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 .
Status of Claims
This Office Action is in response to the application filed on October 07, 2024. Claims 1-20 are presently pending and are presented for examination.
Double Patenting
Claims 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12051335 as follows:
Current Application’s claims
Parent Application’s claims
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4
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5
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7
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20
Although the claims at issue are not identical, they are not patentably distinct from each other because parent claims additionally include receiving state data from a plurality of aircraft, the state data specifying position data and heading data for each of the plurality of aircraft. The current claims do not introduce a patentably distinct inventive concept beyond the parent claims. Reference in 103 rejection below teaches the limitation and therefore it is obvious to modify the parent application to include the limitation(s) in this application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission.
For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-l.jsp.
Claim Rejections - 35 USC 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process that can be performed in the human mind or via mathematical optimization. Claims 1, 13, and 20 of the applicant are directed to an abstract idea of aircraft path planning and resource allocation, utilizing a partitioner to manage qubit assignment and generate navigational parameters based on cost. More specifically, the claim recites “partitioning a plurality of qubits... based on maneuverability options" and "generating a first solution representing... a lowest-cost first maneuverability option”; which falls within the “Mathematical Concepts” and “Mental Processes” groupings of abstract ideas.
Dependent claims 2-12 and 14-19 do not comprise any further limitations which, when considered individually or as an ordered combination, cause the abstract idea to be integrated into a practical application or recite significantly more than the abstract idea. Therefore, claims 2-12 and 14-19 are also rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 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 discloses as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8-11, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2014/0214257 (hereinafter, "Williams") in view of U.S. Pub. No. 2012/0191333 (hereinafter, "Sawhill").
Regarding claim 1, Williams discloses a system for quantum-computing-based aircraft path planning (“integration of quantum computing systems into mobile systems for the purpose of providing real-time, quantum computer-based control of the mobile systems” (para 0001) and “on-board quantum computing resources to determine an optimal (or at least, “good enough”) path through the navigation challenge” (para 0020)), the system comprising: a classical computing system (Fig. 2, #250); a quantum computing system (Fig. 1, #123) communicatively coupled to the classical computing system (“ The mobile system may include an action subsystem that is communicatively coupled to the quantum computing subsystem, wherein the action subsystem controllably causes the mobile system to perform an action, and wherein the result of the quantum computing operation may be used by the action subsystem to influence at least one parameter of the action performed by the mobile system” (para 0006)), wherein the classical computing system is configured to:
partition a plurality of qubits of the quantum computing system between a plurality of aircraft (“the programming subsystem may be configured to map candidate investments to the qubits of the quantum processor such that each candidate investment corresponds to at least one qubit in the quantum processor, and to map correlations between respective pairs of the candidate investments to the coupling devices of the quantum processor such that each correlation corresponds to at least one coupling device in the quantum processor” (para 0035)) based on one or more respective first groups of mutually exclusive maneuverability options … (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: …, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006) and “Exemplary mobile systems include, but are not limited to: … air-based mobile systems such as aircraft, planes, jets, helicopters, rockets, etc.” (para 0002)); and
wherein the quantum computing system is configured to:
generate a first solution representing, for each of the one or more respective first groups of mutually exclusive maneuverability options for each of the plurality of aircraft (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: a speed of the mobile system, a direction of the mobile system, a velocity of the mobile system, an acceleration of the mobile system, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006)),
However, Williams does not explicitly teach
… for each of the plurality of aircraft;
a respective lowest-cost first maneuverability option for each of the one or more respective aircraft.
Sawhill, in the same field of endeavor, teaches
… for each of the plurality of aircraft (“planning, disruption management, and optimization of networked, scheduled or on-demand air transport fleet trajectory operations from gate-to-gate (departure to arrival airport)” (para 0257) and “modifying the degrees of freedom for maneuvering by either increasing the dimensionality allowed for deconfliction (allowing vertical maneuvers) or decreasing the separation standard” (para 0131));
a respective lowest-cost first maneuverability option for each of the one or more respective aircraft (“Separation and a number of other factors influence the Trajectories of aircraft. Paths must be constructed (planned and replanned) to optimize many competing goals and constraints. These goals can be expressed in terms of monetized Cost Functions. Hard constraints like Separation are abstracted as very steep Cost Functions. Soft constraints like on-time arrival and goals like conserving fuel are monetized. The goal is to compute Paths that lie on the Pareto frontier of cost functions” (para 0040) and ).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Regarding claim 8, Williams discloses the system of claim 7. However, Williams does not explicitly teach
wherein each of the plurality of total inter-aircraft repulsion costs is equal to a sum of an inter-aircraft repulsion cost for each of the plurality of aircraft for a given group of mutually exclusive qubits.
