DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/13/2022 and 01/02/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
101 Rejection Arguments
Applicant asserts:
Applicant argues, on page 6-8, that the specific use of a classical processing unit and a quantum processor integrates the claimed method/system into a practical application
Examiner response:
Examiner respectfully disagrees and notes that MPEP 2106.05(f) shows that merely applying the abstract idea to a generic computer component does not integrate a judicial exception into a practical application. The quantum processor is not defined in a way where it could not simulated/emulated by a classical computer. Therefore, the quantum processor is interpreted as a classical computer emulating operations of a quantum processor to implement abstract ideas.
101 Rejection Arguments
Applicant asserts:
Applicant argues, on page 6-8, that the Mugel reference thus falls within the exception of 35 U.S.C. §102(b)(1)(B): "A disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) if- (B)the subject matter disclosed had, before such disclosure, been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor."
Examiner response:
Examiner respectfully disagrees and points to MPEP 717.01(b)(1), MPEP 2153.02, and MPEP 2155.02 "An applicant may show that the subject matter disclosed had been publicly disclosed by the inventor or a joint inventor before the disclosure or effective filing date of the subject matter on which the rejection was based by way of an affidavit or declaration under 37 CFR 1.130(b) (an affidavit or declaration of prior public disclosure)". However, the amendments have changed the scope of the claims, so Examiner has brought in new references and used a version of the Mugel disclosure that falls outside the 1 year grace period.
Applicant asserts:
Applicant argues, on page 9-10, that Mugel does not disclose that the updating is performed iteratively in the limitation “performing algebraic operations on the tensors in the tensor network using the processor to update iteratively the tensors in the tensor network”.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant asserts:
Applicant argues, on page 10-11, that Mugel does not disclose the specific use of a classical processing unit and a quantum processor as defined by claims 1 and 9.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant asserts:
Applicant argues, on page 10-11, that Mugel does not disclose the features "data storage unit for storing the data set" and "constructing a cost function from the Euclidean distances" of former claim 9 (which are also present in amended claim 9). As mentioned above, "Euclidean distances" has been generalized to "distances " in claim 9. The Applicant submits that Mugel does also not disclose the amended feature "constructing a cost function based on distances [...]" of claims 1 and 9. Therefore, the subject-matter of claims 1 and 9 is novel over Mugel.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant asserts:
Applicant argues, on page 10-11, that Dai is not related to clustering or to quantum, besides the simple mention of a quantum processor in par. [0058]. There would be no incentive and no motivation for a skilled person to combine the teaching of Mugel and Dai.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant asserts:
Applicant argues, on page 10-11, that Gabriele does also not disclose the amended feature "constructing a cost function based on distances [...]" of claims 1 and 9.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Calculating[, by a classical processing unit,] distances between the data points in the data set; which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(l)(c)).
“building[, by a classical processing unit,] a cost function based on the distances between the data points in the form of a Hamiltonian;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(l)(c)).
“creating[, by a classical processing unit,] from the cost function a tensor network comprising a plurality of tensors;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could create a tensor network from the cost function.
“performing algebraic operations on the tensors in the tensor network [using the quantum processor] to update iteratively the tensors in the tensor network;” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(l)(c)).
“establishing the clusters from parameters of the updated tensors.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could establish clusters based on the parameters of the update tensors.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer-implemented method for establishing clusters for a set of data points in a data set stored in a storage unit, the method comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“by a classical processing unit” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“using the quantum processor” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“passing, by the classical processing unit, the tensor network to a quantum processor;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and outputting the updated tensors.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer-implemented method for establishing clusters for a set of data points in a data set stored in a storage unit, the method comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“by a classical processing unit” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“using the quantum processor” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“passing, by the classical processing unit, the tensor network to a quantum processor;” (well-understood, routine, conventional MPEP 2106.05(d))
“and outputting the updated tensors.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, wherein the performing of the algebraic operations on the tensors establishes an energy minimum for the tensor network.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could establish an energy minimum from the algebraic operations.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 3:
Claim 3 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 4:
Claim 4 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 5:
Claim 5 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 6:
Claim 6 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 7:
Claim 7 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 8:
Claim 8 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“calculating the Euclidean distances between the data points in the data set” which is an abstract idea because it is directed to a mathematical relationships, mathematical formulas or equations, and mathematical calculations. (MPEP 2106.04(a)(2)(l)(c)).
