
amplifying the plurality of pump pulses causing each of the plurality of pump pulses to take a 0-phase state or a π-phase state and model a support vector indicating places of non-zero elements in a source signal; and
optimizing the support vector to minimize a cost function.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the limitations to "optimize a first parameter of a source signal, the first parameter comprising a support vector indicating places of non-zero elements in the source signal to minimize a sot function" and to "optimize a second parameter of the source signal, the second parameter comprising real number values in the source signal to minimize the cost function" amount to a description in words to mathematically perform optimization of first and second parameters to minimize a cost function and carry out an L0 regularization-based compression of data. The limitations "to model a support vector indicating places of non-zero elements in source signal" and "optimizing the support vector to minimize a cost function" also amount to abstract idea limitations that describe mathematical optimizing of a source signal as represented by a support vector to minimize a cost function.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These additional limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the limitations of "sensing of a source signal" amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); and (2) the limitations of "a quantum machine" and "a classical machine" amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Claim 15 contains additional physical limitations (that are not underlined) such as "injecting a plurality of pump pulses into a coherent Ising machine optical parametric oscillator with an optical parametric oscillator formed in a fiber ring cavity having an output coupler and an input coupler, the output coupler in communication with a homodyne detection output and a second harmonic generation (SHG) crystal" and "amplifying the plurality of pump pulses causing each of the plurality of pump pulses to take a 0-phase state or a π-phase state," which amount to an integration into a practical application because they describe applying the judicial exception with or by use of a particular machine (see MPEP 2106.05(b)) and effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)). The subject matter eligibility analysis thus concludes for claims 15 (and dependent claims 16-19), and these claims are deemed to not be rejected under 35 U.S.C. 101.
Next in step 2B, independent claims 1 and 8 are further considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The sensing of a source signal does not add something significantly more because such a limitation amounts to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and does not describe any gathering of data in an unconventional way or using a particular physical measuring arrangement. The limitations of "a quantum machine" and "a classical machine" do not add something significantly more because they amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), and describe a general quantum machine and classical machine to carry out the calculations (without any applied change or improvement to the machines themselves).
Dependent claims 2-4 and 9-11 contain additional limitations that fall under the abstract idea grouping of a mathematical concept to describe multidimensional properties of the data of the source signal, calculation steps carried out by the quantum machine and classical machine, or defining the type of cost function used. Dependent claims 5-6 and 12-13 further describe generic details of the quantum machine and classical machine, and are not indicative of an integration into a practical application or significantly more as they merely recite using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Dependent claims 7 and 14 describe the type of source signal received as input data without further details describing any particular measuring arrangement or how the source signal is physically produced, and thus amount to insignificant extrasolution data gathering to the judicial exception (see MPEP 2106.05(g)).
4. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensed source signal, or further physical applications using the optimized to drive a change in operation, transformation, or maintenance/repair of a technology or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Claim Rejections - 35 USC § 102
5. 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 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.
6. Claims 1-4, 6-11, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rose et al. (US Pat. Pub. 2015/0006443, hereinafter "Rose").