Sawhill, in the same field of endeavor, teaches
wherein each of the plurality of total inter-aircraft repulsion costs is equal to a sum of an inter-aircraft repulsion cost for each of the plurality of aircraft for a given group of mutually exclusive qubits (“The "fictitious forces" generated between the charged strings in the trajectory representation will repel the strings enough so as to ensure aircraft separation, but the counteracting string tension will ensure the minimum cost trajectory subject to this constraint” (para 0077) and “Agents use Cost Functions to evaluate Path options. Cost Functions quantify issues like separation... Optimization is achieved by minimizing overall "costs" associated with a Path” (para 0036)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to provide a smooth, well-behaved density measure without discontinuities; see Sawhill at least at [0130].
Regarding claim 9, Williams discloses the system of claim 1. Additionally, Williams discloses wherein the path planning system is configured to transmit a control signal, from a control system to one or more of the plurality of aircraft, the control signal comprising instructions for navigation of the one or more of the plurality of aircraft based on the first solution (“wherein a result of the quantum computing operation influences a behavior of the mobile system. The mobile system may further include a navigation subsystem that is communicatively coupled to both the quantum computing subsystem and the mobility subsystem, wherein the navigation subsystem may control the mobility subsystem, and wherein the result of the quantum computing operation may be used by the navigation subsystem to influence the mobility subsystem” (para 0006)).
Regarding claim 10, Williams discloses the system of claim 1. Additionally, Williams discloses wherein: the classical computing system is configured to transmit the one or more groups of mutually exclusive maneuverability options to the quantum computing system (“map correlations between respective pairs of the candidate investments to the coupling devices of the quantum processor such that each correlation corresponds to at least one coupling device in the quantum processor. As illustrated in FIG. 2, programming interfaces 222, 223, and 225 of the programming subsystem of quantum processor 200 may be communicatively coupled, via communication conduits 251 and 252, to mobile system behavior control module 250” (para 0035)), and the quantum computing system is configured to transmit the generated solution to the classical computing system (“The mobile system may include an action subsystem that is communicatively coupled to the quantum computing subsystem” (para 0006)).
Regarding claim 11, Williams discloses the system of claim 1. Additionally, Williams discloses wherein the one or more respective first groups of maneuverability options represent one or more of: a change in direction, a change in speed, and a change in altitude (“The at least one parameter of the navigation subsystem may influence at least one of: a speed of the mobile system, a direction of the mobile system, a velocity of the mobile system, an acceleration of the mobile system, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006)).
Regarding claim 13, Williams discloses a method for quantum-computing-based aircraft path planning (“integration of quantum computing systems into mobile systems for the purpose of providing real-time, quantum computer-based control of the mobile systems” (para 0001) and “on-board quantum computing resources to determine an optimal (or at least, “good enough”) path through the navigation challenge” (para 0020)), the method performed at a path-planning system comprising a classical computing system (Fig. 2, #250) and a quantum computing system (Fig. 1, #123) communicatively coupled to the classical computing system (“a navigation subsystem that is communicatively coupled to both the quantum computing subsystem and the mobility subsystem, The mobile system may include an action subsystem that is communicatively coupled to the quantum computing subsystem, wherein the action subsystem controllably causes the mobile system to perform an action, and wherein the result of the quantum computing operation may be used by the action subsystem to influence at least one parameter of the action performed by the mobile system” (para 0006)), the method comprising: partition, by the classical computing system, a plurality of qubits of the quantum computing system between a plurality of aircraft (“the programming subsystem may be configured to map candidate investments to the qubits of the quantum processor such that each candidate investment corresponds to at least one qubit in the quantum processor, and to map correlations between respective pairs of the candidate investments to the coupling devices of the quantum processor such that each correlation corresponds to at least one coupling device in the quantum processor” (para 0035)) based on one or more respective first groups of mutually exclusive maneuverability options for each of the plurality of aircraft (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: …, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006) and “Exemplary mobile systems include, but are not limited to: … air-based mobile systems such as aircraft, planes, jets, helicopters, rockets, etc.” (para 0002)); and generate, by the quantum computing system, a first solution representing, for each of the one or more respective first groups of mutually exclusive maneuverability options … (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: a speed of the mobile system, a direction of the mobile system, a velocity of the mobile system, an acceleration of the mobile system, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006))
However, Williams does not explicitly teach
… for the plurality of aircraft;
a respective lowest-cost first maneuverability option for each of the one or more respective aircraft.