“and constructing a cost function from the Euclidean distances;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could construct a cost function from the Euclidean distances.
“and solving the cost function to identify a minimum in the cost function.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could solve the cost function to identify a minimum in the cost function.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A system for establishing clusters for a set of data points in a data set, the system comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a data storage unit for storing the data set;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a central processing unit for” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a quantum processor for receiving the cost function from the central processing unit” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A system for establishing clusters for a set of data points in a data set, the system comprising:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a data storage unit for storing the data set;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a central processing unit for” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“- a quantum processor for receiving the cost function from the central processing unit” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Claim 10 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
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.
Claim(s) 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Samuel Mugel et al; “Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks” (hereinafter “Mugel”) in view of Jiachen Huang et al; US 20210209270 A1 (hereinafter “Huang”) in further view of Bertrand Marchand; US 20210375499 A1 (hereinafter “Marchand”) in further view of Will Wei Sun et al; “Dynamic Tensor Clustering” (hereinafter “Sun”)
Regarding claim 1, Mugel teaches A computer-implemented method for establishing clusters for a set of data points in a data set stored in a storage unit, the method comprising: (Mugel Page 2 Paragraph 1; "In Sec. IV we expose the data-preparation procedure, which reduces the problem’s dimensionality by identifying irrelevant assets and performing clusterization")
Calculating, [by a classical processing unit], distances between the data points in the data set (Mugel Page 7 Paragraph 3; “We then compute the Euclidean distance in the data trend for each pair of assets.” Examiner notes that a Euclidean distance is a distance)
creating, [by the classical processing unit], from the cost function a tensor network comprising a plurality of tensors; (Mugel Page 6 Paragraph 2; "TNs are a natural tool to solve optimization problems. People have been using them as an ansatz to approximate low-energy eigenstates of Hamiltonians, and many algorithms have been invented to this aim (see e.g. Ref.[23] and references therein). The idea here is that, by mapping optimization problems to Hamiltonian eigenvalue problems, as done in quantum annealing, we can then use the huge machinery of TN techniques and algorithms to solve the optimization problem at hand." Examiner notes that tensor network is made for solving the optimization problem/cost function comprising a plurality of tensors)
performing algebraic operations on the tensors in the tensor network using the quantum processor; (Mugel Page 1 Paragraph 3; “we implement D-Wave Hybrid quantum annealing, a Variational Quantum Eigensolver (VQE) on a quantum processor of IBM-Q” Mugel Page 6 Paragraph 3; "In our case, we implemented an optimization strategy over the so-called Matrix Product States (MPS)" Mugel Page 5 Fig 1; "The tensor network on the right hand side is an example of Matrix Product State." Examiner notes that the optimization strategy applied to the tensor network is performing algebraic operations on the tensors in the tensor network using the processor to update iteratively/summation the tensors in the tensor network into Matrix Product State using the quantum processor (a quantum processor of IBM-Q))
Mugel does not teach by the classical processing unit
passing, by the classical processing unit, the tensor network to a quantum processor;
However, Huang does teach by the classical processing unit (Huang Fig 3 and Paragraph 0032; “method 200 for performing contraction of a tensor network, according to some embodiments of the present disclosure. Method 200 can be implemented by a computing device (e.g., cloud service system 100 or computing device 100a of FIG. 3).” Huang Paragraph 0058; “the contraction processes shown above (e.g., method 200 of FIG. 4) can be used for quantum circuits or simulating quantum circuits.” Examiner notes that a classical processing unit is present (processor 102a))
PNG
media_image1.png
541
801
media_image1.png
Greyscale
passing, by the classical processing unit, the tensor network to a quantum processor; (Huang Paragraph 0053; “the computing device can perform the contraction on the plurality of sub-networks based on the contraction order. In some embodiments, the plurality of sub-networks can be distributed to a plurality of computing nodes (e.g., a plurality of cloud service devices 100 of FIG. 1) of a cloud system, respectively, for performing contraction on each of the plurality of sub-networks.” Examiner notes that computing device contains a classical processing unit that passes/distributes the sub-networks to computing nodes/processors that simulate a quantum processor)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel and Huang. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. One of ordinary skill would have motivation to combine Mugel and Huang to improve the computational time “ the use of sub-networks can improve the computational time of contracting tensor networks by more than a 100 times.” (Huang Paragraph 0056).
