DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/14/2025 has been entered.
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 11/14/2025.
Response to Amendment
The amendment filed 11/14/2025 has been entered. Claims 1-33 remain pending in the application. Claims 28-33 are new.
Response to Arguments
Applicant’s arguments, filed 11/14/2025, with respect to the rejections of claims 1 and 11 under 103 have been fully considered and are persuasive because of the amendments. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Bolt et al. (US Pub. 2021/0158232) in view of Linvill (US Pub. 2020/0027029) in view of Lam et al. (US Pub. 2021/0287773) in view of Shen et al. (US Pub. 2022/0180125) in view of Monroe et al. (US Pub. 2018/0114138) and further in view of Williams (US Pub. 2014/0337612).
Applicant argues (pages 3-4)
Applicant respectfully submits that there is no apparent reason to combine the references as in the rejection, and that the rejection is based on improper hindsight. The Office's stated rationale for combining the references is inadequate under Graham v. John Deere and MPEP § 2143. The Office Action states that combining Lam with Bolt "would help performing the compute workflow via either the quantum computing, the classical computing, or both to produce results." This statement says nothing more than that it would be "better," and is far from required articulated reasoning.
The Office's approach constitutes improper hindsight reconstruction. The mere fact that references can be physically combined does not establish obviousness. The Office Action must articulate reasoning with rational underpinning for why a skilled artisan would have been motivated to make the specific combination and apply it to the claimed application domain.
The Office Action does not explain why a skilled artisan working on quantum computing services (Bolt's focus) would 1) Incorporate drug discovery workflows from Lam, 2) Integrate general neural network training from Shen, 3) Combine trapped ion hardware from Monroe with quantum annealing from Williams, and then 4) Apply this combination specifically to value chain network product/device/vehicle/service design optimization. This cumulative combination requires integrating five disparate references focused on different technical problems and different application domains, and then applying the result to an application domain (value chain network optimization) that none of the references address. The lack of evidence established in the Office Action leaves the Applicant wanting for the required rational underpinning for such complex combination rejections, which, when present, could support a prima facie case of obviousness; knowing conclusory statements and improper hindsight do not.
In response
Applicant argues “there is no apparent reason to combine the references as in the rejection, and that the rejection is based on improper hindsight … The Office's approach constitutes improper hindsight reconstruction. The mere fact that references can be physically combined does not establish obviousness. The Office Action must articulate reasoning with rational underpinning for why a skilled artisan would have been motivated to make the specific combination and apply it to the claimed application domain …”. The examiner respectfully disagrees.
MPEP 2141.01.a.1 states that “the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). See Bigio, 381 F.3d at 1325, 72 USPQ2d at 1212”. The cited references are analogous art because they are from the same field of endeavor or similar problem-solving area, quantum computing and/or executing quantum computer task.
For example, Bolt describing a quantum computing service comprising receiving, translating and executing a quantum task. Bolt in at least paragraph 0046 disclose the system comprises both classical and quantum computing portions. Bolt, however, does not explicitly teach when receiving a request, using both classical and quantum computing system to process the input to generate the outputs, the outputs between the two computing systems are compared to determine which computing system to execute the task. While Lam, an analogous art, teaches the computational system comprising both classical and quantum computing. In paragraphs 0005-0009, 0020-0021 and 0071, Lam teaches when receiving a compute workflow which comprising computing tasks, determining a computing platform for a task between classical and quantum computing by determining a probability of an advantage of using the quantum computing compared to the classical computing for the computing tasks, “determining whether a likelihood of an advantage, for example a performance advantage and/or cost advantage, is probable for the quantum computing when compared to the classical computing for the computing tasks”. Therefore, combining Lam to Bolt would help determining which computing platform to execute the computing task with less cost and better performance.
Similar to the explanation above, Bolt and other cited references such as Shen, Monroe and Williams are analogous art because they are from the same field of endeavor or similar problem-solving area, quantum computing and/or executing quantum computer task. Bolt has the majority structure of the quantum computing system of the claim invention. The reason of combining other references is to add to Bolt the missing elements which clearly stated in the 103 rejections section below. The secondary references such as Lam, Shen, Monroe and Williams are not intended to read the structure of the quantum computing system. The secondary references only address the missing elements of Bolt. Further, Examiner clearly states the advantages to combine the references in the 103 rejections section.
Applicant argues (pages 4-5)
The rejection further fails to consider the claimed invention as an integrated system addressing a specific technical problem. Under MPEP § 2143.03, the Office must consider the claimed invention as a whole rather than treating it as a collection of separable elements.
The amended claim requires sophisticated integration of multiple components operating together for a specific purpose. Specifically, the system relates to a DPANN system that generates predictions about quantum versus classical computing advantages for value chain network optimization tasks, determination logic that selects appropriate computing resources based on these predictions, a quantum computing system integrating both trapped ion and quantum annealing capabilities, and application of this integrated system to the specific technical problem of optimizing product, device, vehicle, or service design and configuration in value chain networks. The specification describes this as a unified system addressing the recognized problem that "successfully using quantum computers to solve practical problems may require significant trial and error and/or otherwise require significant expertise in using quantum computers." The claimed invention provides an intelligent, prediction-based solution that automatically determines whether quantum, classical, or hybrid computing resources should be deployed for value chain network optimization tasks.
The rejection treats each claim element in isolation, and this piecemeal approach fails to analyze whether the combination would achieve the integrated functionality claimed: a DPANN based prediction system that intelligently allocates quantum and classical computing resources specifically for value chain network product/device/vehicle/service design optimization. The examiner has not shown that the combination would function as the claimed integrated system or address the claimed application domain.
Therefore, Bolt, Lam, Shen, Monroe, Williams, Langtin, AMT, Schuster, Achkir, Dukatz, Chen, Shin, White, Rubin, and combinations thereof fail to teach the features of claims 1 and 11. Accordingly, Applicant respectfully requests withdrawal of the rejection and allowance of claims 1 and 11. Claims 2-10 and 12-27 depend from one of claims 1 or 11. Accordingly, Applicant respectfully requests withdrawal of the rejections and allowance of claims 2-10 and 12-27.
In response
The Applicant argues “The rejection treats each claim element in isolation, and this piecemeal approach …”. The examiner respectfully disagrees.
Under the 103 rejections below, each of claim limitations is considered, the claim is considered as a whole, and only references that are analogous art are used and combined to come up to the claim invention. That is not a piecemeal approach.
Other arguments comprising the amended limitations “the specific technical problem of optimizing product, device, vehicle, or service design and configuration in value chain networks … system that intelligently allocates quantum and classical computing resources specifically for value chain network product/device/vehicle/service design optimization” will be further considered.
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 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.
Claims 1-2, 4, 11-13, 15, 22 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. (US Pub. 2021/0158232) in view of Linvill (US Pub. 2020/0027029) in view of Lam et al. (US Pub. 2021/0287773) in view of Shen et al. (US Pub. 2022/0180125) in view of Monroe et al. (US Pub. 2018/0114138) and further in view of Williams (US Pub. 2014/0337612).
As per claim 1, Bolt teaches a computer-implemented method for executing a quantum computing task [paragraph 0017, “a process involving a quantum computing service receiving, translating, and executing a quantum task”], the method comprising:
providing a quantum computing system including a trapped ion computer module and a quantum annealing computing system [abstract, “A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task”; Fig. 1, paragraph 0073, “a quantum computing service may provide potential quantum computer users with access to quantum computers using various quantum computing technologies, such as quantum annealers, ion trap machines, superconducting machines, photonic devices, etc.”; paragraph 0097, “allow a customer to simulate one or more particular quantum computing technology environments. For example, a customer may simulate a quantum circuit in an annealing quantum computing environment and an ion trap quantum computing environment to determine simulated error rates”];
receiving a request, from a computing client, to execute a computing task [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed”; paragraph 0039, “the quantum computing object may be a quantum task”; paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed”];
executing the quantum computing task via the quantum computing system [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed and select at least one of the first or second quantum hardware providers to execute the quantum computing object; paragraph 0037, “the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies”], wherein the executing the quantum computing task includes:
generating a response to the quantum computing task [paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed … the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies … execution results received from the first or second quantum hardware provider to be stored and providing, by the quantum computing service, a notification to the customer that the quantum computing task has been completed”; wherein, paragraph 0073, “various quantum computing technologies, such as quantum annealers, ion trap machines, …”, and Fig. 1 shows the first quantum hardware provider and the second quantum hardware provider are the annealing quantum hardware provider and ion trap quantum hardware provider],
optimizing the response [paragraph 0083, “the machine learning service may cause the quantum algorithms or quantum circuits to be run on various different quantum computing technology-based quantum computers. Based on the results, the machine learning service may determine one or more optimizations to improve the quantum algorithms or quantum circuits”];
transmitting, via the quantum computing system, the optimized response related to the executed quantum computing task to the computing client [paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed … the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies … execution results received from the first or second quantum hardware provider to be stored and providing, by the quantum computing service, a notification to the customer that the quantum computing task has been completed”; wherein, paragraph 0073, “various quantum computing technologies, such as quantum annealers, ion trap machines, …”].