Sawhill, in the same field of endeavor, teaches
… for the plurality of aircraft (“planning, disruption management, and optimization of networked, scheduled or on-demand air transport fleet trajectory operations from gate-to-gate (departure to arrival airport)” (para 0257));
a respective lowest-cost first maneuverability option for each of the one or more respective aircraft (“Separation and a number of other factors influence the Trajectories of aircraft. Paths must be constructed (planned and replanned) to optimize many competing goals and constraints. These goals can be expressed in terms of monetized Cost Functions. Hard constraints like Separation are abstracted as very steep Cost Functions. Soft constraints like on-time arrival and goals like conserving fuel are monetized. The goal is to compute Paths that lie on the Pareto frontier of cost functions” (para 0040)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Regarding claim 20, Williams discloses a non-transitory computer-readable storage medium storing instructions for quantum-computing-based aircraft path planning (“integration of quantum computing systems into mobile systems for the purpose of providing real-time, quantum computer-based control of the mobile systems” (para 0001) and “on-board quantum computing resources to determine an optimal (or at least, “good enough”) path through the navigation challenge” (para 0020)) that, when executed by one or more processors of a path-planning system comprising a classical computing system (Fig. 2, #250) and a quantum computing system (Fig. 1, #123) communicatively coupled to one another (“a navigation subsystem that is communicatively coupled to both the quantum computing subsystem and the mobility subsystem, The mobile system may include an action subsystem that is communicatively coupled to the quantum computing subsystem, wherein the action subsystem controllably causes the mobile system to perform an action, and wherein the result of the quantum computing operation may be used by the action subsystem to influence at least one parameter of the action performed by the mobile system” (para 0006)), cause the path-planning system to:
partition, by the classical computing system, a plurality of qubits of the quantum computing system between a plurality of aircraft (“the programming subsystem may be configured to map candidate investments to the qubits of the quantum processor such that each candidate investment corresponds to at least one qubit in the quantum processor, and to map correlations between respective pairs of the candidate investments to the coupling devices of the quantum processor such that each correlation corresponds to at least one coupling device in the quantum processor” (para 0035)) based on one or more respective first groups of mutually exclusive maneuverability options for each of the plurality of aircraft (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: …, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006) and “Exemplary mobile systems include, but are not limited to: … air-based mobile systems such as aircraft, planes, jets, helicopters, rockets, etc.” (para 0002)); and
generate, by the quantum computing system, a first solution representing, for each of the one or more respective first groups of mutually exclusive maneuverability options (“The quantum computing operation performed by the quantum computing subsystem may include a real-time optimization of at least one parameter of the navigation subsystem based on data from the data extraction subsystem. The at least one parameter of the navigation subsystem may influence at least one of: a speed of the mobile system, a direction of the mobile system, a velocity of the mobile system, an acceleration of the mobile system, a trajectory of the mobile system, a travel route of the mobile system, a travel time of the mobile system, and a destination of the mobile system” (para 0006))...
However, Williams does not explicitly teach
… for the plurality of aircraft, a respective lowest-cost first maneuverability option for each of the one or more respective aircraft.
Sawhill, in the same field of endeavor, teaches
… for the plurality of aircraft (“planning, disruption management, and optimization of networked, scheduled or on-demand air transport fleet trajectory operations from gate-to-gate (departure to arrival airport)” (para 0257)), a respective lowest-cost first maneuverability option for each of the one or more respective aircraft (“Separation and a number of other factors influence the Trajectories of aircraft. Paths must be constructed (planned and replanned) to optimize many competing goals and constraints. These goals can be expressed in terms of monetized Cost Functions. Hard constraints like Separation are abstracted as very steep Cost Functions. Soft constraints like on-time arrival and goals like conserving fuel are monetized. The goal is to compute Paths that lie on the Pareto frontier of cost functions” (para 0040)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Claims 4-7 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2014/0214257 (hereinafter, "Williams") in view of U.S. Pub. No. 2012/0191333 (hereinafter, "Sawhill") as applied to claims 1 and 13 above, and in further view of U.S. Pub. No. 2020/0349509 (hereinafter, "Sharma").
Regarding claim 4, Williams discloses the system of claim 1. However, Williams does not explicitly teach wherein the one or more respective first groups of mutually exclusive maneuverability options include data representing a quadratic unconstrained binary optimization, the data representing the quadratic unconstrained binary optimization including a plurality of matrices.
Sharma, in the same field of endeavor, teaches
wherein the one or more respective first groups of mutually exclusive maneuverability options include data representing a quadratic unconstrained binary optimization (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)), the data representing the quadratic unconstrained binary optimization including a plurality of matrices (“wherein the one or more preferences relate to maximizing a valuation associated with the route order, minimizing a distance associated with the route order, minimizing a travel time associated with the route order, or minimizing a cost associated with the route order; identify one or more parameters for determining the route order for the plurality of locations; generate a quantum model based on the first information, the second information, and the one or more parameters” (para 0004)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to generate a quantum model based on the representation, the one or more preferences, and the one or more parameters; see Sharma at least at [0005].
Regarding claim 5, Williams discloses the system of claim 4. However, Williams does not explicitly teach wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total distances-to-target.