Mugel in view of Huang does not teach building, [by the classical processing unit], a cost function based on the distances between the data points in the form of a Hamiltonian;
However, Marchand does teach building, [by the classical processing unit], a cost function based on the distances between the data points in the form of a Hamiltonian; (Marchand Paragraph 0093; “the distance between representative Hamiltonians H.sub.Ryd (t.sub.f) and target Hamiltonian H.sub.MIS is used as a cost function within an iterative optimization process to identify a specific position configuration of atoms which reduce In this specific example, an extension base of the Hamiltonians constituted by operators n.sub.u for u∈V is used, due to being particularly suited to this example.” Examiner notes that a cost function is constructed/used from based on the distances between the data points in the form of a Hamiltonian (distance between representative Hamiltonians H.sub.Ryd (t.sub.f) and target Hamiltonian H.sub.MIS))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel, Huang, and Marchand. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. Marchand teaches using cost functions for positioning atoms. One of ordinary skill would have motivation to combine Mugel, Huang, and Marchand to improve quantum computing systems through improving the positions of the atoms “ The methods hereby provided aim at improving the positioning of the atoms or ions of the quantum computing system, aiming at aligning effective real interactions between the atoms or ions with a theoretical or ideal implementation of the quantum calculation or operation.” (Marchand Paragraph 0077).
Mugel in view of Huang in further view of Marchand does not teach to update iteratively the tensors in the tensor network
outputting the updated tensors.
establishing the clusters from parameters of the updated tensors.
However, Sun does teach to update iteratively the tensors in the tensor network (Sun Algorithm 1 shows updating the tensors in the tensor network at line 10; it is being iteratively updated because the update is being performed within the For loop on line 2)
PNG
media_image2.png
414
651
media_image2.png
Greyscale
outputting the updated tensors. (Examiner refers to previous mapping to show that following step 10 of updating the tensor T it is outputted for next loop)
establishing the clusters from parameters of the updated tensors. (Sun Algorithm 2 shows establishing the clusters (apply K-means clustering) from the parameters of the updated tensors (Step 2 updates tensor T and step 3 uses output of updated tensor for clustering))
PNG
media_image3.png
216
643
media_image3.png
Greyscale
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel, Huang, Marchand, and Sun. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. Marchand teaches using cost functions for positioning atoms. Sun teaches a new dynamic tensor clustering method. One of ordinary skill would have motivation to combine Mugel, Huang, Marchand, and Sun to reduce dimensionality and computational cost with and improvement to clustering accuracy “we use a simple simulation example to demonstrate that, our clustering method, which assumes (4) and thus exploits the underlying structure of the tensor data, can not only reduce the dimensionality and the computational cost, but also improve the clustering accuracy.” (Sun Page 7 Paragraph 1).
