DETAILED ACTION
This action is in response to the original filing filed on 03/20/2023. Claims 1-9 are pending examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 3 is objected to because of the following informalities: “wherein the feature information is degree of connection between spins”. “Information is degree” appears to be missing a connecting word, such as “information is the degree” or “information is a degree”. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: “wherein the feature information is statistic of elements of a matrix that is specified from the given model.”. “Information is statistic” appears to be missing a connecting word, such as “information is a statistic”. 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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 6, and 8
Step 1: Claims 1, 6, and 8 recite a device, a method, and a non-transitory computer-readable recording medium, and so are directed to the statutory categories of a machine, method, and product of manufacture.
Step 2A Prong 1: The claimed limitation recites, inter alia: “derive feature information that represents a feature of a model used to solve a combinatorial optimization problem, when the model is given;” Under its broadest reasonable interpretation, this limitation amounts to no more than a mentally performable process, as deriving information representing features of a model is performable by a human being with the aid of pen and paper.
The claims also recite “select a solution method for the combinatorial optimization problem from among predetermined multiple types of solution methods based on the feature information; and” Under its broadest reasonable interpretation, this limitation amounts to no more than a mental process comprising determination or judgement, as selecting a solution from among a predetermined number of possible solutions is a determination easily performable by a human being.
Step 2A Prong 2: The judicial exceptions are not sufficiently integrated into a practical application. The additional elements of claims 1, 6, and 8 beyond the recited abstract ideas are: “a memory configured to store instructions; and a processor configured to execute the instructions to:”, which amounts to no more than invoking computers as a tool merely to perform an existing process (see MPEP 2106.05(f)).
The additional element of “send a solution request that includes information that can specify the model used to solve the combinatorial optimization problem to a solution device that solves the combinatorial optimization problem using the selected solution method” amounts to no more than well-understood, routine, and conventional activity in the art, particularly that of transmitting data over a network (see MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Thus, the judicial exception is not integrated into a practical application
Step 2B: The claims do not recite significantly more than the judicial exception. The additional elements of claims 1, 6, and 8 beyond the recited abstract ideas are: “a memory configured to store instructions; and a processor configured to execute the instructions to:”, which amounts to no more than invoking computers as a tool merely to perform an existing process (see MPEP 2106.05(f)).
The additional element of “send a solution request that includes information that can specify the model used to solve the combinatorial optimization problem to a solution device that solves the combinatorial optimization problem using the selected solution method” amounts to no more than well-understood, routine, and conventional activity in the art, particularly that of transmitting data over a network (see MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Considering the additional elements individually and in combination, the claims are directed to the judicial exception without significantly more.
Claims 2, 7, and 9
Step 1: Claims 2, 7, and 9 recite a device, a method, and a non-transitory computer-readable recording medium, and so are directed to the statutory categories of a machine, method, and product of manufacture.
Step 2A Prong 1: Claims 2, 7, and 9 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claims 1, 6, and 8, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2B Prong 2: The claim recites the additional element of: “wherein the processor receives a solution to the combinatorial optimization problem from the solution device to which the solution request was sent.”, which amounts to no more than merely reciting an element that is known to be well-understood, routine, and conventional in the art, particularly transmitting or receiving data over a network (see MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Thus, the judicial exception is not integrated into a practical application
Step 2B: The claims do not contain significantly more than the judicial exception.
Claim 3
Step 1: Claim 3 recites a device, and so is directed to the statutory category of a machine.
Step 2A Prong 1: Claim 3 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2B Prong 2: The claim recites the additional element of: “wherein the feature information is degree of connection between spins.”, which amounts to no more than generally linking to a technological environment or field of use (see MPEP 2106.05(h)).
Thus, the judicial exception is not integrated into a practical application
Step 2B: The claims do not contain significantly more than the judicial exception.
Claim 4
Step 1: Claim 3 recites a device, and so is directed to the statutory category of a machine.
Step 2A Prong 1: Claim merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2B Prong 2: The claim recites the additional element of: “wherein the feature information is statistic of elements of a matrix that is specified from the given model.”, which amounts to no more than generally linking to a technological environment or field of use (see MPEP 2106.05(h)).
Thus, the judicial exception is not integrated into a practical application
Step 2B: The claims do not contain significantly more than the judicial exception.
Claim 5
Step 1: Claim 3 recites a device, and so is directed to the statutory category of a machine.
Step 2A Prong 1: Claim merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2B Prong 2: The claim recites the additional element of: “wherein the feature information is an eigenvalue of a matrix that is specified from the given model.”, which amounts to no more than generally linking to a technological environment or field of use (see MPEP 2106.05(h)).
Thus, the judicial exception is not integrated into a practical application
Step 2B: The claims do not contain significantly more than the judicial exception.
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, 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.