Sharma, in the same field of endeavor, teaches
wherein the data representing the quadratic unconstrained binary optimization (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)) comprises a plurality of total distances-to-target (“represent weightings according to respective priorities assigned to maximizing valuation, minimizing time, and minimizing distance” (para 0033) and “the route platform may determine distances and/or travel times between the plurality of locations using a shortest path solver” (para 0021)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to determine a shortest route that minimizes a distance and/or a cost associated with the route; see Sharma at least at [0012].
Regarding claim 6, Williams discloses the system of claim 5. However, Williams does not explicitly teach wherein each of the plurality of total distances-to-target is equal to a sum of a distance-to-target for each of the plurality of aircraft for a given group of mutually exclusive qubits.
Sharma, in the same field of endeavor, teaches
wherein each of the plurality of total distances-to-target is equal to a sum of a distance-to-target (“determine distances and/or travel times between the plurality of locations using a shortest path solver” ) for each of the plurality of aircraft (“A logistics enterprise may be associated with one or more vehicles (e.g.,... aircraft)” (para 0016)) for a given group of mutually exclusive qubits (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to determine a shortest route that minimizes a distance and/or a cost associated with the route; see Sharma at least at [0012].
Regarding claim 7, Williams discloses the system of claim 4. However, Williams does not explicitly teach
wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total inter-aircraft repulsion costs.
Sharma, in the same field of endeavor, teaches
wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total inter-aircraft repulsion costs (“generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations (e.g., based on the representation of the plurality of locations) and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to generate a quantum model based on the representation, the one or more preferences, and the one or more parameters; see Sharma at least at [0005].
Sawhill, in the same field of endeavor, teaches
wherein the data comprises a plurality of total inter-aircraft repulsion costs (“Separation and a number of other factors influence the Trajectories of aircraft. Paths must be constructed (planned and replanned) to optimize many competing goals and constraints. These goals can be expressed in terms of monetized Cost Functions” (para 0040)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Regarding claim 16, Williams discloses the method of claim 13. However, Williams does not explicitly teach wherein the one or more respective first groups of mutually exclusive maneuverability options include data representing a quadratic unconstrained binary optimization, the data representing the quadratic unconstrained binary optimization including a plurality of matrices.
Sharma, in the same field of endeavor, teaches
wherein the one or more respective first groups of mutually exclusive maneuverability options include data representing a quadratic unconstrained binary optimization (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)), the data representing the quadratic unconstrained binary optimization including a plurality of matrices (“wherein the one or more preferences relate to maximizing a valuation associated with the route order, minimizing a distance associated with the route order, minimizing a travel time associated with the route order, or minimizing a cost associated with the route order; identify one or more parameters for determining the route order for the plurality of locations; generate a quantum model based on the first information, the second information, and the one or more parameters” (para 0004)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to generate a quantum model based on the representation, the one or more preferences, and the one or more parameters; see Sharma at least at [0005].
Regarding claim 17, Williams discloses the method of claim 16. However, Williams does not explicitly teach wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total distances-to-target.
Sharma, in the same field of endeavor, teaches
wherein the data representing the quadratic unconstrained binary optimization (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)) comprises a plurality of total distances-to-target (“represent weightings according to respective priorities assigned to maximizing valuation, minimizing time, and minimizing distance” (para 0033) and “the route platform may determine distances and/or travel times between the plurality of locations using a shortest path solver” (para 0021)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to determine a shortest route that minimizes a distance and/or a cost associated with the route; see Sharma at least at [0012].
Regarding claim 18, Williams discloses the method of claim 17. However, Williams does not explicitly teach wherein each of the plurality of total distances-to-target is equal to a sum of a distance-to-target for each of the plurality of aircraft for a given group of mutually exclusive qubits.
Sharma, in the same field of endeavor, teaches
wherein each of the plurality of total distances-to-target is equal to a sum of a distance-to-target (“determine distances and/or travel times between the plurality of locations using a shortest path solver” ) for each of the plurality of aircraft (“A logistics enterprise may be associated with one or more vehicles (e.g.,... aircraft)” (para 0016)) for a given group of mutually exclusive qubits (“the route platform may generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters.” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to determine a shortest route that minimizes a distance and/or a cost associated with the route; see Sharma at least at [0012].
Regarding claim 19, Williams discloses the method of claim 16. However, Williams does not explicitly teach wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total inter-aircraft repulsion costs.
Sharma, in the same field of endeavor, teaches
wherein the data representing the quadratic unconstrained binary optimization comprises a plurality of total inter-aircraft repulsion costs (“generate a quadratic unconstrained binary optimization (QUBO) problem based on the information relating to the plurality of locations (e.g., based on the representation of the plurality of locations) and may convert the QUBO problem to the matrix, the route platform may apply one or more constraints to the QUBO problem to obtain a constrained QUBO problem. The one or more constraints may be based on the information relating to the one or more preferences and/or the one or more parameters” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sharma in order to generate a quantum model based on the representation, the one or more preferences, and the one or more parameters; see Sharma at least at [0005].