Regarding claim 2, Mugel teaches The method of claim 1, wherein the performing of the algebraic operations on the tensors establishes an energy minimum for the tensor network. (Mugel Page 9 Paragraph 2; "We notice that our quantum-inspired TN solver tends to approach the problem’s global minimum more reliably than D-Wave Hybrid in some cases" Examiner notes that the TN network where the optimization strategy/algebraic operations is applied on the tensors establishes an energy minimum/global minimum for the tensor network)
Regarding claim 3, Mugel teaches The method of claim 1, wherein the iterative updating of the tensors concludes when all of the coefficients of the tensors have been updated at least once. (Mugel Page 6 Fig 2; "The coefficient of the quantum state of n qubits is a tensor with exponentially many coefficients in the system’s size. " Examiner notes that the equation shows iterative updating of the tensors concludes when all of the coefficients C of the tensors have been updated at least once to obtain the Matrix Product State)
Regarding claim 4, Mugel teaches The method of claim 1, wherein the iterative updating concludes after a predefined number of iterations. (Mugel Page 6 Fig 2; "The coefficient of the quantum state of n qubits is a tensor with exponentially many coefficients in the system’s size." Examiner notes that the iterative updating concludes after a predefined number of iterations/number of tensors)
Regarding claim 5, Mugel does not teach The method of claim 1, wherein the iterative updating concludes after reaching a convergence criterion.
However, Sun does teach The method of claim 1, wherein the iterative updating concludes after reaching a convergence criterion. (Sun Page 15 Paragraph 3; " the next result shows that Algorithm 1 generates a contracted estimator. It is thus guaranteed that the estimator converges to the truth as the number of iterations increases" Examiner notes the iterative updating concludes after reaching a convergence criterion (as iteration increases estimator converges))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel, Huang, Marchand, and Sun. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. Marchand teaches using cost functions for positioning atoms. Sun teaches a new dynamic tensor clustering method. One of ordinary skill would have motivation to combine Mugel, Huang, Marchand, and Sun to reduce dimensionality and computational cost with and improvement to clustering accuracy “we use a simple simulation example to demonstrate that, our clustering method, which assumes (4) and thus exploits the underlying structure of the tensor data, can not only reduce the dimensionality and the computational cost, but also improve the clustering accuracy.” (Sun Page 7 Paragraph 1).
Regarding claim 6, Mugel teaches The method of claim 1, further comprising changing precision parameters of the tensor network. (Mugel Page 9 Paragraph 2; "In the case of the M, L and XL datasets, for instance, our TN algorithm returns solutions which have a larger Sharpe ratio and/or larger profits. Furthermore, for the XXL dataset the solution could still be further improved by playing with different hyperparameters and fine-tuning the algorithm further" Examiner notes that the precision parameters/hyperparameters of the tensor network are changed)
Regarding claim 7, Mugel teaches The method of claim 1 wherein the datasets are at least one of financial data, sensor data, vision data, language processing data, or health data. (Mugel Page 9 Table III; "Profits (percentual) computed by the different methods for the different datasets and time periods from Table I." Examiner notes that computed profits suggests datasets are at least of financial data)
Regarding claim 8, Mugel does not teach A non-transitory computer readable storage medium having stored thereon a computer program comprising program instructions, the computer program being loadable into a data-processor device and adapted to cause the data-processor device to carry out the method of claim 1.
However, Huang does teach A non-transitory computer readable storage medium having stored thereon a computer program comprising program instructions, the computer program being loadable into a data-processor device and adapted to cause the data-processor device to carry out the method of claim 1. (Huang Paragraph 0004; “Embodiments of the present disclosure further provide a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a system”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel and Huang. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. One of ordinary skill would have motivation to combine Mugel and Huang to improve the computational time “ the use of sub-networks can improve the computational time of contracting tensor networks by more than a 100 times.” (Huang Paragraph 0056).