In regards to claim 1, Rose teaches a hybrid system (Rose paragraph [0102] teaches a hybrid system comprising both a quantum processor and a non-quantum classical analog processor) for L0 regularization-based compressed sensing (Rose paragraph [0011] teaches formulating an objective function where a regularization term is governed by an L0-norm form, and paragraph [0095] and [0135] teach solving a computational problem by employing compressed sensing) of a source signal (Rose paragraph [0108] teaches receiving an input source signal having a dimensionality of N), the hybrid system comprising:
a quantum machine configured to optimize a first parameter of a source signal (Rose paragraphs [0012] and [0102] teaches using a quantum processor (quantum machine) to optimize an objective cost function for a first set of weight values (first parameter), and paragraphs [0096]-[0098] teaches carrying out the optimization on compressed sensing real-valued signals with weights as represented by a matrix dictionary D), the first parameter comprising a support vector indicating places of non-zero elements in the source signal (Rose paragraph [0098] teaches where the first set of weight values (first parameter) comprise a support basis vector having weight values of 0 or 1, and paragraph [0511] teaches where non-zero weights have magnitudes fixed to be equal to 1 (to indicate places of non-zero weight elements), where the use of binary weights makes the regularization term of the L0 form) to minimize a cost function (Rose paragraphs [0010] and [0012] teach where the optimization minimizes an objective function (cost function) of a quadratic unconstrained binary optimization problem (QUBO)); and
a classical machine configured to optimize a second parameter of the source signal (Rose paragraph [0011] and [0102] teach using a non-quantum processor (classical machine) to optimize a separate (second) set of assigned values for the dictionary, and paragraphs [0096]-[0098] teaches carrying out the optimization on compressed sensing real-valued signals as represented within the matrix dictionary D), the second parameter comprising real number values in the source signal to minimize the cost function (Rose paragraph [0011] teaches where the assigned set of first values (second parameter) comprises a matrix of real values to minimize the objective (cost) function).
In regards to claim 2, Rose teaches the hybrid system wherein:
the source signal is an N-dimensional source signal (Rose paragraph [0108] teaches an input source signal having a dimensionality of N).
In regards to claim 3, Rose teaches the hybrid system wherein the quantum machine and the classical machine are configured to alternatively perform their corresponding optimizations (Rose paragraph [0102] teach where the quantum processor and non-quantum processor alternately perform their corresponding optimizations), wherein:
when the quantum machine optimizes the first parameter, the classical machine is configured to keep the second parameter constant (Rose paragraph [0102] teaches a back-and-forth optimization procedure, where the quantum processor (quantum machine) is used to optimize for the Boolean weights (while the second parameter real value elements of D are held constant while waiting for processing by the non-quantum processor); and
when the classical machine optimizes the second parameter, the quantum machine is configured to keep the first parameter constant (Rose paragraph [0102] teaches a back-and-forth optimization procedure, where the non-quantum processor (classical machine) is used to optimize for the dictionary D values (while the first parameter weight values are held constant while waiting for processing by the quantum processor).
In regards to claim 4, Rose teaches the hybrid system wherein the cost function comprises a Hamiltonian cost function (Rose paragraph [0129] teaches where the cost function for solving a QUBO problem may be represented as a Hamiltonian).
In regards to claim 6, Rose teaches the hybrid system wherein the classical machine comprises a digital processor or a field programmable gate array (Rose paragraph [0142] teaches where the non-quantum processor (classical machine) comprises a digital processor or a field programmable gate array (FPGA)).
In regards to claim 7, Rose teaches the hybrid system wherein the source signal is a magnetic resonance imaging signal (Rose paragraph [0179] teaches where the source signal data can be from fMRI (magnetic resonance imaging)).
In regards to claim 8, Rose teaches a method of L0 regularization-based compressed sensing (Rose paragraph [0011] teaches a method of formulating an objective function where a regularization term is governed by an L0-norm form, and paragraph [0095] and [0135] teach solving a computational problem by employing compressed sensing) of a source signal (Rose paragraph [0108] teaches receiving an input source signal having a dimensionality of N), the method comprising:
optimizing, by a quantum machine, a first parameter of a source signal (Rose paragraphs [0012] and [0102] teaches using a quantum processor (quantum machine) to optimize an objective cost function for a first set of weight values (first parameter), and paragraphs [0096]-[0098] teaches carrying out the optimization on compressed sensing real-valued signals with weights as represented by a matrix dictionary D), the first parameter comprising a support vector indicating places of non-zero elements in the source signal (Rose paragraph [0098] teaches where the first set of weight values (first parameter) comprise a support basis vector having weight values of 0 or 1, and paragraph [0511] teaches where non-zero weights have magnitudes fixed to be equal to 1 (to indicate places of non-zero weight elements), where the use of binary weights makes the regularization term of the L0 form) to minimize a cost function (Rose paragraphs [0010] and [0012] teach where the optimization minimizes an objective function (cost function) of a quadratic unconstrained binary optimization problem (QUBO)); and
optimizing, by classical machine, a second parameter of the source signal (Rose paragraph [0011] and [0102] teach using a non-quantum processor (classical machine) to optimize a separate (second) set of assigned values for the dictionary, and paragraphs [0096]-[0098] teaches carrying out the optimization on compressed sensing real-valued signals as represented within the matrix dictionary D), the second parameter comprising real number values in the source signal to minimize the cost function (Rose paragraph [0011] teaches where the assigned set of first values (second parameter) comprises a matrix of real values to minimize the objective (cost) function).