Sawhill, in the same field of endeavor, teaches
wherein the data comprises a plurality of total inter-aircraft repulsion costs (“Separation and a number of other factors influence the Trajectories of aircraft. Paths must be constructed (planned and replanned) to optimize many competing goals and constraints. These goals can be expressed in terms of monetized Cost Functions” (para 0040)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Claims 2-3, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2014/0214257 (hereinafter, "Williams") in view of U.S. Pub. No. 2012/0191333 (hereinafter, "Sawhill") as applied to claims 1 and 13 above, and in further view of U.S. Pat. No. 8,868,328 (hereinafter, "Estkowski").
Regarding claim 2, Williams discloses the system of claim 1. However, Williams does not explicitly teach wherein the classical computing system is configured to: calculate a first respective distance to a target for each of the plurality of aircraft, wherein calculating the first respective distances is based on state data corresponding respectively to the plurality of aircraft, the state data specifying respective position data and respective heading data for the plurality of aircraft; and
calculate, based on the state data, a first respective inter-aircraft repulsion…, wherein the one or more respective first groups of mutually exclusive maneuverability options are generated based on one or more of the first respective distances to the target for the plurality of aircraft and based on one or more of the first respective inter-aircraft repulsions for the plurality of aircraft.
Estkowski, in the same field of endeavor, teaches
wherein the classical computing system is configured to: calculate a first respective distance to a target … (“A plurality of fat paths may be calculated between a time referenced position of an aircraft and reference point based on maneuvering characteristics of the aircraft and a probabilistic zone of interest for other aircraft” (Col. 3, lines 18-22) and “One or more homotopically distinct regions of travel, referred to herein as fat paths, may be mapped for the aircraft between any point on its path and destination points. The fat paths are based on distance to destination and maneuver constraints” (Col. 4, lines 5-9)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Estkowski in order to determine feasible routing path options based on distance to destination and maneuver constraints; see Estkowski at least at [Col. 4, lines 8-9].
Sawhill, in the same field of endeavor, teaches
… for each of the plurality of aircraft (“planning, disruption management, and optimization of networked, scheduled or on-demand air transport fleet trajectory operations from gate-to-gate (departure to arrival airport)” (para 0257));
wherein calculating the first respective distances is based on state data (“For the density computation, a Gaussian integral, applied to the distance from each aircraft to the measurement point, is used. This provided the probability density of finding an aircraft at the specified point if the aircraft positions are considered to have an uncertainty specified by a spread parameter.” (para 0130)) corresponding respectively to the plurality of aircraft (Fig. 15A, #576A-B)), the state data specifying respective position data and respective heading data for the plurality of aircraft (“The behavior of the airspace is a function of aircraft density, flight path geometries, mixes of aircraft types and performance, and separation minima” (para 0130)); and
calculate, based on the state data, a first respective inter-aircraft repulsion for each of the plurality of aircraft (“the entry and exit points for each respective trajectory are initially positioned roughly based on the information known about the respective aircraft at the time of trajectory negotiation or entry into the airspace given its position entry an intended destination. The FIG. 4 shows a conceptual airspace model with trajectories of aircraft entering that have been deconflicted, i.e. deformed to enforce minimum separation” (para 0125) and “the probability density of finding an aircraft at the specified point if the aircraft positions are considered to have an uncertainty specified by a spread parameter” (para 0130)), wherein the one or more respective first groups of mutually exclusive maneuverability options are generated based on one or more of the first respective distances to the target for the plurality of aircraft (“the continual replanning of trajectories incorporates objective functions for the separation and maneuvering of the aircraft” (para 0024) and “For the density computation, a Gaussian integral, applied to the distance from each aircraft to the measurement point, is used” (para 0130)) and based on one or more of the first respective inter-aircraft repulsions for the plurality of aircraft (“the continual replanning of trajectories incorporates objective functions for the separation and maneuvering of the aircraft” (para 0024)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Regarding claim 3, Williams discloses the system of claim 2. However, Williams does not explicitly teach wherein the classical computing system is configured to:
calculate, based at least in part on the respective lowest-cost first maneuverability option for each of the plurality of aircraft, a second respective distance to target for each of the plurality of aircraft and a second respective inter-aircraft repulsion for each of the plurality of aircraft, wherein the second respective distances to target and second respective inter-aircraft repulsions represent a subsequent time-step with respect to the first respective distance to target and first respective inter-aircraft repulsion; and
generate one or more respective second groups of mutually exclusive maneuverability options for each of the plurality of respective aircraft, wherein the one or more respective second groups of mutually exclusive maneuverability options are generated based on the second respective distance to the target for each of the plurality of aircraft and based on the second respective inter-aircraft repulsion for each of the plurality of aircraft; and
the quantum computing system is configured to: generate a second solution representing, for each of the one or more respective second groups of mutually exclusive maneuverability options for the plurality of aircraft, a respective lowest-cost second maneuverability option for each of the one or more respective aircraft.