Regarding claim 9, Mugel teaches A system for establishing clusters for a set of data points in a data set, the system comprising: (Mugel Page 2 Paragraph 1; "In Sec. IV we expose the data-preparation procedure, which reduces the problem’s dimensionality by identifying irrelevant assets and performing clusterization")
calculating the distances between the data points in the data set (Mugel Page 7 Paragraph 3; “We then compute the Euclidean distance in the data trend for each pair of assets.” Examiner notes that a Euclidean distance is a distance)
creating from the cost function a tensor network comprising a plurality of tensors; (Mugel Page 6 Paragraph 2; "TNs are a natural tool to solve optimization problems. People have been using them as an ansatz to approximate low-energy eigenstates of Hamiltonians, and many algorithms have been invented to this aim (see e.g. Ref.[23] and references therein). The idea here is that, by mapping optimization problems to Hamiltonian eigenvalue problems, as done in quantum annealing, we can then use the huge machinery of TN techniques and algorithms to solve the optimization problem at hand." Examiner notes that tensor network is made for solving the optimization problem/cost function comprising a plurality of tensors)
performing algebraic operations on the tensors in the tensor network (Mugel Page 1 Paragraph 3; “we implement D-Wave Hybrid quantum an nealing, a Variational Quantum Eigensolver (VQE) on a quantum processor of IBM-Q” Mugel Page 6 Paragraph 3; "In our case, we implemented an optimization strategy over the so-called Matrix Product States (MPS)" Mugel Page 5 Fig 1; "The tensor network on the right hand side is an example of Matrix Product State." Examiner notes that the optimization strategy applied to the tensor network is performing algebraic operations on the tensors in the tensor network using the processor to update iteratively/summation the tensors in the tensor network into Matrix Product State)
Mugel does not teach - a data storage unit for storing the data set;
a classical processing unit:
the quantum processor for:
passing the tensor network to a quantum processor;
receiving the tensor network from the central classical processing unit
However, Huang does teach - a data storage unit for storing the data set; (Huang Paragraph 0029; “Memory 104 can be configured to store instructions and data accessible by at least one processor 102.”)
a classical processing unit: (Huang Fig 3 and Paragraph 0032; “method 200 for performing contraction of a tensor network, according to some embodiments of the present disclosure. Method 200 can be implemented by a computing device (e.g., cloud service system 100 or computing device 100a of FIG. 3).” Huang Paragraph 0058; “the contraction processes shown above (e.g., method 200 of FIG. 4) can be used for quantum circuits or simulating quantum circuits.” Examiner notes that a classical processing unit is present (processor 102a))
PNG
media_image1.png
541
801
media_image1.png
Greyscale
passing the tensor network to a quantum processor; (Huang Paragraph 0053; “the computing device can perform the contraction on the plurality of sub-networks based on the contraction order. In some embodiments, the plurality of sub-networks can be distributed to a plurality of computing nodes (e.g., a plurality of cloud service devices 100 of FIG. 1) of a cloud system, respectively, for performing contraction on each of the plurality of sub-networks.” Examiner notes that computing device contains a classical processing unit that passes/distributes the sub-networks to computing nodes/processors that simulate a quantum processor)
the quantum processor for: (Huang Fig 3 and Paragraph 0032; “method 200 for performing contraction of a tensor network, according to some embodiments of the present disclosure. Method 200 can be implemented by a computing device (e.g., cloud service system 100 or computing device 100a of FIG. 3).” Huang Paragraph 0058; “the contraction processes shown above (e.g., method 200 of FIG. 4) can be used for quantum circuits or simulating quantum circuits.” Examiner notes that a quantum processor is present (classical processor that can simulate quantum circuits); Examiner interprets the quantum processor as being a classical processor simulating quantum operations because the claims and specification do not require a quantum processor comprised of physical qubits)
receiving the tensor network from the central classical processing unit (Huang Paragraph 0053; “the computing device can perform the contraction on the plurality of sub-networks based on the contraction order. In some embodiments, the plurality of sub-networks can be distributed to a plurality of computing nodes (e.g., a plurality of cloud service devices 100 of FIG. 1) of a cloud system, respectively, for performing contraction on each of the plurality of sub-networks.” Examiner notes that the tensor network (sub-network) is received from the classical processing unit (computing device contains a classical processing unit that distributes the sub-networks to computing nodes/processors that simulate a quantum processor))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel and Huang. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. One of ordinary skill would have motivation to combine Mugel and Huang to improve the computational time “ the use of sub-networks can improve the computational time of contracting tensor networks by more than a 100 times.” (Huang Paragraph 0056).