Claims 1-2, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Linvill (US 11568293 B2, hereinafter Linvill) in view of. Alam et al. (US 12387130 B1, hereinafter Alam)
Regarding Claim 1, Linvill teaches a solution method selection device (Col. 1 Lines 40-53 In general, one innovative aspect of the subject matter described in this specification can be implemented in a computer implemented method comprising receiving, at a quantum formulation solver, data representing a computational task to be performed; deriving, by the quantum formulation solver, a formulation of the data representing the computational task that is formulated for a selected type of quantum computing resource; routing, by the quantum formulation solver, the formulation of the data representing the computational task to a quantum computing resource of the selected type to obtain data representing a solution to the computational task;), including:
a memory configured to store instructions; and a processor configured to execute the instructions to: ([Col. 18 Lines 50-67] The digital and/or quantum computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, one or more qubits, or a combination of one or more of them. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal that is capable of encoding digital and/or quantum information, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode digital and/or quantum information for transmission to suitable receiver apparatus for execution by a data processing apparatus.)
select a solution method for the combinatorial optimization problem from among predetermined multiple types of solution methods based on the feature information; and ([Col. 1 Lines 61-64] In some implementations the computational task comprises an optimization task, simulation task, machine learning task, arithmetic task, database search task or data compression task. [Col. 2 Lines 5-13] In some implementations the operations further comprise selecting, at the quantum formulation solver, an available quantum computing resource of the selected type, and wherein routing the formulation of the data representing the computational task to the quantum computing resource of the selected type comprises routing the formulation of the data representing the computational task to the selected available quantum computing resource of the selected type. (Selection of a solution method from a pool of predetermined types for a computational task, including an optimization task))
send a solution request that includes information that can specify the model used to solve the combinatorial optimization problem to a solution device that solves the combinatorial optimization problem using the selected solution method. ([Col. 7 Lines 33-41] For example, in some cases the input data 102 may include data representing an optimization task to be solved, and may also include data specifying that a quantum annealing computing resource should be used to perform the optimization task. As another example, in some cases the input data may include data representing a simulation task to be performed, and may also include data specifying that a quantum simulator should be used to perform the simulation task. (Sending the optimization task to be solved with data signifying which resource should be used to solve it (in this case, quantum annealing))).
Linvill does not teach: derive feature information that represents a feature of a model used to solve a combinatorial optimization problem, when the model is given;
In the same field of endeavor, Alam teaches: derive feature information that represents a feature of a model used to solve a combinatorial optimization problem, when the model is given; ([Col 11, Lines 45-53] As shown in the right column of Table 1, in an example deep reinforcement learning process for quantum program synthesis, an action of an agent corresponds to applying a quantum logic gate to a quantum logic circuit; the reward corresponds to a Hamiltonian expectation value; the state corresponds to state probabilities and a graph; the solved criterion corresponds to the Hamiltonian expectation value being maximized, and the policy of the agent corresponds to a neural network. [Col. 18 Lines 47-54] At 350, the agent samples the neural network 312 to select a quantum logic gate. To sample the neural network, the agent operates the neural network, for example, on a classical computing resource. In some examples, the agent provides neural network input data to the neural network, the neural network then processes the neural network input data to produce neural network output data, and the agent receives the neural network output data. [Col. 20 Lines 5 – 14] At 354, the agent receives and processes quantum processor output data from the quantum resource 314. In the example shown, the agent receives measurements generated by the quantum resource 314 executing the current version of the quantum program, and computes “state” and “reward” information (e.g., according to Table 1 or otherwise) from the measurements. The reward information can be computed by evaluating a cost function (e.g., a cost function based on the Hamiltonian specified by the problem to be solved). (The policy (corresponding to a neural network) used to solve a combinatorial optimization problem (COP) is sampled in order to derive information of one of its features (selection of a logic gate or computation of reward values of the model)))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the concept of deriving feature information that represent features of a model use to solve optimization problems as taught by Alam into Linvill as both are in the same field of solving combinatorial tasks and achieving this combination would represent an improvement in the state of the art by allowing for an automated process which would speed up the generation of quantum programs to find solutions for specific optimization problems (Alam Col. 3 Lines 4-15).
Regarding Claim 2, the combination of Linvill and Alam teaches all of the limitations of Claim 1, including: wherein the processor receives a solution to the combinatorial optimization problem from the solution device to which the solution request was sent. (Alam [Col. 4 Lines 48-51] The server 108 can receive, from each computing resource, output data from the execution of each computing job. [Col. 20 Lines 20-31] At 356, the agent checks if the reward satisfies the “solved” criteria (e.g., as specified in Table 1 or otherwise). For example, the agent may check to see if the reward value is greater than a threshold (for a maximization problem) or less than a threshold (for a minimization problem). As an example, the agent may check to see if the Hamiltonian expectation value is exactly one, which occurs when the quantum program gives the optimal bitstring with 100% certainty (e.g., each bitstring sampled is the MaxCut). Other, less onerous conditions may be used. If the reward does satisfy the “solved” criteria at 356, then the agent returns the results at 360. (Receiving the solution to the problem. The agent of Alam checks to see if the submitted problem to be solved has indeed been solved. If it has, then the results are returned.))