Sawhill, in the same field of endeavor, teaches
wherein: the classical computing system is configured to:
calculate, based at least in part on the respective lowest-cost first maneuverability option for each of the plurality of aircraft (“Optimization is achieved by minimizing overall “costs” associated with a Path” (para 0036)), ... a second respective inter-aircraft repulsion (“identify effective approaches for separation assurance for aircraft trajectories” (para 0020)) for each of the plurality of aircraft (“managing fleets of aircraft trajectories” (para 0066)), wherein the second respective distances to target and second respective inter-aircraft repulsions represent a subsequent time-step (“Continuous Replanning has a time granularity of Delta T. The Delta T value is set according to the agility required to react in a timely way to disruptions. The Delta T is mediated by available computational resources, communications latencies, and other factors affecting the lead times required to take management actions to implement flight path changes derived from the system and technique” (para 0028)) with respect to the first respective distance to target “an internal force of elasticity is applied to each trajectory causing the trajectories to follow ever more flyable, relatively shorter curved paths, conserving fuel, while still maintaining separation via the repulsive inter-trajectory force. Elasticity can be thought of the tendency for short sections of a trajectory to imitate the natural curve of longer sections of the trajectory” (para 0246)) and first respective inter-aircraft repulsion (“The system attempts to carefully deform the trajectories such that separation is enforced, and the paths are always flyable” (para 0202)); and
generate one or more respective second groups of mutually exclusive maneuverability options for each of the plurality of respective aircraft (“continuous replanning of flight paths so as to continually adjust all future flight paths to take into account current and forecast externalities” (para 0022) and “modifying the degrees of freedom for maneuvering by either increasing the dimensionality allowed for deconfliction (allowing vertical maneuvers) or decreasing the separation standard” (para 0131)), wherein the one or more respective second groups of mutually exclusive maneuverability options are generated based on the second respective distance to the target for each of the plurality of aircraft and based on the second respective inter-aircraft repulsion for each of the plurality of aircraft (“The resulting trajectory optimization calculations allow for frequent, real-time updating of trajectories (i.e., in seconds or minutes as appropriate to the need), to account for the impact of disruptions on each flight, based on the primary capital or operating cost function being optimized” (para 0073), “an internal force of elasticity is applied to each trajectory causing the trajectories to follow ever more flyable, relatively shorter curved paths, conserving fuel, while still maintaining separation via the repulsive inter-trajectory force. Elasticity can be thought of the tendency for short sections of a trajectory to imitate the natural curve of longer sections of the trajectory” (para 0246)), and “The system attempts to carefully deform the trajectories such that separation is enforced, and the paths are always flyable” (para 0202)); and
the quantum computing system is configured to: generate a second solution representing, for each of the one or more respective second groups of mutually exclusive maneuverability options for the plurality of aircraft (“The fleet optimization may be implemented through assignment and management of trajectories (flight plans) for each aircraft. These trajectories may be produced to satisfy multiple constraints” (para 0267) and “Continuous Replanning has a time granularity of Delta T. The Delta T value is set according to the agility required to react in a timely way to disruptions. The Delta T is mediated by available computational resources, communications latencies, and other factors affecting the lead times required to take management actions to implement flight path changes derived from the system and technique” (para 0028)), a respective lowest-cost second maneuverability option for each of the one or more respective aircraft (“These trajectories may be produced to satisfy multiple constraints, including minimized time-of-flight, optimized fuel burn (and carbon), and optimum Direct Operating Cost (DOC). These trajectories may be de-conflicted within an operator's fleet and the available regional air traffic flow data” (para 0267) and “a series of Paths generated at each Delta T by Continuous Replanning. At each delta T, the best Path is (re-)calculated from that point in time into the future” (para 0032)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to continually determine the best Path to fly; see Sawhill at least at [0033].
Estkowski, in the same field of endeavor, teaches
calculate … a second respective distance to target for each of the plurality of aircraft (“A plurality of fat paths may be calculated between a time referenced position of an aircraft and reference point based on maneuvering characteristics of the aircraft and a probabilistic zone of interest for other aircraft” (Col. 3, lines 18-22) and “One or more homotopically distinct regions of travel, referred to herein as fat paths, may be mapped for the aircraft between any point on its path and destination points. The fat paths are based on distance to destination, maneuver constraints” (Col. 4, lines 5-9)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Estkowski in order to determine feasible routing path options based on distance to destination and maneuver constraints; see Estkowski at least at [Col. 4, lines 8-9].