In regards to claim 9, Rose teaches the method wherein:
the source signal is an N-dimensional source signal (Rose paragraph [0108] teaches an input source signal having a dimensionality of N).
In regards to claim 10, Rose teaches the method wherein the quantum machine and the classical machine alternatively perform their corresponding optimizations (Rose paragraph [0102] teach where the quantum processor and non-quantum processor alternately perform their corresponding optimizations), wherein:
when the quantum machine is optimizing the first parameter, the classical machine keeps the second parameter constant (Rose paragraph [0102] teaches a back-and-forth optimization procedure, where the quantum processor (quantum machine) is used to optimize for the Boolean weights (while the second parameter real value elements of D are held constant while waiting for processing by the non-quantum processor); and
when the classical machine is optimizing the second parameter, the quantum machine keeps the first parameter constant (Rose paragraph [0102] teaches a back-and-forth optimization procedure, where the non-quantum processor (classical machine) is used to optimize for the dictionary D values (while the first parameter weight values are held constant while waiting for processing by the quantum processor).
In regards to claim 11, Rose teaches the method wherein the cost function comprises a Hamiltonian cost function (Rose paragraph [0129] teaches where the cost function for solving a QUBO problem may be represented as a Hamiltonian).
In regards to claim 13, Rose teaches the method wherein the classical machine comprises a digital processor or a field programmable gate array (Rose paragraph [0142] teaches where the non-quantum processor (classical machine) comprises a digital processor or a field programmable gate array (FPGA)).
In regards to claim 14, Rose teaches the method wherein the source signal is a magnetic resonance imaging signal (Rose paragraph [0179] teaches where the source signal data can be from fMRI (magnetic resonance imaging)).
Claim Rejections - 35 USC § 103
7. 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.
8. Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rose et al. (US Pat. Pub. 2015/0006443) as applied to claim 1 or claim 8, and further in view of Tezak et al. (US Pat. Pub. 2022/0164693, hereinafter "Tezak").
In regards to claim 5, Rose teaches the hybrid system as explained in the rejection of claim 1 above. Rose fails to expressly teach wherein the quantum machine is a coherent Ising machine.
Tezak paragraph [0010] teaches a quantum processor unit (QPU) that can be used to process a stream of input data as a quantum streaming kernel, where the QPU can process the input data stream over time while producing an output data data stream and maintaining a coherent quantum state that depends on the history of input data. Tezak paragraph [0013] teaches where a quantum processor unit (QPU) can be implemented using a variety of QPU hardware such as ion traps, superconducting circuits, optical quantum computers, or semi-classical systems such as a coherent Ising machine).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Tezak to specify that the quantum machine is a coherent Ising machine because a coherent Ising machine is one of the known types of hardware implementations for a quantum processor unit (quantum machine). Therefore, it would be well within the level of ordinary skill to specify a quantum machine as a coherent Ising machine that allows for maintaining of a coherent quantum state as is well known in the art.
In regards to claim 12, Rose teaches the method as explained in the rejection of claim 8 above. Rose fails to expressly teach wherein the quantum machine is a coherent Ising machine.