Mugel in view of Huang does not teach constructing a cost function from based on the distances between the data points in the form of a Hamiltonian;
However, Marchand does teach constructing a cost function from based on the distances between the data points in the form of a Hamiltonian; (Marchand Paragraph 0093; “the distance between representative Hamiltonians H.sub.Ryd (t.sub.f) and target Hamiltonian H.sub.MIS is used as a cost function within an iterative optimization process to identify a specific position configuration of atoms which reduce In this specific example, an extension base of the Hamiltonians constituted by operators n.sub.u for u∈V is used, due to being particularly suited to this example.” Examiner notes that a cost function is constructed/used from based on the distances between the data points in the form of a Hamiltonian (distance between representative Hamiltonians H.sub.Ryd (t.sub.f) and target Hamiltonian H.sub.MIS))
solving the cost function to identify a minimum in the cost function. (Marchand Paragraph 0081; “an objective is to reduce, increase, minimize or maximize such cost function”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel, Huang, and Marchand. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. Marchand teaches using cost functions for positioning atoms. One of ordinary skill would have motivation to combine Mugel, Huang, and Marchand to improve quantum computing systems through improving the positions of the atoms “ The methods hereby provided aim at improving the positioning of the atoms or ions of the quantum computing system, aiming at aligning effective real interactions between the atoms or ions with a theoretical or ideal implementation of the quantum calculation or operation.” (Marchand Paragraph 0077).
Mugel in view of Huang in further view of Marchand does not teach updating iteratively the tensors in the tensor network;
outputting the updated tensors.
establishing the clusters from parameters of the updated tensors.
However, Sun does teach updating iteratively the tensors in the tensor network; (Sun Algorithm 1 shows updating the tensors in the tensor network at line 10; it is being iteratively updated because the update is being performed within the For loop on line 2)
PNG
media_image2.png
414
651
media_image2.png
Greyscale
outputting the updated tensors. (Examiner refers to previous mapping to show that following step 10 of updating the tensor T it is outputted for next loop)
establishing the clusters from parameters of the updated tensors. (Sun Algorithm 2 shows establishing the clusters (apply K-means clustering) from the parameters of the updated tensors (Step 2 updates tensor T and step 3 uses output of updated tensor for clustering))
PNG
media_image3.png
216
643
media_image3.png
Greyscale
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Mugel, Huang, Marchand, and Sun. Mugel teaches several quantum and quantum inspired algorithms for dynamic portfolio optimization. Huang teaches a method for performing contraction on a tensor network. Marchand teaches using cost functions for positioning atoms. Sun teaches a new dynamic tensor clustering method. One of ordinary skill would have motivation to combine Mugel, Huang, Marchand, and Sun to reduce dimensionality and computational cost with and improvement to clustering accuracy “we use a simple simulation example to demonstrate that, our clustering method, which assumes (4) and thus exploits the underlying structure of the tensor data, can not only reduce the dimensionality and the computational cost, but also improve the clustering accuracy.” (Sun Page 7 Paragraph 1).
Regarding claim 10, Mugel teaches The system of claim 9, wherein the quantum processor is a quantum annealing processor. (Mugel Page 5 Paragraph 4; "In this work we used the quantum annealer provided by D-Wave, in particular the so-called D-Wave 2000Q processor" Examiner notes that D-Wave 2000Q processor is a quantum annealing processor)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DUC TRAN whose telephone number is (571)272-6870. The examiner can normally be reached Mon-Fri 8:00-5:00 EST.
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, Viker Lamardo can be reached at (571) 270-5871. 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.
/D.D.T./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
Prosecution Insights