Regarding claim 12, Williams discloses the system of claim 1. However, Williams does not explicitly teach wherein the classical computing system is configured to generate the one or more respective first groups of maneuverability options, the generating comprising:
determining a zone for the respective aircraft based on the state data; and
generating the one or more groups of respective first maneuverability options in accordance with one or more maneuverability option constraints applicable to the determined zone.
Estkowski, in the same field of endeavor, teaches
wherein the classical computing system is configured to generate the one or more respective first groups of maneuverability options (“A plurality of fat paths may be calculated between a time referenced position of an aircraft and reference point based on maneuvering characteristics of the aircraft and a probabilistic zone of interest for other aircraft” (Col. 3, lines 17-22)), the generating comprising:
determining a zone for the respective aircraft based on the state data (“determining a present location of a control vehicle within two presently overlapping fat paths wherein a fat path comprises a homotopically distinct region of travel” (Col. 1, lines 33-36); and
generating the one or more groups of respective first maneuverability options in accordance with one or more maneuverability option constraints applicable to the determined zone (“Based on maneuverability characteristics and speed of the control vehicle, constraints may be placed on the control vehicle, a fat path may be generated along a subset of the plurality of trajectory paths to maintain separation of the control vehicle from the second vehicle” (Col. 3, lines 32-38)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Estkowski in order to scheduled arrival time while observing constraints regarding safety; see Estkowski at least at [Col. 1, lines 22-24].
Regarding claim 14, Williams discloses the method of claim 13, However, Williams does not explicitly teach calculating, by the classical computing system, a first respective distance to a target for each of the plurality of aircraft, wherein calculating the first respective distances is based on state data corresponding respectively to the plurality of aircraft, the state data specifying respective position data and respective heading data for the plurality of aircraft; and
calculating, by the classical computing system, based on the state data, a first respective inter-aircraft repulsion for each of the plurality of aircraft, wherein the one or more respective first groups of mutually exclusive maneuverability options are generated based on one or more of the first respective distances to the target for the plurality of aircraft and based on one or more of the first respective inter-aircraft repulsions for the plurality of aircraft.
Estkowski, in the same field of endeavor, teaches
calculating, by the classical computing system, a first respective distance to a target for each of the plurality of aircraft (“A plurality of fat paths may be calculated between a time referenced position of an aircraft and reference point based on maneuvering characteristics of the aircraft and a probabilistic zone of interest for other aircraft” (Col. 3, lines 18-22) and “One or more homotopically distinct regions of travel, referred to herein as fat paths, may be mapped for the aircraft between any point on its path and destination points. The fat paths are based on distance to destination, maneuver constraints” (Col. 4, lines 5-9)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Estkowski in order to determine feasible routing path options based on distance to destination and maneuver constraints; see Estkowski at least at [Col. 4, lines 8-9].
Sawhill, in the same field of endeavor, teaches
wherein calculating the first respective distances is based on state data (“For the density computation, a Gaussian integral, applied to the distance from each aircraft to the measurement point, is used. This provided the probability density of finding an aircraft at the specified point if the aircraft positions are considered to have an uncertainty specified by a spread parameter.” (para 0130)) corresponding respectively to the plurality of aircraft (Fig. 15A, #576A-B)), the state data specifying respective position data and respective heading data for the plurality of aircraft (“The behavior of the airspace is a function of aircraft density, flight path geometries, mixes of aircraft types and performance, and separation minima” (para 0130)); and
calculating, by the classical computing system, based on the state data, a first respective inter-aircraft repulsion for each of the plurality of aircraft (“the entry and exit points for each respective trajectory are initially positioned roughly based on the information known about the respective aircraft at the time of trajectory negotiation or entry into the airspace given its position entry an intended destination. The FIG. 4 shows a conceptual airspace model with trajectories of aircraft entering that have been deconflicted, i.e. deformed to enforce minimum separation” (para 0125) and “the probability density of finding an aircraft at the specified point if the aircraft positions are considered to have an uncertainty specified by a spread parameter” (para 0130)), wherein the one or more respective first groups of mutually exclusive maneuverability options are generated based on one or more of the first respective distances to the target for the plurality of aircraft (“the continual replanning of trajectories incorporates objective functions for the separation and maneuvering of the aircraft” (para 0024) and “For the density computation, a Gaussian integral, applied to the distance from each aircraft to the measurement point, is used” (para 0130)) and based on one or more of the first respective inter-aircraft repulsions for the plurality of aircraft (“the continual replanning of trajectories incorporates objective functions for the separation and maneuvering of the aircraft” (para 0024)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to achieve optimization by minimizing overall costs associated with a Path; see Sawhill at least at [0036].