Tezak paragraph [0010] teaches a quantum processor unit (QPU) that can be used to process a stream of input data as a quantum streaming kernel, where the QPU can process the input data stream over time while producing an output data data stream and maintaining a coherent quantum state that depends on the history of input data. Tezak paragraph [0013] teaches where a quantum processor unit (QPU) can be implemented using a variety of QPU hardware such as ion traps, superconducting circuits, optical quantum computers, or semi-classical systems such as a coherent Ising machine).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Tezak to specify that the quantum machine is a coherent Ising machine because a coherent Ising machine is one of the known types of hardware implementations for a quantum processor unit (quantum machine). Therefore, it would be well within the level of ordinary skill to specify a quantum machine as a coherent Ising machine that allows for maintaining of a coherent quantum state as is well known in the art.
Allowable Subject Matter
9. Claims 15-19 are allowable.
10. The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 15, the closest prior art, Rose et al. (US Pat. Pub. 2015/0006443) at least teaches a method of performing an L0 regularization-based compressed sensing (Rose paragraph [0011] teaches a method of formulating an objective function where a regularization term is governed by an L0-norm form, and paragraph [0095] and [0135] teach solving a computational problem by employing compressed sensing), the method comprising:
model a support vector indicating places of non-zero elements in a source signal (Rose paragraph [0098] teaches modeling in a dictionary matrix D, support basis vectors having weight values of 0 or 1, and paragraph [0511] teaches where non-zero weights have magnitudes fixed to be equal to 1 (to indicate places of non-zero weight elements), where the use of binary weights makes a regularization term of the L0 form); and
optimizing the support vector to minimize a cost function (Rose paragraphs [0010] and [0012] teach carrying out optimization to minimize an objective function (cost function) of a quadratic unconstrained binary optimization problem (QUBO)).
11. However, claim 15 contains allowable subject matter because the closest prior art, Rose et al. (US Pat. Pub. 2015/0006443) fails to anticipate or render obvious the method comprising:
injecting a plurality of pump pulses into a coherent Ising machine optical parametric oscillator with an optical parametric oscillator formed in a fiber ring cavity having an output coupler and an input coupler, the output coupler in communication with a homodyne detection output and a second harmonic generation (SHG) crystal; amplifying the plurality of pump pulses causing each of the plurality of pump pulses to take a 0-phase state or a π-phase state, in combination with the rest of the claim limitations as claimed and defined by the Applicant.
12. Dependent claims 16-19 depend from claim 15 and contain allowable subject matter for at least the same reasons as given for claim 15.
Pertinent Art
13. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
B. Ignjatovic et al. (US Pat. Pub. 2023/0229727) discloses Ising Machine Based on Coupled Bistable Nodes for Solving Combinatorial Problems. C. Elfving et al. (US Pat. Pub. 2023/0418896) discloses Solving a Set of (Non) Linear Differential Equations Using a Hybrid Data Processing System Comprising a Classical Computer System and a Quantum Computer System.
D. Matsuura et al. (US Pat. Pub. 2020/0311878) discloses Apparatus and Method for Image Reconstruction Using Feature-Aware Deep Learning.
E. Wang et al. (US Pat. Pub. 2018/0321347) discloses System and Method of Robust Quantitative Susceptibility Mapping.
F. Utsunomiya et al. (US Pat. No. 10,140,580) discloses Quantum Computing Device For Ising Model, Quantum Parallel Computing Device for Ising Model, and Quantum Computing Method for Ising Model.
G. Rubin (US Pat. No. 11,604,644) discloses Accelerating Hybrid Quantum/Classical Algorithms.
H. Ohwa et al. (US Pat. Pub. 2020/0184375) discloses Optimization Apparatus, Non-Transitory Computer-Readable Storage Medium for Storing Optimization Program, and Optimization Method.
Conclusion
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 1/24/2026