Bolt teaches “execute quantum computing tasks using quantum computers based on different quantum computing technologies … such as ion trap machines”. However,
Bolt does not explicitly teach
wherein the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network;
in response to receiving the request, generating, via a machine learning model, a prediction associated with a difference in outcome between a quantum-optimized result and a non-quantum-optimized result to the computing task,
wherein the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
wherein the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
wherein the machine learning model is refined based on results of predictions generated by the machine learning model;
determining, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the computing task;
in response to determining to use the quantum computing system, generating a quantum computing task;
trapping a set of ions by confining the set of ions using one or more electromagnetic fields via the trapped ion computer module, wherein one or more qubits are stored in states of the set of ions, and
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task; and
Linvill teaches
the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network [abstract, “Methods, systems, and apparatus for solving computational tasks using quantum computing resources”; paragraphs 0036 and 0039, “The system 100 for performing computational tasks is configured to receive, as input, data representing a computational task to be solved, e.g., input data 102. The system 100 may be configured to solve multiple computational tasks, e.g., including optimization tasks, simulation tasks, arithmetic tasks, database search, machine learning tasks, or data compression tasks, and the input data 102 may include data that specifies one of the multiple computational tasks. … the input data 102 may be data that represents the task of optimizing the design of a water network in order to optimize the amount of water distributed by the network … the input data 102 may include data representing one or more parameters associated with the optimization task, e.g., level of water pressure in each pipe, level of water pressure at each connecting node, height of water level in each water tank, concentration of chemicals in the water throughout the network, water age or water source. Furthermore, the input data 102 may include dynamic input data representing one or more current properties or values of parameters of the water network, e.g., a current number of water pipes in use, a current level of water pressure in each pipe, a current concentration of chemicals in the water, or a current temperature of the water”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network of Linvill. Doing so would help solving the computational task to optimize the design of the product and/or service in a network (for example, optimizing the amount of water distributed by the network) (Linvill, 0100).
Bolt and Linvill do not teach
in response to receiving the request, generating, via a machine learning model, a prediction associated with a difference in outcome between a quantum- optimized result and a non-quantum-optimized result to the computing task,
wherein the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
wherein the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
wherein the machine learning model is refined based on results of predictions generated by the machine learning model;
determining, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the computing task;
in response to determining to use the quantum computing system, generating a quantum computing task;
trapping a set of ions by confining the set of ions using one or more electromagnetic fields via the trapped ion computer module, wherein one or more qubits are stored in states of the set of ions, and
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task;
Lam teaches
in response to receiving the request, generating, via a machine learning model, a prediction associated with a difference in outcome between a quantum- optimized result and a non-quantum-optimized result to the computing task [paragraphs 0020-0021, “receiving parameters … defining a compute workflow to be performed … the compute workflow comprising computing tasks … retrieving data sets from a repository relating to the compute workflow … comparing at least part of the computing tasks and at least part of the data sets for determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks”; paragraph 0071, “compare the computing tasks and at least part of the data sets for determining whether a likelihood of an advantage, for example a performance advantage and/or cost advantage, is probable for the quantum computing when compared to the classical computing for the computing tasks”; paragraphs 0087-0088, “FIGS. 1-6 highlight examples of the machine learning operation … the machine learning operation may operate via ensemble machine learning, which may compare and contrast the results of various machine learning operations to increase the collective confidence of the predicted results … ensemble machine learning may include classical machine learning tasks performed via the classical computing and quantum machine learning tasks performed via the quantum computing … For computing tasks included by the compute workflow indicating a likelihood of an advantage, for example a performance advantage or a cost advantage, is probable and/or favorable from a quantum computing pipeline, quantum machine learning may be applied, as will be understood by those of skill in the art. For computing tasks included by the compute workflow for which a likelihood of the advantage is improbable and/or not favorable from a quantum computing pipeline, classical machine learning may be applied”];
determining, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the computing task [paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow … distributing a classical computing task to be performed via the classical computing if included by or recommended based on the likelihood of the advantage by the compute workflow”; paragraph 0009, “The system may (g) perform the compute workflow via the computing environment to produce results”; paragraph 0074, “a job scheduler 130 may distribute the workflow to classical computing 140 and quantum computing 150 aspects of the computing environment. The computing environment may then perform the compute workflow to produce results”; paragraph 0007, “The computing environment may include a classical computing processor to perform the classical computing and a quantum computing processor to perform the quantum computing”];
in response to determining to use the quantum computing system, generating a quantum computing task [paragraph 0072, “Compute workflows may be classified into tasks to be accomplished via quantum computing with the highest probable efficiency, most economically efficient computation … identifying a quantum computing task included by the compute workflow to be performed via the quantum computing”; paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow; It can be seen that the quantum task is generated/identified and distributed via the quantum computing];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include generating a prediction associated with a difference in outcome between a quantum- optimized result and a non-quantum-optimized result to the computing task, determining which computing system to use to execute the computing task based on the prediction, and generating a quantum computing task of Lam. Doing so would help performing the compute workflow via either the quantum computing, the classical computing, or both to produce results (Lam, 0021).
Bolt, Linvill and Lam do not teach
wherein the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
wherein the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
wherein the machine learning model is refined based on results of predictions generated by the machine learning model;
trapping a set of ions by confining the set of ions using one or more electromagnetic fields via the trapped ion computer module, wherein one or more qubits are stored in states of the set of ions, and
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task;
Shen teaches
the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model [paragraph 0360, “performing one or more machine learning algorithms embodied in one or more neural network”; Fig. 10, paragraph 0112, “a machine learning model may be trained by calculating weight parameters according to a neural network architecture … trained machine learning models corresponding to one or more neural networks may be used to infer or predict information”; paragraph 0101, “untrained neural network 1006 is trained using supervised learning, wherein training dataset 1002 includes an input paired with a desired output for an input, or where training dataset 1002 includes input having a known output and an output of neural network 1006 is manually graded … untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs … errors are then propagated back through untrained neural network 1006 …. training framework 1004 adjusts weights that control untrained neural network 1006 … training framework 1004 trains untrained neural network 1006 repeatedly while adjust weights to refine an output of untrained neural network 1006 using a loss function and adjustment algorithm (train and retrain the untrained neural network 1006) … training framework 1004 trains untrained neural network 1006 until untrained neural network 1006 achieves a desired accuracy”],
the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions [paragraphs 0100-0101, “training and deployment of a deep neural network … untrained neural network 1006 is trained using supervised learning, wherein training dataset 1002 includes an input paired with a desired output for an input, or where training dataset 1002 includes input having a known output and an output of neural network 1006 is manually graded … untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs], and
the machine learning model is refined based on results of predictions generated by the machine learning model [paragraph 0101, “untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs … errors are then propagated back through untrained neural network 1006 …. training framework 1004 adjusts weights that control untrained neural network 1006 … training framework 1004 trains untrained neural network 1006 repeatedly while adjust weights to refine an output of untrained neural network 1006 using a loss function and adjustment algorithm (train and retrain the untrained neural network 1006 based on the error) … training framework 1004 trains untrained neural network 1006 until untrained neural network 1006 achieves a desired accuracy”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model, wherein the machine learning model is refined based on results of predictions generated by the machine learning model of Shen. Doing so would help training a neural network until the neural network achieves a desired accuracy (Shen, 0101).
Bolt, Linvill, Lam and Shen do not teach
trapping a set of ions by confining the set of ions using one or more electromagnetic fields via the trapped ion computer module, wherein one or more qubits are stored in states of the set of ions, and
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task;
Monroe teaches
trapping a set of ions by confining the set of ions using one or more electromagnetic fields via the trapped ion computer module, wherein one or more qubits are stored in states of the set of ions [paragraph 0017, “a trapped ion quantum computer is a type of quantum computer in which ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be processed and transferred through the collective quantized motion of the ions in the trap”; Since Bolt in paragraphs 0037 and 0073 teaches executing quantum computing tasks using quantum computers such as ion trap machines, while Monroe teaches “trapped ion quantum computer is a type of quantum computer in which ions, or charged atomic particles, can be confined and suspended in free space, and Qubits are stored in stable electronic states of each ion”, therefore, the combination of Bolt and Monroe teaches the above claim limitation];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the process of executing the requested quantum computing task includes trapping a set of ions of Monroe. Doing so would help scaling up quantum computers and processing quantum information with higher accuracy (Monroe, 0019).
Bolt, Linvill, Lam, Shen and Monroe do not teach
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task;
Williams teaches
optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … maximum acceptable objective function value”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include optimizing the response by identifying, via the quantum annealing computing system, a maximum of an objective function over a set of candidate solutions associated with the quantum computing task of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
As per claim 2, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Williams further teaches
the optimizing the response includes identifying a minimum of the objective function over the set of candidate solutions using the quantum annealing computing system [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … minimum acceptable objective function value”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include optimizing the response by identifying, via the quantum annealing computing system, a minimum of an objective function over a set of candidate solutions associated with the requested quantum computing task of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
As per claim 4, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt further teaches
the quantum computing system supports a quantum computing model including at least one of: quantum circuit model, quantum Turing machine, spintronic computing system, adiabatic quantum computing system, one-way quantum computer, or quantum cellular automata [abstract, “A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit”; paragraph 0023, “FIG. 15B, illustrates an example process for scheduling execution of a quantum circuit on a quantum computer”; paragraph 0155, “a quantum computing service may support both circuit based quantum computers and annealing based quantum computers].
As per claim 11, Bolt teaches a quantum computing system [abstract, a quantum computing service] comprising:
a quantum computing module that is configured to receive a request to execute a computing task [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed”; paragraph 0039, “the quantum computing object may be a quantum task”; paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed”], wherein the quantum computing module is configured to execute the requested computing task using the trapped ion computer module to generate a response to the requested computing task [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed and select at least one of the first or second quantum hardware providers to execute the quantum computing object; paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed … the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies … execution results received from the first or second quantum hardware provider to be stored and providing, by the quantum computing service, a notification to the customer that the quantum computing task has been completed”; wherein, paragraph 0073, “various quantum computing technologies, such as quantum annealers, ion trap machines, …”, and Fig. 1 shows the first quantum hardware provider and the second quantum hardware provider are the annealing quantum hardware provider and ion trap quantum hardware provider],
optimize the response [paragraph 0083, “the machine learning service may cause the quantum algorithms or quantum circuits to be run on various different quantum computing technology-based quantum computers. Based on the results, the machine learning service may determine one or more optimizations to improve the quantum algorithms or quantum circuits”];
wherein the quantum computing module is configured to transmit the optimized response related to the executed quantum computing task to a computing client [paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed … the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies … execution results received from the first or second quantum hardware provider to be stored and providing, by the quantum computing service, a notification to the customer that the quantum computing task has been completed”; wherein, paragraph 0073, “various quantum computing technologies, such as quantum annealers, ion trap machines, …”].
Bolt does not explicitly teach
a trapped ion computer module configured to trap a set of ions by confining and suspending the set of ions using one or more electromagnetic fields, wherein:
one or more qubits are stored in one or more stable electronic states of each ion, and
quantum information is transferred through collective quantized motions of ions in a shared trap;
wherein the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network;
in response to receiving the request, the quantum computing module uses a machine learning model to generate a prediction associated with a difference in outcome between a quantum-optimized result and a non-quantum-optimized result to the requested computing task,
the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
the machine learning model is refined based on results of predictions generated by the machine learning model,
the machine learning model is configured to determine, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the requested computing task, and
in response to the machine learning model determining to use the quantum computing system, execute the requested computing task,
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task.
Linvill teaches
the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network [abstract, “Methods, systems, and apparatus for solving computational tasks using quantum computing resources”; paragraphs 0036 and 0039, “The system 100 for performing computational tasks is configured to receive, as input, data representing a computational task to be solved, e.g., input data 102. The system 100 may be configured to solve multiple computational tasks, e.g., including optimization tasks, simulation tasks, arithmetic tasks, database search, machine learning tasks, or data compression tasks, and the input data 102 may include data that specifies one of the multiple computational tasks. … the input data 102 may be data that represents the task of optimizing the design of a water network in order to optimize the amount of water distributed by the network … the input data 102 may include data representing one or more parameters associated with the optimization task, e.g., level of water pressure in each pipe, level of water pressure at each connecting node, height of water level in each water tank, concentration of chemicals in the water throughout the network, water age or water source. Furthermore, the input data 102 may include dynamic input data representing one or more current properties or values of parameters of the water network, e.g., a current number of water pipes in use, a current level of water pressure in each pipe, a current concentration of chemicals in the water, or a current temperature of the water”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the computing task includes optimizing the design or configuration of at least one of a product, a device, a vehicle, or a service in a value chain network of Linvill. Doing so would help solving the computational task to optimize the design of the product and/or service in a network (for example, optimizing the amount of water distributed by the network) (Linvill, 0100).
Bolt and Linvill do not teach
a trapped ion computer module configured to trap a set of ions by confining and suspending the set of ions using one or more electromagnetic fields, wherein:
one or more qubits are stored in one or more stable electronic states of each ion, and
quantum information is transferred through collective quantized motions of ions in a shared trap;
in response to receiving the request, the quantum computing module uses a machine learning model to generate a prediction associated with a difference in outcome between a quantum-optimized result and a non-quantum-optimized result to the requested computing task,
the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
the machine learning model is refined based on results of predictions generated by the machine learning model,
the machine learning model is configured to determine, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the requested computing task, and
in response to the machine learning model determining to use the quantum computing system, execute the requested computing task,
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task.
Monroe teaches
a trapped ion computer module configured to trap a set of ions by confining and suspending the set of ions using one or more electromagnetic fields, wherein one or more qubits are stored in one or more stable electronic states of each ion, and quantum information is transferred through collective quantized motions of ions in a shared trap [paragraph 0017, “a trapped ion quantum computer is a type of quantum computer in which ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be processed and transferred through the collective quantized motion of the ions in the trap”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include a trapped ion quantum computer to trap a set of ions by confining and suspending the set of ions using one or more electromagnetic fields, wherein one or more qubits are stored in one or more stable electronic states of each ion, and wherein quantum information is transferred through collective quantized motions of ions in a shared trap of Monroe. Doing so would help scaling up quantum computers and processing quantum information with higher accuracy (Monroe, 0019).
Bolt, Linvill and Monroe do not teach
in response to receiving the request, the quantum computing module uses a machine learning model to generate a prediction associated with a difference in outcome between a quantum-optimized result and a non-quantum- optimized result to the requested computing task,
the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model,
the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions, and
the machine learning model is refined based on results of predictions generated by the machine learning model,
the machine learning model is configured to determine, based on the prediction, which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the requested computing task, and
in response to the machine learning model determining to use the quantum computing system, execute the requested computing task,
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task.
Shen teaches
the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model [paragraph 0360, “performing one or more machine learning algorithms embodied in one or more neural network”; Fig. 10, paragraph 0112, “a machine learning model may be trained by calculating weight parameters according to a neural network architecture … trained machine learning models corresponding to one or more neural networks may be used to infer or predict information”; paragraph 0101, “untrained neural network 1006 is trained using supervised learning, wherein training dataset 1002 includes an input paired with a desired output for an input, or where training dataset 1002 includes input having a known output and an output of neural network 1006 is manually graded … untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs … errors are then propagated back through untrained neural network 1006 …. training framework 1004 adjusts weights that control untrained neural network 1006 … training framework 1004 trains untrained neural network 1006 repeatedly while adjust weights to refine an output of untrained neural network 1006 using a loss function and adjustment algorithm (train and retrain the untrained neural network 1006) … training framework 1004 trains untrained neural network 1006 until untrained neural network 1006 achieves a desired accuracy”],
the machine learning model is at least one of (i) trained by deep learning on a set of outcomes associated with the computing task or (ii) trained on a dataset derived from human expert decisions [paragraphs 0100-0101, “training and deployment of a deep neural network … untrained neural network 1006 is trained using supervised learning, wherein training dataset 1002 includes an input paired with a desired output for an input, or where training dataset 1002 includes input having a known output and an output of neural network 1006 is manually graded … untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs], and
the machine learning model is refined based on results of predictions generated by the machine learning model [paragraph 0101, “untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs … errors are then propagated back through untrained neural network 1006 …. training framework 1004 adjusts weights that control untrained neural network 1006 … training framework 1004 trains untrained neural network 1006 repeatedly while adjust weights to refine an output of untrained neural network 1006 using a loss function and adjustment algorithm (train and retrain the untrained neural network 1006 based on the error) … training framework 1004 trains untrained neural network 1006 until untrained neural network 1006 achieves a desired accuracy”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the machine learning model is included in a dual process artificial neural network (DPANN) system that is configured to train and refine the machine learning model, wherein the machine learning model is refined based on results of predictions generated by the machine learning model of Shen. Doing so would help training a neural network until the neural network achieves a desired accuracy (Shen, 0101).
Bolt, Linvill, Monroe and Shen do not teach
in response to receiving the request, the quantum computing module uses a machine learning model to generate a prediction associated with a difference in outcome between a quantum-optimized result and anon-quantum- optimized result to the requested computing task,
the machine learning model is configured to determine which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the requested computing task based on the prediction, and
in response to the machine learning model determining to use the quantum computing system, execute the requested computing task,
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task.
Lam teaches
in response to receiving the request, the quantum computing module uses a machine learning model to generate a prediction associated with a difference in outcome between a quantum-optimized result and anon-quantum- optimized result to the requested computing task [paragraphs 0020-0021, “receiving parameters … defining a compute workflow to be performed … the compute workflow comprising computing tasks … retrieving data sets from a repository relating to the compute workflow … comparing at least part of the computing tasks and at least part of the data sets for determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks”; paragraph 0071, “compare the computing tasks and at least part of the data sets for determining whether a likelihood of an advantage, for example a performance advantage and/or cost advantage, is probable for the quantum computing when compared to the classical computing for the computing tasks”; paragraphs 0087-0088, “FIGS. 1-6 highlight examples of the machine learning operation … the machine learning operation may operate via ensemble machine learning, which may compare and contrast the results of various machine learning operations to increase the collective confidence of the predicted results … ensemble machine learning may include classical machine learning tasks performed via the classical computing and quantum machine learning tasks performed via the quantum computing … For computing tasks included by the compute workflow indicating a likelihood of an advantage, for example a performance advantage or a cost advantage, is probable and/or favorable from a quantum computing pipeline, quantum machine learning may be applied, as will be understood by those of skill in the art. For computing tasks included by the compute workflow for which a likelihood of the advantage is improbable and/or not favorable from a quantum computing pipeline, classical machine learning may be applied”];
the machine learning model is configured to determine which of: the quantum computing system, a traditional computer system, or a hybrid quantum computing system to use to execute the requested computing task based on the prediction [paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow … distributing a classical computing task to be performed via the classical computing if included by or recommended based on the likelihood of the advantage by the compute workflow”; paragraph 0009, “The system may (g) perform the compute workflow via the computing environment to produce results”; paragraph 0074, “a job scheduler 130 may distribute the workflow to classical computing 140 and quantum computing 150 aspects of the computing environment. The computing environment may then perform the compute workflow to produce results”; paragraph 0007, “The computing environment may include a classical computing processor to perform the classical computing and a quantum computing processor to perform the quantum computing”; paragraphs 0087-0088, “FIGS. 1-6 highlight examples of the machine learning operation … the machine learning operation may operate via ensemble machine learning, which may compare and contrast the results of various machine learning operations to increase the collective confidence of the predicted results … ensemble machine learning may include classical machine learning tasks performed via the classical computing and quantum machine learning tasks performed via the quantum computing … For computing tasks included by the compute workflow indicating a likelihood of an advantage, for example a performance advantage or a cost advantage, is probable and/or favorable from a quantum computing pipeline, quantum machine learning may be applied, as will be understood by those of skill in the art. For computing tasks included by the compute workflow for which a likelihood of the advantage is improbable and/or not favorable from a quantum computing pipeline, classical machine learning may be applied”]; and
in response to the machine learning model determining to use the quantum computing system, execute the requested computing task [paragraph 0072, “Compute workflows may be classified into tasks to be accomplished via quantum computing with the highest probable efficiency, most economically efficient computation … identifying a quantum computing task included by the compute workflow to be performed via the quantum computing”; paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow … performing the compute workflow via the computing environment to produce results];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include generating a prediction associated with a difference in outcome between a quantum- optimized result and a non-quantum-optimized result to the computing task, determining which computing system to use to execute the computing task based on the prediction, and generating a quantum computing task of Lam. Doing so would help performing the compute workflow via either the quantum computing, the classical computing, or both to produce results (Lam, 0021).
Bolt, Linvill, Monroe, Shen and Lam do not teach
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task.
Williams teaches
a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … maximum acceptable objective function value”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include a quantum annealing computing system that is configured to optimize the response by identifying a maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
As per claim 12, Bolt, Linvill, Monroe, Shen, Lam and Williams teach the system of claim 11.
Monroe further teaches
a laser system to induce at least one of a coupling between qubit states, or a coupling between internal qubit states and external motion states [paragraph 0018, “Lasers are usually applied to induce coupling between the qubit states (for single qubit operations), or coupling between the internal qubit states and the external motional states”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include a laser system to induce at least one of a coupling between qubit states, or a coupling between internal qubit states and external motion states of Monroe. Doing so would help imparting qubit state-dependent forces on one or more ions, effecting entangling quantum gates (Monroe, Fig. 2, 0180).
As per claim 13, Bolt, Linvill, Monroe, Shen, Lam and Williams teach the system of claim 11.
Williams further teaches
the quantum annealing computing system is configured to identify a minimum of the objective function over the set of candidate solutions [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … minimum acceptable objective function value”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum annealing computing system is configured to identify a minimum of the objective function over the set of candidate solutions of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
As per claim 15, Bolt, Linvill, Monroe, Shen, Lam and Williams teach the system of claim 11.
Bolt further teaches
the quantum computing module supports a quantum computing model including at least one of: quantum circuit model, quantum Turing machine, spintronic computing system, adiabatic quantum computing system, one-way quantum computer, or quantum cellular automata [abstract, “A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit”; paragraph 0023, “FIG. 15B, illustrates an example process for scheduling execution of a quantum circuit on a quantum computer”; paragraph 0155, “a quantum computing service may support both circuit based quantum computers and annealing based quantum computers].
As per claim 22, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Monroe teaches
the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields; the one or more qubits are stored in stable electronic states of each of the set of ions; and quantum information is transferred through collective quantized motions of the set of ions in a shared trap [paragraph 0017, “a trapped ion quantum computer is a type of quantum computer in which ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be processed and transferred through the collective quantized motion of the ions in the trap”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields, the one or more qubits are stored in stable electronic states of each of the set of ions, and quantum information is transferred through collective quantized motions of the set of ions in a shared trap of Monroe. Doing so would help scaling up quantum computers and processing quantum information with higher accuracy (Monroe, 0019).
As per claim 25, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Shen further teaches
training the machine learning model using a stream of sensor data from at least one value chain network entity of a value chain network [paragraph 0180, “GPU(s) 1220 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks based at least in part on input (e.g., sensor data) from sensors of a vehicle 1200”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include training the machine learning model using a stream of sensor data from at least one value chain network entity of a value chain network of Shen. Doing so would help generating a trained neural network based on the training data (Shen. 0101).
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen et al. in view of Monroe et al. in view of Williams and further in view of Langtin et al. (US Pub. 2018/0330264).
As per claim 3, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Williams further teaches
the identifying the maximum includes an identification of an absolute maximum relating to at least one of: a size, a length, a cost, a time, or a distance from the set of candidate solutions [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … maximum acceptable objective function value … solution criteria include a minimum/maximum number of iterations, a minimum/maximum computation time, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the identifying the maximum includes an identification of an absolute maximum relating to at least one of: a size, a length, a cost, a time, or a distance from the set of candidate solutions of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the identifying the maximum … using a quantum fluctuation-based computation.
Langtin teaches
the identifying the maximum … using a quantum fluctuation-based computation [paragraphs 0334-0336, “the objective function may provide a measure of the degree to which an annealing schedule improves, degrades, or otherwise changes the performance of the problem when executed … an annealing schedule is selected by the digital processor based on the objective function … the annealing schedule which maximizes the objective function may be selected”, wherein, paragraph 0302, “To determine an improved, or optimized, annealing schedule, it can be beneficial to measure quantum fluctuations at different points during annealing”; Since Bolt (as modified) teaches the identifying the maximum includes an identification of an absolute maximum relating to at least a computation time (Williams, 0078), and Langtin teaches the identifying the maximum is based on quantum fluctuations, therefore, the combination of Bolt (as modified) and Langtin read on the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include identifying the maximum using a quantum fluctuation-based computation of Langtin. Doing so would help optimizing annealing schedules which are used by the system for computing the received problem (Langtin, 0339).
As per claim 14, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the system of claim 13.
Williams further teaches
identification of the minimum or the maximum includes an identification of an absolute minimum or an absolute maximum relating to at least one of: a size, a length, a cost, a time, or a distance from the set of candidate solutions [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a … maximum acceptable objective function value … solution criteria include a minimum/maximum number of iterations, a minimum/maximum computation time, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the identifying the maximum includes an identification of an absolute maximum relating to at least one of: a size, a length, a cost, a time, or a distance from the set of candidate solutions using a quantum fluctuation-based computation of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
identification of the minimum or the maximum … using a quantum fluctuation-based computation.
Langtin teaches
identification of the minimum or the maximum … using a quantum fluctuation-based computation [paragraphs 0334-0336, “the objective function may provide a measure of the degree to which an annealing schedule improves, degrades, or otherwise changes the performance of the problem when executed … an annealing schedule is selected by the digital processor based on the objective function … the annealing schedule which maximizes the objective function may be selected”, wherein, paragraph 0302, “To determine an improved, or optimized, annealing schedule, it can be beneficial to measure quantum fluctuations at different points during annealing”; Since Bolt (as modified) teaches the identifying the maximum includes an identification of an absolute maximum relating to at least a computation time (Williams, 0078), and Langtin teaches the identifying the maximum is based on quantum fluctuations, therefore, the combination of Bolt (as modified) and Langtin read on the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include identifying the maximum using a quantum fluctuation-based computation of Langtin. Doing so would help optimizing annealing schedules which are used by the system for computing the received problem (Langtin, 0339).
Claims 5-7 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen et al. in view of Monroe et al. in view of Williams and further in view of AMT (Introduction to Quantum Computing).
As per claim 5, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt teaches in paragraph 0073, “quantum computers including … analog or continuous variable quantum computers”.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not explicitly teach
the quantum computing system is physically implemented using an analog approach.
AMT teaches
the quantum computing system is physically implemented using an analog approach [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: analog … Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum computing system is physically implemented using an analog approach of AMT. Doing so would help performing quantum computation using one of the quantum simulation, quantum annealing, or adiabatic quantum computation (AMT, page 2, 2nd-3rd paragraphs).
As per claim 6, Bolt, Linvill, Lam, Shen, Monroe, Williams and AMT teach the method of claim 5.
AMT further teaches
the analog approach includes at least one of: quantum simulation, quantum annealing, or adiabatic quantum computation [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: analog and … Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the analog approach comprises at least one of quantum simulation, quantum annealing, or adiabatic quantum computation of AMT. Doing so would help performing quantum computation using one of the quantum simulation, quantum annealing, or adiabatic quantum computation (AMT, page 2, 2nd-3rd paragraphs).
As per claim 7, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not explicitly teach
the quantum computing system is physically implemented using a digital approach.
AMT teaches
the quantum computing system is physically implemented using a digital approach [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: digital … Digital quantum computers use quantum logic gates to do computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum computing module is physically implemented using a digital approach of AMT. Doing so would help performing quantum computation using quantum logic gates (AMT, page 2, 2nd-3rd paragraphs).
As per claim 16, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the system of claim 11.
Bolt teaches in paragraph 0073, “quantum computers including … analog or continuous variable quantum computers”.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not explicitly teach
the quantum computing module is physically implemented using an analog approach.
AMT teaches
the quantum computing module is physically implemented using an analog approach [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: analog … Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum computing module is physically implemented using an analog approach of AMT. Doing so would help performing quantum computation using one of the quantum simulation, quantum annealing, or adiabatic quantum computation (AMT, page 2, 2nd-3rd paragraphs).
As per claim 17, Bolt, Linvill, Lam, Shen, Monroe, Williams and AMT teach the system of claim 16.
AMT further teaches
the analog approach includes at least one of: quantum simulation, quantum annealing, or adiabatic quantum computation [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: analog and … Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the analog approach comprises at least one of quantum simulation, quantum annealing, or adiabatic quantum computation of AMT. Doing so would help performing quantum computation using one of the quantum simulation, quantum annealing, or adiabatic quantum computation (AMT, page 2, 2nd-3rd paragraphs).
As per claim 18, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the system of claim 11.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not explicitly teach
the quantum computing module is physically implemented using a digital approach.
AMT teaches
the quantum computing module is physically implemented using a digital approach [page 2, 3rd paragraph, “There are currently two main approaches to physically implementing a quantum computer: digital … Digital quantum computers use quantum logic gates to do computation”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum computing module is physically implemented using a digital approach of AMT. Doing so would help performing quantum computation using quantum logic gates (AMT, page 2, 2nd-3rd paragraphs).
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen et al. in view of Monroe et al. in view of Williams and further in view of Schuster et al. (2022/0156630).
As per claim 8, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the quantum computing system includes an error- corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction.
Schuster teaches
the quantum computing system includes an error-corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction [abstract, “A quantum computer may include physical gate qubits, capable of general quantum gate operations … Because errors accrue at a lower rate in the quantum memory, the physical gate qubits may be able to perform error correction for a large number of logical qubits in the quantum memory”; paragraph 0004, “a system for resource-efficient quantum error correction comprises a plurality of physical gate qubits, wherein each of the plurality of physical gate qubits has an associated gate error rate … and perform a quantum error correcting code on the logical qubit using the plurality of physical gate qubits”; paragraph 0057, “FIG. 1 is a schematic diagram of a quantum computer component with error-correction”; paragraph 0070, “The quantum computer component 10 also includes control components 20. The control components 20 are capable of controlling the physical gate qubits 12 and the qubit couplers 14 shown in FIG. 1 and are capable of performing classical computing tasks, such as determining when error correction is required … the control components 20 may compose or otherwise form a part of a quantum error correction circuit that is configured to perform or control the quantum error correction techniques”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the quantum computing system comprises an error-corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction of Schuster. Doing so would help performing error correction for a large number of logical qubits in the quantum memory (Schuster, abstract).
As per claim 19, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the system of claim 11.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
The system of claim 11 further comprising an error-corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction.
Schuster teaches
The system comprising an error-corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction [abstract, “A quantum computer may include physical gate qubits, capable of general quantum gate operations … Because errors accrue at a lower rate in the quantum memory, the physical gate qubits may be able to perform error correction for a large number of logical qubits in the quantum memory”; paragraph 0004, “a system for resource-efficient quantum error correction comprises a plurality of physical gate qubits, wherein each of the plurality of physical gate qubits has an associated gate error rate … and perform a quantum error correcting code on the logical qubit using the plurality of physical gate qubits”; paragraph 0057, “FIG. 1 is a schematic diagram of a quantum computer component with error-correction”; paragraph 0070, “The quantum computer component 10 also includes control components 20. The control components 20 are capable of controlling the physical gate qubits 12 and the qubit couplers 14 shown in FIG. 1 and are capable of performing classical computing tasks, such as determining when error correction is required … the control components 20 may compose or otherwise form a part of a quantum error correction circuit that is configured to perform or control the quantum error correction techniques”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the system comprising an error-corrected quantum computer that is a gate-based quantum computer with deployed quantum error correction of Schuster. Doing so would help performing error correction for a large number of logical qubits in the quantum memory (Schuster, abstract).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen et al. in view of Monroe et al. in view of Williams and further in view of Achkir (US Pub. 2019/0362287).
As per claim 9, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the computing task relates to automatically discovering smart contract configuration opportunities in a value chain network, and
the quantum computing system is configured to optimize pricing of a smart-container-based freight transportation service via the quantum annealing computing system.
Achkir teaches
the computing task relates to automatically discovering smart contract configuration opportunities in a value chain network [abstract, “Systems, methods, and devices are disclosed for providing real-time updates and predictive functionality in a supply distribution chain of a product. A request is received from a user to view decentralized status information for a product, where the decentralized status information can include real time updates for building the product, assembling the product, shipping the product, and/or exchanging payments between suppliers, partners, or both. This decentralized status information for the product is received from one or more nodes on a distributed network, with the nodes being suppliers and/or partners in the supply distribution chain of the product. The user is granted at least read access to the decentralized status information of the product, as well as a prediction of product build completion based on node supply chain relationships specified within a smart contract”; Since Bolt in paragraphs 0035 and 0037 teaches that a quantum computing service receiving a request to perform a task from a customer, and Achkir teaches the system, based on the received request, providing the decentralized status information of the product, and the prediction of product build completion based on node supply chain relationships specified within a smart contract, therefore, the combination of Bolt and Achkir teaches the above claim limitation], and
the quantum computing system is configured to optimize pricing of a smart-container-based freight transportation service via the quantum annealing computing system [paragraphs 0028-0032, “The decentralized status information can include real time or near real time updates for any number of events associated with building the product (step 208), assembling the product (210), shipping the product (212), or exchanging payments between the one or more of suppliers or partners (214) based on the completion of conditions within contracts … an example supply chain 300 that can be used to process a customer request, from the initial order of the product to the product's final delivery … a smart contract can specify that a manufacturing plan 308 can only be generated once demand forecast 304 and/or supply plan 306 has been added to the blockchain … A manufacturing plan 308 can be developed based on supply plan 306, as well as information from demand forecast 304 that projects which suppliers will be the fastest and/or lowest cost suppliers available. For example, while supply plan 306 may list 10 suppliers who are available to provide a certain component of the product … manufacturing plan 308 may then provide or select that lowest cost supplier over other available suppliers. The manufacturing plan 308 can provide or select the particular supplier based on conditions within smart contracts that optimize the price, speed of manufacture, etc.”; Since Bolt in Figs. 1, 10, paragraphs 0035, 0037 and 0216 teaches that a quantum computing service which including the annealing quantum computer receiving a request to perform a task from a customer, generating one or more response candidates/results, and the process is repeated a number of times until one or more thresholds are met, such as the completion of an optimization problem, while Achkir teaches the system, based on the received request, developing and providing the user the decentralized status information within a smart contract with optimized cost (supplier and shipping cost, etc.,), therefore, the combination of Bolt and Achkir teaches the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the system discovering smart contract configuration opportunities in a value chain network, and optimize pricing of a smart-container-based freight transportation service of Achkir. Doing so would help developing and providing the user the decentralized status information within a smart contract (Achkir, abstract).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen et al. in view of Monroe et al. in view of Williams and further in view of Dukatz et al. (US Pub. 2018/0308000).
As per claim 10, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the computing task relates to at least one of risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly or fraud detection, or generating a prediction.
Dukatz teaches
the computing task relates to at least one of risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly or fraud detection, or generating a prediction [Fig. 2, paragraph 0084, “The database 206 is configured to store data representing properties associated with using the one or more additional computing resources 110a-110d, e.g., one or more quantum computing resources, to solve the multiple computational tasks. For example, properties of using the one or more additional computing resources 110a-110d to solve the multiple computational tasks may include, for each computational task, one or more of (i) approximate qualities of solutions generated by the one or more additional computing resources 110a-110d, (ii) computational times associated with solutions generated by the one or more additional computing resources 110a-110d, or (iii) computational costs associated with solutions generated by the one or more additional computing resources 110a-110d”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the computing task relates to at least one of risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly or fraud detection, or generating a prediction of Dukatz. Doing so would help predicting a response corresponding to the received task.
As per claim 20, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the system of claim 11.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the computing task relates to at least one of: automatically discovering smart contract configuration opportunities in a value chain network, risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly detection, graph clustering analysis for fraud detection, or generating a prediction.
Dukatz teaches
the computing task relates to at least one of: automatically discovering smart contract configuration opportunities in a value chain network, risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly detection, graph clustering analysis for fraud detection, or generating a prediction [Fig. 2, paragraph 0084, “The database 206 is configured to store data representing properties associated with using the one or more additional computing resources 110a-110d, e.g., one or more quantum computing resources, to solve the multiple computational tasks. For example, properties of using the one or more additional computing resources 110a-110d to solve the multiple computational tasks may include, for each computational task, one or more of (i) approximate qualities of solutions generated by the one or more additional computing resources 110a-110d, (ii) computational times associated with solutions generated by the one or more additional computing resources 110a-110d, or (iii) computational costs associated with solutions generated by the one or more additional computing resources 110a-110d”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the computing task relates to at least one of automatically discovering smart contract configuration opportunities in a value chain network, risk identification, risk mitigation, accelerated sampling from stochastic processes for risk analysis, graph clustering analysis for anomaly detection, graph clustering analysis for fraud detection, or generating a prediction of Dukatz. Doing so would help predicting a response corresponding to the received task.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen in view of Monroe et al. in view of Williams and further in view of Chen et al. (US Pub. 2022/0269284).
As per claim 21, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the receiving the request includes receiving a request to optimize at least one of a set of movements of a value chain network entity or a set of routes of the value chain network entity;
the value chain network entity includes at least one of a robot, a robotic fleet, or a smart container; and
the identifying the maximum of the objective function further includes optimizing, using the quantum annealing computing system, the at least one of the set of movements or the set of routes.
Chen teaches
the receiving the request includes receiving a request to optimize at least one of a set of movements of a value chain network entity or a set of routes of the value chain network entity [Figs. 11-12, paragraphs 0066-0071, “method of managing a fleet of robots comprises receiving a first task … receiving, by a processor of a system in which the fleet of robots is configured to operate, a second task, different from the first task … receiving, by the processor of the system, a capability of one or more robots from the one or more robots, determining, by the processor of the system, if the capability of the one or more robots enables the one or more robots to perform the first task and the second task … assigning, by the processor of the system, the first task and the second task to the one or more robots … receiving feedback, by one or more of the processor of the system or a processor of a robot from the fleet of robots, wherein the feedback may include information relating to an object along a route for accessing a location … altering, by the processor of the system, the data in the mission profile, wherein altering the data in the mission profile causes the mission assigned to the first robot to be rescheduled, such that the first robot performs the mission in an order relative to all other missions assigned to the first robot based on the altered data”];
the value chain network entity includes at least one of a robot, a robotic fleet, or a smart container [abstract, “a method includes defining missions based on factors associated with the missions or environmental data associated with the system, assigning the missions to the fleet of robots based on capabilities of the robots, generating a schedule of the missions and the robots, and managing the fleet of robots using feedback”]; and
the identifying the maximum of the objective function further includes optimizing, using the quantum annealing computing system, the at least one of the set of movements or the set of routes [paragraphs 0076-0078, “receiving feedback during performance of the first mission, wherein the feedback includes information relating to the environment, and revising the first mission based on the feedback, wherein revising the first mission causes at least one robot from the one or more robots to alter a scheduled operation … determine one or more routes for accessing the location of a task, wherein altering the scheduled operation includes rescheduling the first missions assigned to the robot, including the first mission, such that the robot performs the missions in a shortest time period”; Since Bolt (as modified) teaches that a quantum computing service which including the annealing quantum computer receiving a request to perform a task from a customer, generating one or more response candidates/results, and the process is repeated a number of times until one or more thresholds are met, such as the completion of an optimization problem (Bolt, Figs. 1, 10, paragraphs 0035, 0037 and 0216), optimizing the response/result using the quantum annealing by identify the maximum value of the objective function (Williams, paragraphs 0077-0078), while Chen teaches a concept of optimizing/managing the fleet of robots comprises altering the schedule (rescheduling) of at least one robot based on the feedback so that the robot performs the missions in a shortest time period, therefore, the combination of Bolt (as modified) and Chen teaches the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include receiving a request to optimize at least one of a set of movements of a value chain network entity or a set of routes of the value chain network entity, the value chain network entity includes at least one of a robot, a robotic fleet, or a smart container, and optimizing the at least one of the set of movements or the set of routes of Chen. Doing so would help generating a schedule and managing the fleet of robots using feedback (Chen, abstract).
Claims 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen in view of Monroe et al. in view of Williams and further in view of Shin (US Pub. 2020/0001465).
As per claim 23, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the receiving the request includes receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design.
Shin teaches
the receiving the request includes receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design [abstract, “an input unit configured to receive a customizing request for the face of the robot, and a processor configured to acquire customizing data based on the receives customizing request, to generate a face design based on the acquired customizing data, and to control the display to display a face image based on the generated face design”; paragraph 0064, “The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm”; paragraph 0135, “the processor 180 may receive input corresponding to the customizing request from the user through the input unit 120”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the receiving the request includes receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design of Shin. Doing so would help generating the optimized design based on the received request (Shin, abstract).
As per claim 24, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Shen further teaches
the generating the prediction includes inputting a set of inputs into the machine learning model [paragraph 0101, “untrained neural network 1006 is trained using supervised learning, wherein training dataset 1002 includes an input paired with a desired output for an input, or where training dataset 1002 includes input having a known output and an output of neural network 1006 is manually graded … untrained neural network 1006 is trained in a supervised manner and processes inputs from training dataset 1002 and compares resulting outputs against a set of expected or desired outputs … errors are then propagated back through untrained neural network 1006 …. training framework 1004 adjusts weights that control untrained neural network 1006 … training framework 1004 trains untrained neural network 1006 repeatedly while adjust weights to refine an output of untrained neural network 1006 using a loss function and adjustment algorithm (train and retrain the untrained neural network 1006) … training framework 1004 trains untrained neural network 1006 until untrained neural network 1006 achieves a desired accuracy”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the generating the prediction includes inputting a set of inputs into the machine learning model of Shen. Doing so would help training a neural network using the input data (Shen, 0101).
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
the set of inputs includes at least one of demand information, supply information, energy cost information, capital costs for computational resources, development cost, energy cost, or performance information on available resources.
Shin further teaches
the set of inputs includes at least one of demand information, supply information, energy cost information, capital costs for computational resources, development cost, energy cost, or performance information on available resources [paragraph 0094, “The robot 100a may perform the above-described operations by using the learning model composed of at least one artificial neural network”; paragraph 0112, “A user may input various requests or commands to the robot 100a through the input unit 120”; abstract, “an input unit configured to receive a customizing request for the face of the robot, and a processor configured to acquire customizing data based on the receives customizing request, to generate a face design based on the acquired customizing data, and to control the display to display a face image based on the generated face design”; paragraph 0064, “The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm”; paragraph 0135, “the processor 180 may receive input corresponding to the customizing request from the user through the input unit 120”; examiner interprets the commands that the user inputs into the device 100a as the demand information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the set of inputs includes at least one of demand information, supply information, energy cost information, capital costs for computational resources, development cost, energy cost, or performance information on available resources of Shin. Doing so would help generating the optimized design based on the received input (Shin, abstract).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen in view of Monroe et al. in view of Williams and further in view of White et al. (US Pub. 2020/0389299).
As per claim 26, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Bolt, Linvill, Lam, Shen, Monroe and Williams do not teach
in response to determining to use the hybrid quantum computing system, performing, via the hybrid quantum computing system, time-division multiplexing between the quantum computing system and the traditional computing system to generate a response to the computing task.
White teaches
in response to determining to use the hybrid quantum computing system, performing, via the hybrid quantum computing system, time-division multiplexing between the quantum computing system and the traditional computing system to generate a response to the computing task [paragraph 0017, “The classical and quantum communications signals, that is the classical messages and second signals, may be time-interleaved in a time-division multiplexed system”; paragraph 0027, “the first signal and the second signal are sent using a time division multiplexing technique”; Since Bolt (as modified) teaches determining a system such as a classical or quantum computing to generate a response (Lam, paragraphs 0021, 0009, 0007, 0072-0074), and White teaches in case the hybrid quantum computing system is determined, performing time-division multiplexing technique, therefore, the combination of Bolt (as modified) and White teaches the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include in response to determining to use the hybrid quantum computing system, performing, via the hybrid quantum computing system, time-division multiplexing between the quantum computing system and the traditional computing system of White. Doing so would help sending the signals over the same optical channel and at the same wavelength (White, 0028).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Shen in view of Monroe et al. in view of Williams and further in view of Rubin (US Patent 11,604,644).
As per claim 27, Bolt, Linvill, Lam, Shen, Monroe and Williams teach the method of claim 1.
Lam teaches
generating a set of quantum computing tasks and a set of traditional computing tasks [paragraph 0072, “Compute workflows may be classified into tasks to be accomplished via quantum computing with the highest probable efficiency, most economically efficient computation”; paragraph 0021, “distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow … distributing a classical computing task to be performed via the classical computing if included by or recommended based on the likelihood of the advantage by the compute workflow”];
executing the set of quantum computing tasks via the quantum computing system [paragraph 0007, “The computing environment may include a classical computing processor to perform the classical computing and a quantum computing processor to perform the quantum computing”; paragraph 0072, “Compute workflows may be classified into tasks to be accomplished via quantum computing with the highest probable efficiency, most economically efficient computation … identifying a quantum computing task included by the compute workflow to be performed via the quantum computing”]; and
executing the set of traditional computing tasks via the traditional computer system [paragraph 0007, “The computing environment may include a classical computing processor to perform the classical computing and a quantum computing processor to perform the quantum computing”; paragraph 0021, “distributing a classical computing task to be performed via the classical computing”].
Bolt, Linvill, Lam, Shen, Monroe and Williams do not explicitly teach
determining to use the hybrid quantum computing system.
Rubin teaches
determining to use the hybrid quantum computing system [abstract, “hybrid quantum/classical algorithms are executed in a computing system”; Col. 1, lines 20-21, “Quantum processors can perform computational tasks by executing quantum algorithms”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include determining to use the hybrid quantum computing system of Rubin. Doing so would help executing hybrid classical/quantum algorithms (Rubin, Col. 1, 27-28).
Claims 28-32 are rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. (US Pub. 2021/0158232) in view of Linvill (US Pub. 2020/0027029) in view of Lam et al. (US Pub. 2021/0287773) in view of Monroe et al. (US Pub. 2018/0114138) and further in view of Williams (US Pub. 2014/0337612).
As per claim 28, Bolt teaches a method for resource allocation, the method comprising:
receiving a request [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed”; paragraph 0039, “the quantum computing object may be a quantum task”; paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed”];
executing the computing task using the quantum computing system [paragraph 0035, “one or more computing devices that implement the quantum computing service are configured to receive, from a customer of the quantum computing service, a definition of a quantum computing object to be executed and select at least one of the first or second quantum hardware providers to execute the quantum computing object; paragraph 0037, “the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies”], wherein executing the computing task using the quantum computing system comprises:
optimizing a response to the request [paragraph 0037, “a method includes receiving, at a quantum computing service implemented on one or more computing devices, from a customer of the quantum computing service, a definition of a quantum computing task to be performed … the first quantum hardware provider and the second quantum hardware provider are configured to execute quantum computing tasks using quantum computers based on different quantum computing technologies … execution results received from the first or second quantum hardware provider to be stored and providing, by the quantum computing service, a notification to the customer that the quantum computing task has been completed”; paragraph 0083, “the machine learning service may cause the quantum algorithms or quantum circuits to be run on various different quantum computing technology-based quantum computers. Based on the results, the machine learning service may determine one or more optimizations to improve the quantum algorithms or quantum circuits”];
Bolt in Fig. 1, paragraph 0073 also teaches “a quantum computing service may provide potential quantum computer users with access to quantum computers using various quantum computing technologies, such as quantum annealers, ion trap machines, superconducting machines, photonic devices, etc.”
Bolt does not explicitly teach
receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design in a value chain network;
generating a prediction using a machine learning model by inputting a set of inputs into the machine learning model, wherein the set of inputs includes at least one of demand information, supply information, energy cost information, capital costs for computational resources, development cost, energy cost, or performance information on available resources;
training the machine learning model using a stream of sensor data from at least one value chain network entity of the value chain network;
determining, based on the prediction, whether to execute a computing task using a quantum computing system, a traditional computer system, or a hybrid quantum computing system;
in response to determining to execute the computing task using the quantum computing system;
trapping a set of ions using one or more electromagnetic fields, wherein the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields;
manipulating one or more qubits represented by the set of ions to execute the computing task, wherein the one or more qubits are stored in stable electronic states of each of the set of ions, and wherein quantum information is transferred through collective quantized motions of the set of ions in a shared trap; and
optimizing a response to the request by identifying at least one of a minimum or a maximum of an objective function over a set of candidate solutions using a quantum annealing computing system, wherein the optimizing includes identifying a minimum of the objective function over the set of candidate solutions.
Linvill teaches
receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design in a value chain network [abstract, “Methods, systems, and apparatus for solving computational tasks using quantum computing resources”; paragraphs 0036 and 0039, “The system 100 for performing computational tasks is configured to receive, as input, data representing a computational task to be solved, e.g., input data 102. The system 100 may be configured to solve multiple computational tasks, e.g., including optimization tasks, simulation tasks, arithmetic tasks, database search, machine learning tasks, or data compression tasks, and the input data 102 may include data that specifies one of the multiple computational tasks. … the input data 102 may be data that represents the task of optimizing the design of a water network in order to optimize the amount of water distributed by the network … the input data 102 may include data representing one or more parameters associated with the optimization task, e.g., level of water pressure in each pipe, level of water pressure at each connecting node, height of water level in each water tank, concentration of chemicals in the water throughout the network, water age or water source. Furthermore, the input data 102 may include dynamic input data representing one or more current properties or values of parameters of the water network, e.g., a current number of water pipes in use, a current level of water pressure in each pipe, a current concentration of chemicals in the water, or a current temperature of the water”];
generating a prediction using a machine learning model by inputting a set of inputs into the machine learning model, wherein the set of inputs includes at least one of demand information, supply information, energy cost information, capital costs for computational resources, development cost, energy cost, or performance information on available resources [paragraph 0049, “The machine learning model may process each machine learning model input to generate a respective machine learning model output”; paragraphs 0017, 0036 and 0039, “receiving, at a machine learning module, the data representing a computational task to be performed … receive, as input, data representing a computational task to be solved, e.g., input data 102. The system 100 may be configured to solve multiple computational tasks, e.g., including optimization tasks, simulation tasks, arithmetic tasks, database search, machine learning tasks, or data compression tasks, and the input data 102 may include data that specifies one of the multiple computational tasks. … the input data 102 may be data that represents the task of optimizing the design of a water network in order to optimize the amount of water distributed by the network … the input data 102 may include data representing one or more parameters associated with the optimization task, e.g., level of water pressure in each pipe, level of water pressure at each connecting node, height of water level in each water tank, concentration of chemicals in the water throughout the network, water age or water source. Furthermore, the input data 102 may include dynamic input data representing one or more current properties or values of parameters of the water network, e.g., a current number of water pipes in use, a current level of water pressure in each pipe, a current concentration of chemicals in the water, or a current temperature of the water”];
training the machine learning model using a stream of sensor data from at least one value chain network entity of the value chain network [paragraph 0049, “The machine learning model may process each machine learning model input to generate a respective machine learning model output”; paragraphs 0017, 0036 and 0039, “receiving, at a machine learning module, the data representing a computational task to be performed … receive, as input, data representing a computational task to be solved, e.g., input data 102. The system 100 may be configured to solve multiple computational tasks, e.g., including optimization tasks, simulation tasks, arithmetic tasks, database search, machine learning tasks, or data compression tasks, and the input data 102 may include data that specifies one of the multiple computational tasks. … the input data 102 may be data that represents the task of optimizing the design of a water network in order to optimize the amount of water distributed by the network … the input data 102 may include a total number of available water pipes, a total number of available connecting nodes or a total number of available water tanks … data representing one or more parameters associated with the optimization task, e.g., level of water pressure in each pipe, level of water pressure at each connecting node, height of water level in each water tank, concentration of chemicals in the water throughout the network, water age or water source. Furthermore, the input data 102 may include dynamic input data representing one or more current properties or values of parameters of the water network, e.g., a current number of water pipes in use, a current level of water pressure in each pipe, a current concentration of chemicals in the water, or a current temperature of the water; paragraph 0062, “the system 100 may be configured to receive a data feed from an internet of things (IoT) sensor”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the process of receiving a request to optimize at least one of a product design, a smart container design, a robot design, a smart container fleet configuration, or a liquid lens design in a value chain network, generating a prediction using a machine learning model and training the machine learning model using a stream of sensor data of Linvill. Doing so would help solving the computational task to optimize the design of the product and/or service in a network (for example, optimizing the amount of water distributed by the network, a total number of water pipes, a total number of connecting nodes or a total number of water tanks, etc.,) (Linvill, 0100).
Bolt and Linvill do not teach
determining, based on the prediction, whether to execute a computing task using a quantum computing system, a traditional computer system, or a hybrid quantum computing system;
in response to determining to execute the computing task using the quantum computing system;
trapping a set of ions using one or more electromagnetic fields, wherein the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields;
manipulating one or more qubits represented by the set of ions to execute the computing task, wherein the one or more qubits are stored in stable electronic states of each of the set of ions, and wherein quantum information is transferred through collective quantized motions of the set of ions in a shared trap; and
optimizing a response to the request by identifying at least one of a minimum or a maximum of an objective function over a set of candidate solutions using a quantum annealing computing system, wherein the optimizing includes identifying a minimum of the objective function over the set of candidate solutions.
Lam teaches
determining, based on the prediction, whether to execute a computing task using a quantum computing system, a traditional computer system, or a hybrid quantum computing system [paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow … distributing a classical computing task to be performed via the classical computing if included by or recommended based on the likelihood of the advantage by the compute workflow”; paragraph 0009, “The system may (g) perform the compute workflow via the computing environment to produce results”; paragraph 0074, “a job scheduler 130 may distribute the workflow to classical computing 140 and quantum computing 150 aspects of the computing environment. The computing environment may then perform the compute workflow to produce results”; paragraph 0007, “The computing environment may include a classical computing processor to perform the classical computing and a quantum computing processor to perform the quantum computing”];
in response to determining to execute the computing task using the quantum computing system [paragraph 0072, “Compute workflows may be classified into tasks to be accomplished via quantum computing with the highest probable efficiency, most economically efficient computation … identifying a quantum computing task included by the compute workflow to be performed via the quantum computing”; paragraph 0021, “determining a likelihood of an advantage for the quantum computing compared to the classical computing for the computing tasks … distributing a quantum computing task to be performed via the quantum computing if included by or recommended based on the likelihood of the advantage by the compute workflow];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include determining which computing system to use to execute the computing task based on the prediction of Lam. Doing so would help performing the compute workflow via either the quantum computing, the classical computing, or both to produce results (Lam, 0021).
Bolt, Linvill and Lam do not teach
trapping a set of ions using one or more electromagnetic fields, wherein the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields;
manipulating one or more qubits represented by the set of ions to execute the computing task, wherein the one or more qubits are stored in stable electronic states of each of the set of ions, and wherein quantum information is transferred through collective quantized motions of the set of ions in a shared trap; and
optimizing a response to the request by identifying at least one of a minimum or a maximum of an objective function over a set of candidate solutions using a quantum annealing computing system, wherein the optimizing includes identifying a minimum of the objective function over the set of candidate solutions.
Monroe teaches
trapping a set of ions using one or more electromagnetic fields, wherein the trapping the set of ions includes suspending the set of ions in free space using the one or more electromagnetic fields, manipulating one or more qubits represented by the set of ions to execute the computing task, wherein the one or more qubits are stored in stable electronic states of each of the set of ions, and wherein quantum information is transferred through collective quantized motions of the set of ions in a shared trap [paragraph 0017, “a trapped ion quantum computer is a type of quantum computer in which ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be processed and transferred through the collective quantized motion of the ions in the trap”; paragraph 0010, “In the operation of a quantum computer, the computations are initialized by setting the qubits in a controlled initial state. By manipulating those qubits, predetermined sequences of quantum logic gates are realized that represent the problem to be solved”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include trapping a set of ions using one or more electromagnetic fields including to suspend the set of ions in free space using the one or more electromagnetic fields, manipulating one or more qubits represented by the set of ions to execute the computing task, wherein the one or more qubits are stored in stable electronic states of each of the set of ions, and wherein quantum information is transferred through collective quantized motions of the set of ions in a shared trap of Monroe. Doing so would help scaling up quantum computers and processing quantum information with higher accuracy (Monroe, 0019).
Bolt, Linvill, Lam and Monroe do not teach
optimizing a response to the request by identifying at least one of a minimum or a maximum of an objective function over a set of candidate solutions using a quantum annealing computing system, wherein the optimizing includes identifying a minimum of the objective function over the set of candidate solutions.
Williams teaches
optimizing a response to the request by identifying at least one of a minimum or a maximum of an objective function over a set of candidate solutions using a quantum annealing computing system, wherein the optimizing includes identifying a minimum of the objective function over the set of candidate solutions [paragraphs 0077-0078, “a hybrid computing system … may be used to perform both quantum annealing by the quantum computational resources and simulated annealing by the digital computational resources to produce two respective candidate solutions. The two candidate solutions may then be compared (e.g., by the digital computational resources or by a separate digital computing system) and the candidate solution that best satisfies some solution criterion may be returned as the result … A solution criterion may include, for example, a characteristic of a candidate solution that is evaluated and used to determine the quality of that candidate solution, such as a minimum/maximum acceptable objective function value”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include a quantum annealing computing system that is configured to optimize the response by identifying a minimum/maximum of an objective function over a set of candidate solutions associated with the requested quantum computing task of Williams. Doing so would help evaluating and determining the quality of the candidate solutions using quantum annealing system (Williams, 0078).
As per claim 29, Bolt, Linvill, Lam, Monroe and Williams teach the method of claim 28.
Williams further teaches
optimizing the response includes optimizing for at least one of: minimizing cost, maximizing performance, minimizing energy consumption, minimizing time, or maximizing throughput [paragraph 0051, “the total cost … in optimizing an objective function may be minimized by using a quantum computing system to determine the value of at least one input parameter that optimizes the objective function”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include optimizing the response includes optimizing for at least one of: minimizing cost, maximizing performance, minimizing energy consumption, minimizing time, or maximizing throughput of Williams. Doing so would help reducing the cost of completing a computational task (Williams, 0006).
As per claim 30, Bolt, Linvill, Lam, Monroe and Williams teach the method of claim 28.
Lam further teaches
the determining whether to execute the computing task further includes:
comparing a predicted quantum-optimized result against a predicted traditional computer result based on at least one of: computational efficiency, cost efficiency, energy efficiency, or time efficiency [paragraph 0071, “determining whether classical computing 140 or quantum computing 150 may be appropriate for a job or other computational task. For example, the jobs definition aspect 120 may compare the computing tasks and at least part of the data sets for determining whether a likelihood of an advantage, for example a performance advantage and/or cost advantage, is probable for the quantum computing when compared to the classical computing for the computing tasks”; paragraph 0088, “the machine learning operation may operate via ensemble machine learning, which may compare and contrast the results of various machine learning operations to increase the collective confidence of the predicted results. In one embodiment, ensemble machine learning may include classical machine learning tasks performed via the classical computing and quantum machine learning tasks performed via the quantum computing. For example, aspects of the job definition aspect may categorize machine learning tasks as having a probability of benefitting from the efficiencies provided via quantum computing. For computing tasks included by the compute workflow indicating a likelihood of an advantage, for example a performance advantage or a cost advantage, is probable and/or favorable from a quantum computing pipeline, quantum machine learning may be applied”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include comparing a predicted quantum-optimized result against a predicted traditional computer result based on at least one of: computational efficiency, cost efficiency, energy efficiency, or time efficiency of Lam. Doing so would help determining if quantum computing system or classical computing system is appropriate for a job or other computational task (Lam, 0071).
As per claim 31, Bolt, Linvill, Lam, Monroe and Williams teach the method of claim 28.
Linvill further teaches
the set of inputs further includes historical outcome data from prior optimization tasks executed in the value chain network [paragraph 0017, “receiving, at a machine learning module, the data representing a computational task to be performed; processing the received data using the machine learning model”; paragraph 0049, “the machine learning module may include a machine learning model that may be trained using training data … The training data may include labeled training examples, e.g., a machine learning model input paired with a respective known machine learning model output”; It can be seen that a known machine learning model output for an input is based on the past outcome of the model, thus, examiner interprets the term “a respective known machine learning model output” as a “historical outcome data”]; and
the machine learning model is refined based on results of the executed computing task [paragraph 0049, “The machine learning model may process each machine learning model input to generate a respective machine learning model output, compute a loss function between the generated machine learning model output and the known machine learning model, and back-propagate gradients to adjust machine learning model parameters from initial values to trained values”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the set of inputs includes historical outcome data, and the machine learning model is refined based on results of the executed computing task of Linvill. Doing so would help improving the accuracy and efficiency of the performance of the machine learning model.
As per claim 32, Bolt, Linvill, Lam, Monroe and Williams teach the method of claim 28.
Linvill further teaches
the stream of sensor data includes data from at least one of: Internet of Things sensors, product condition sensors, environmental sensors, operational sensors, or performance monitoring sensors deployed on the at least one value chain network entity [paragraph 0062, “the system 100 may be configured to receive a data feed from an internet of things (IoT) sensor”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include the stream of sensor data includes data from at least one of: Internet of Things sensors, product condition sensors, environmental sensors, operational sensors, or performance monitoring sensors deployed on the at least one value chain network entity of Linvill. Doing so would help processing the received input to generate a machine learning model output (Linvill, 0049).
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Bolt et al. in view of Linvill in view of Lam et al. in view of Monroe et al. in view of Williams and further in view of Chen et al. (US Pub. 2022/0269284).
As per claim 33, Bolt, Linvill, Lam, Monroe and Williams teach the method of claim 28.
Bolt, Linvill, Lam, Monroe and Williams do not explicitly teach
receiving the request includes receiving a request to optimize movements or routes of at least one of: a robot, a robotic fleet, a smart container, or a smart container fleet in the value chain network.
Chen teaches
receiving the request includes receiving a request to optimize movements or routes of at least one of: a robot, a robotic fleet, a smart container, or a smart container fleet in the value chain network [Figs. 11-12, paragraphs 0066-0071, “method of managing a fleet of robots comprises receiving a first task … receiving, by a processor of a system in which the fleet of robots is configured to operate, a second task, different from the first task … receiving, by the processor of the system, a capability of one or more robots from the one or more robots, determining, by the processor of the system, if the capability of the one or more robots enables the one or more robots to perform the first task and the second task … assigning, by the processor of the system, the first task and the second task to the one or more robots … receiving feedback, by one or more of the processor of the system or a processor of a robot from the fleet of robots, wherein the feedback may include information relating to an object along a route for accessing a location … altering, by the processor of the system, the data in the mission profile, wherein altering the data in the mission profile causes the mission assigned to the first robot to be rescheduled, such that the first robot performs the mission in an order relative to all other missions assigned to the first robot based on the altered data”; paragraphs 0076-0078, “receiving feedback during performance of the first mission, wherein the feedback includes information relating to the environment, and revising the first mission based on the feedback, wherein revising the first mission causes at least one robot from the one or more robots to alter a scheduled operation … determine one or more routes for accessing the location of a task, wherein altering the scheduled operation includes rescheduling the first missions assigned to the robot, including the first mission, such that the robot performs the missions in a shortest time period”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for providing quantum computing services of Bolt to include receiving the request includes receiving a request to optimize movements or routes of at least one of: a robot, a robotic fleet, a smart container, or a smart container fleet in the value chain network of Chen. Doing so would help generating a schedule and managing the fleet of robots using the received request/feedback (Chen, abstract).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Hastings et al. (US Patent 10,176,433) describes a quantum optimization algorithm for creating schedules used to operate a quantum computing device during a quantum computational process.
Johnson et al. (US Patent 10,484,479) describes a method of for distributing computing software frameworks to integrate quantum processing devices into their workflow.
Conclusion
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/TRI T NGUYEN/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128