Regarding claim 15, Williams discloses the method of claim 14. However, Williams does not explicitly teach calculating, by the classical computing system, based at least in part on the respective lowest-cost first maneuverability option for each of the plurality of aircraft, a second respective distance to target for each of the plurality of aircraft and a second respective inter-aircraft repulsion for each of the plurality of aircraft, wherein the second respective distances to target and second respective inter-aircraft repulsions represent a subsequent time-step with respect to the first respective distance to target and first respective inter-aircraft repulsion; and
generating, by the classical computing system, one or more respective second groups of mutually exclusive maneuverability options for each of the plurality of respective aircraft, wherein the one or more respective second groups of mutually exclusive maneuverability options are generated based on the second respective distance to the target for each of the plurality of aircraft and based on the second respective inter-aircraft repulsion for each of the plurality of aircraft; and generating, by the quantum computing system, a second solution representing, for each of the one or more respective second groups of mutually exclusive maneuverability options for the plurality of aircraft, a respective lowest-cost second maneuverability option for each of the one or more respective aircraft.
Sawhill, in the same field of endeavor, teaches
calculating, by the classical computing system, based at least in part on the respective lowest-cost first maneuverability option for each of the plurality of aircraft (“Optimization is achieved by minimizing overall “costs” associated with a Path” (para 0036))..., a second respective inter-aircraft repulsion (“identify effective approaches for separation assurance for aircraft trajectories” (para 0020)) for each of the plurality of aircraft (“managing fleets of aircraft trajectories” (para 0066)), wherein the second respective distances to target and second respective inter-aircraft repulsions represent a subsequent time-step (“Continuous Replanning has a time granularity of Delta T. The Delta T value is set according to the agility required to react in a timely way to disruptions. The Delta T is mediated by available computational resources, communications latencies, and other factors affecting the lead times required to take management actions to implement flight path changes derived from the system and technique” (para 0028)) with respect to the first respective distance to target “an internal force of elasticity is applied to each trajectory causing the trajectories to follow ever more flyable, relatively shorter curved paths, conserving fuel, while still maintaining separation via the repulsive inter-trajectory force. Elasticity can be thought of the tendency for short sections of a trajectory to imitate the natural curve of longer sections of the trajectory” (para 0246)) and first respective inter-aircraft repulsion (“The system attempts to carefully deform the trajectories such that separation is enforced, and the paths are always flyable” (para 0202)); and
generating, by the classical computing system, one or more respective second groups of mutually exclusive maneuverability options for each of the plurality of respective aircraft (“continuous replanning of flight paths so as to continually adjust all future flight paths to take into account current and forecast externalities” (para 0022)), wherein the one or more respective second groups of mutually exclusive maneuverability options are generated based on the second respective distance to the target for each of the plurality of aircraft and based on the second respective inter-aircraft repulsion for each of the plurality of aircraft (“The resulting trajectory optimization calculations allow for frequent, real-time updating of trajectories (i.e., in seconds or minutes as appropriate to the need), to account for the impact of disruptions on each flight, based on the primary capital or operating cost function being optimized” (para 0073), “an internal force of elasticity is applied to each trajectory causing the trajectories to follow ever more flyable, relatively shorter curved paths, conserving fuel, while still maintaining separation via the repulsive inter-trajectory force. Elasticity can be thought of the tendency for short sections of a trajectory to imitate the natural curve of longer sections of the trajectory” (para 0246)), and “The system attempts to carefully deform the trajectories such that separation is enforced, and the paths are always flyable” (para 0202)); and
generating, by the quantum computing system, a second solution representing, for each of the one or more respective second groups of mutually exclusive maneuverability options for the plurality of aircraft (“The fleet optimization may be implemented through assignment and management of trajectories (flight plans) for each aircraft. These trajectories may be produced to satisfy multiple constraints” (para 0267)), a respective lowest-cost second maneuverability option for each of the one or more respective aircraft (“These trajectories may be produced to satisfy multiple constraints, including minimized time-of-flight, optimized fuel burn (and carbon), and optimum Direct Operating Cost (DOC). These trajectories may be de-conflicted within an operator's fleet and the available regional air traffic flow data” (para 0267) and “a series of Paths generated at each Delta T by Continuous Replanning. At each delta T, the best Path is (re-)calculated from that point in time into the future” (para 0032)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Sawhill in order to continually determine the best Path to fly; see Sawhill at least at [0033];
Estkowski, in the same field of endeavor, teaches
calculating … a second respective distance to target for each of the plurality of aircraft (“A plurality of fat paths may be calculated between a time referenced position of an aircraft and reference point based on maneuvering characteristics of the aircraft and a probabilistic zone of interest for other aircraft” (Col. 3, lines 18-22) and “One or more homotopically distinct regions of travel, referred to herein as fat paths, may be mapped for the aircraft between any point on its path and destination points. The fat paths are based on distance to destination, maneuver constraints” (Col. 4, lines 5-9)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Williams with the teachings of Estkowski in order to determine feasible routing path options based on distance to destination and maneuver constraints; see Estkowski at least at [Col. 4, lines 8-9].
Conclusion
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663