Regarding Claims 6-9, they are method, and a non-transitory computer-readable recording medium claims which correspond to the method selection device of Claim 1. Therefore, they are rejected for the same reason as the method selection device of Claims 1-2 above.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Linvill in view of Alam as applied in Claim 1 above, and in further view of OKUYAMA et al. (US 20220129780 A1, hereinafter Okuyama).
Regarding Claim 3, the combination of Linvill and Alam teaches all of the limitations of Claim 1. However, the combination fails to teach: wherein the feature information is degree of connection between spins.
In the same field of endeavor, Okuyama teaches: wherein the feature information is degree of connection between spins. (Paragraph [0023] A connection relationship between the spins of the Ising model is represented by graph G=(V,E). Paragraph [0031] FIG. 2 is a complete graph (fully connected graph) illustrating an interaction relationship between the entire spins of the Ising model when the number of spins is 6. When the combinatorial optimization problem is transformed into the problem of searching for the ground state of the Ising model, the interaction relationship between the spins becomes a dense structure (a structure in which each spin is adjacent to all other spins) as illustrated in the figure. Conversely, when the plurality of spins can be updated at the same time while satisfying a theoretical background required by the MCMC for the Ising model with such a dense interaction relationship, it is possible to speed up the processes of the MCMC or the SA, and thus, it is possible to efficiently solve the combinatorial optimization problem. (A connection relationship between the spins of the used Ising model in order to solve the optimization problem at hand)).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the feature information being a degree of connections as taught by Okuyama into the combination of Linvill and Alam since all three references are in the same field of solving combinatorial optimization problems, and doing so would allow for efficiently obtaining the ground state in optimization problems, as well as speeding up the process of doing so (Okuyama paragraphs [0007] – [0008]).
Regarding Claim 4, the combination of Linvill and Alam teaches all of the limitations of Claim 1. However, the combination fails to teach: wherein the feature information is statistic of elements of a matrix that is specified from the given model.
In the same field of endeavor, Okuyama teaches: wherein the feature information is statistic of elements of a matrix that is specified from the given model. (Paragraph [0022] In Formula 1, of represents a value of an i-th spin, J.sub.i,j represent an interaction coefficient between the i-th and j-th spins, and h.sub.i represents an external magnetic field coefficient acting on the i-th spin. A matrix J and a vector h that collectively represent these are introduced. [0034] In addition, it can be illustrated that the value satisfying the following Formula 4 also satisfies the condition. Herein, λ is a minimum eigenvalue of the matrix J. Also herein, λ may be a maximum eigenvalue of −J (sign-inverted matrix of the matrix J). (A matrix of the Ising model is described, wherein a statistical maximum or minimum value (eigenvalue) is described in its implementation of Formula 3 ([00003]))).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the feature information being a statistic of elements in a matrix as taught by Okuyama into the combination of Linvill and Alam since all three references are in the same field of solving combinatorial optimization problems, and doing so would allow for efficiently obtaining the ground state in optimization problems, as well as speeding up the process of doing so (Okuyama paragraphs [0007] – [0008]).
Regarding Claim 5, the combination of Linvill and Alam teaches all of the limitations of Claim 1. However, the combination fails to teach: wherein the feature information is an eigenvalue of a matrix that is specified from the given model.
In the same field of endeavor, Okuyama teaches: wherein the feature information is an eigenvalue of a matrix that is specified from the given model. (Paragraph [0034] In addition, it can be illustrated that the value satisfying the following Formula 4 also satisfies the condition. Herein, λ is a minimum eigenvalue of the matrix J. (The eigenvalue of a specified matrix (matrix J) are used to satisfy a formula in order to solve the optimization problem)).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the feature information being an eigenvalue of a matrix specified from a model as taught by Okuyama into the combination of Linvill and Alam since all three references are in the same field of solving combinatorial optimization problems, and doing so would allow for efficiently obtaining the ground state in optimization problems, as well as speeding up the process of doing so (Okuyama paragraphs [0007] – [0008]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicants’ disclosure.
IDE (US 20230342416 A1) teaches a process for solving combinatorial optimization problems.
You et al. (US 11769070 B2) teaches a computer apparatus involving two different types of computer architecture, one of which is a quantum computing unit which runs QUBO in order to solve combinatorial optimization problems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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, Jennifer Welch can be reached at (571) 272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights