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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a system. Therefore, the claim is eligible under Step 1 for being directed to machine.
Step 2A Prong One
Claim 1 recites
accessing configuration information of a product-under-development (PUD); (input data)
providing the configuration information of the PUD to one or more physics-informed simulation models (PISMs) of the processor system; (mere instructions to apply an exception)
and using the one or more PISMs to predict debris-related properties of the PUD operating in an environment. (mental process)
The claimed concept is a method of determining runway orientation by evaluating wind observation data directed to “Mental Process” grouping.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The accessing data step is recited at a high level of generality (i.e., as a general means of collecting input for use in the evaluation step) and amounts to mere data collecting, which is a form of insignificant extra-solution activity.
The claim recites additional elements such as “computer system comprising a processor system electronically coupled to a memory”. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. See applicant’s specification [0062] Fig 9. for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The computer system of claim 1, wherein the environment comprises a hard-to-predict environment having multi-factor stressors comprising debris. (data description)
3. The computer system of claim 2, wherein the debris is circulating and comprises multiple debris types. (mental process)
4. The computer system of claim 1, wherein the processor system operations further comprise computer-aided-design (CAD) operations that simulate the debris-related properties of the PUD in the environment. (mere instructions to apply an exception)
5. The computer system of claim 4, wherein the environment comprises a hard-to-predict environment having multi-factor stressors comprising debris. (data description)
6. The computer system of claim 5, wherein the debris comprises multiple debris types. (mental process)
7. The computer system of claim 1, wherein debris-related properties of the PUD in the environment comprise a score representing how difficult it would be for a cleaning system to remove some or all of debris present on the PUD. (mental process)
Same conclusion for independent claims 8, 14 and dependent claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 8-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kapp et al (US 2021/0078735 A1), hereinafter Kapp.
Claim 1. Kapp discloses A computer system comprising a processor system electronically coupled to a memory, wherein the processor system performs processor system operations comprising:
Kapp: [0010] “Another aspect of the present disclosure is a computer program product including one or more non-transitory machine-readable mediums having instructions encoded thereon that, when executed by one or more processors on board a space based asset, …”
Kapp discloses accessing configuration information of a product-under-development (PUD); providing the configuration information of the PUD to one or more physics-informed simulation models (PISMs) of the processor system; and
Kapp: [0044] “Referring to FIG. 2, one embodiment of an abstract, high-level architecture for a reinforcement learning physics-based system according to the principles of the present disclosure is shown. More specifically, the system comprises an environment 4, an agent 2, and an interface 12. In one embodiment, a physics-based space simulator (e.g. STK, AFSIM, etc.) was used to simulate real world physics and maneuvers of objects (e.g. satellites and anti-satellite missiles) [correspond to PUD] in the environment 4. Since the system of the present disclosure was implemented using a standardized software interface (e.g., Open AI Gym), the interface could be used with Advanced Framework for Simulation, Integration, and Modeling (AFSIM), or another framework and the reinforcement learning algorithms would remain the same. These simulators were used to generate the same kinds of data provided by space data providers, which provide location and velocity information on a variety of space objects. This data was provided as observations to a reinforcement learning agent 2 of the present disclosure so the agent knows where things are in the environment, in a realistic way.”
Kapp discloses using the one or more PISMs to predict debris-related properties of the PUD operating in an environment.
Kapp: [0053] “To be sure, the problem of satellite safety has several constraints that make real life training of reinforcement learning difficult. Satellite safety operates in the real world, which cannot be sped up nor parallelized, and generating scenarios, especially negative ones, is prohibitively expensive since a loss of a satellite creates debris clouds which can damage other satellites. It is believed that if a sufficient number of such experiments occur in the real world it would generate enough debris that space would be largely unusable for all. For all of these reasons it is highly desirable (if not essential) to use a simulation of such a situation (or environment).”
Regarding Claim 8, same ground of rejection is made as discussed above for substantially similar rationale of claim 1.
Claim 2, 9
Kapp discloses wherein the environment comprises a hard-to-predict environment having multi-factor stressors comprising debris.
Kapp: [0031] “Space is becoming increasingly congested and contested with more and more countries (allies and adversaries alike) developing the capability to place assets on orbit. Concurrently with those capabilities, many countries are pursuing counter-space capabilities that put other's space-based assets at risk. These capabilities are becoming ever more numerous and complex. As such, satellite operators, for example, face an increasingly difficult situation should a space-based conflict begin. There are also space environment issues such as space debris, space junk, and solar flares that may adversely affect the life and operation of a space asset. The technology described herein is designed to assist satellite operators in their decision-making with the ultimate goal of increasing the survivability of space-based assets. In certain embodiments of the present disclosure, a system using reinforcement machine learning is used to mitigate threats, whether man-made or natural, to satellites. The present system is capable of learning to stay on mission as long as possible while still dodging (or otherwise mitigating) threats. The capability for threat mitigation in the space domain can potentially be translated to the air domain. The solution of the present disclosure may be optimized to fit on embedded systems such as satellite, space craft, aircraft, drones, missiles, and the like.”
Claim 3, 10
Kapp discloses wherein the debris is circulating and comprises multiple debris types.
Kapp: [0052] “Additionally, the system enhancements could include adding additional threats; jamming, dazzlers, cyber, debris, other satellites and the like. Additional action could be added, as well as maintaining attitude (e.g., magnetic torquer, spin stabilization, mass-expulsion control (MEC), or the like). The threats could also be more realistic. Initially, the missile followed a simple parabolic trajectory, but in the real world an anti-satellite missile most likely would be capable of maneuvering to intercept the satellite within some range. In some cases, a reinforcement learning system for the missile could be used to optimize the likelihood of hitting the satellite and a reinforcement learning system could be used on the satellite which simultaneously tries to dodge the missile.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-7, 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapp et al (US 2021/0078735 A1), hereinafter Kapp, in view of Casale et al (NPL: Lyapunov Optimal Touchless Electrostatic Detumbling of Space Debris in GEO Using a Surface Multisphere Model, 2021), hereinafter Casale.
Claim 4, 11
Kapp does not appear to explicitly disclose wherein the processor system operations further comprise computer-aided-design (CAD) operations that simulate the debris-related properties of the PUD in the environment.
However, Casale discloses computer-aided-design (CAD) operations that simulate the debris-related properties of the PUD in the environment. (page 765 Section II) “Multisphere Model Generation Procedure”
Kapp and Casale are analogous art because they are from the “same field of endeavor” space simulation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kapp and Casale before him or her, to modify the simulation of Kapp to include the multisphere model of Casale because this combination improve performance of the simulation.
The suggestion/motivation for doing so would have been Casale (page 765) “For this reason, the next advancement switches to generating a greater number of spheres on the conductor surface, and is thus called surface MSM (SMSM) [30]; it is faster to set up and it yields more precise torque estimations with just a small increase of computational effort. Also, it represents more accurately the behavior of real electric charges that always position themselves on the surface of the objects.”
Therefore, it would have been obvious to combine Kapp and Casale to obtain the invention as specified in the instant claim(s).
Claim 5. The computer system of claim 4,
wherein the environment comprises a hard-to-predict environment having multi-factor stressors comprising debris.
Kapp: [0031] “Space is becoming increasingly congested and contested with more and more countries (allies and adversaries alike) developing the capability to place assets on orbit. Concurrently with those capabilities, many countries are pursuing counter-space capabilities that put other's space-based assets at risk. These capabilities are becoming ever more numerous and complex. As such, satellite operators, for example, face an increasingly difficult situation should a space-based conflict begin. There are also space environment issues such as space debris, space junk, and solar flares that may adversely affect the life and operation of a space asset. The technology described herein is designed to assist satellite operators in their decision-making with the ultimate goal of increasing the survivability of space-based assets. In certain embodiments of the present disclosure, a system using reinforcement machine learning is used to mitigate threats, whether man-made or natural, to satellites. The present system is capable of learning to stay on mission as long as possible while still dodging (or otherwise mitigating) threats. The capability for threat mitigation in the space domain can potentially be translated to the air domain. The solution of the present disclosure may be optimized to fit on embedded systems such as satellite, space craft, aircraft, drones, missiles, and the like.”
Claim 6. The computer system of claim 5, wherein the debris comprises multiple debris types.
Kapp: [0052] “Additionally, the system enhancements could include adding additional threats; jamming, dazzlers, cyber, debris, other satellites and the like. Additional action could be added, as well as maintaining attitude (e.g., magnetic torquer, spin stabilization, mass-expulsion control (MEC), or the like). The threats could also be more realistic. Initially, the missile followed a simple parabolic trajectory, but in the real world an anti-satellite missile most likely would be capable of maneuvering to intercept the satellite within some range. In some cases, a reinforcement learning system for the missile could be used to optimize the likelihood of hitting the satellite and a reinforcement learning system could be used on the satellite which simultaneously tries to dodge the missile.”
Claim 7, 13
Kapp discloses wherein debris-related properties of the PUD in the environment comprise a score representing how difficult it would be for a cleaning system to remove some or all of debris present on the PUD.
Kapp: [0048] I”n one embodiment, the environment was limited to a scenario with a single fixed missile trajectory and a single fixed initial satellite trajectory over 1200 seconds. In this embodiment, due to time and space constraint, time steps were augmented down by a factor of five such that each step the agent sees is equal to five seconds. The environment terminated the scenario either at the last time step or when the distance between the satellite and missile come below a certain safety threshold set in the beginning. In this example, that was 5000 meters. At each time step, the environment sent the agent its state and reward. In one embodiment, only the xyz positions and velocities of the satellite and missile were provided as state information. The agent then decided either to do nothing or perform a given evasive maneuver. The agent initially received five points for staying on mission, zero for performing a maneuver, and a large negative reward for failing to move out of the danger threshold. The goal is to train the agent to stay on mission as long as possible, but move before the impact point.”
It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the scenario to include how difficult it would be for a cleaning system to remove some or all of debris present on the PUD, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable actions involves only routine skill in the art. In re Aller, 105 USPQ 233.
Claim 12. The computer-implemented method of claim 11, wherein:
Kapp discloses the environment comprises a hard-to-predict environment having multi-factor stressors comprising debris; Kapp: [0031]
and the debris comprises multiple debris types. Kapp: [0052]
Claim(s) 14-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapp et al (US 2021/0078735 A1), hereinafter Kapp, in view of Warner et al (NPL: Assessing next-gen spacesuit reliability: a probabilistic analysis case study at NASA, 2021), hereinafter Warner.
Claim 14. A computer system comprising a processor system electronically coupled to a memory, Kapp discloses wherein the processor system performs processor system operations comprising:
Kapp: [0010] “Another aspect of the present disclosure is a computer program product including one or more non-transitory machine-readable mediums having instructions encoded thereon that, when executed by one or more processors on board a space based asset, …”
Kapp discloses accessing configuration information of a product-under-development (PUD), providing the configuration information of the EMU to one or more physics-informed simulation models (PISMs) of the processor system;
Kapp: [0044] “Referring to FIG. 2, one embodiment of an abstract, high-level architecture for a reinforcement learning physics-based system according to the principles of the present disclosure is shown. More specifically, the system comprises an environment 4, an agent 2, and an interface 12. In one embodiment, a physics-based space simulator (e.g. STK, AFSIM, etc.) was used to simulate real world physics and maneuvers of objects (e.g. satellites and anti-satellite missiles) [correspond to PUD] in the environment 4. Since the system of the present disclosure was implemented using a standardized software interface (e.g., Open AI Gym), the interface could be used with Advanced Framework for Simulation, Integration, and Modeling (AFSIM), or another framework and the reinforcement learning algorithms would remain the same. These simulators were used to generate the same kinds of data provided by space data providers, which provide location and velocity information on a variety of space objects. This data was provided as observations to a reinforcement learning agent 2 of the present disclosure so the agent knows where things are in the environment, in a realistic way.”
Kapp discloses using the one or more PISMs to predict debris-related properties of the EMU in a space environment.
Kapp: [0053] “To be sure, the problem of satellite safety has several constraints that make real life training of reinforcement learning difficult. Satellite safety operates in the real world, which cannot be sped up nor parallelized, and generating scenarios, especially negative ones, is prohibitively expensive since a loss of a satellite creates debris clouds which can damage other satellites. It is believed that if a sufficient number of such experiments occur in the real world it would generate enough debris that space would be largely unusable for all. For all of these reasons it is highly desirable (if not essential) to use a simulation of such a situation (or environment).”
Kapp does not appear to explicitly disclose wherein the PUD comprises an extravehicular mobility unit (EMU);
However, Warner discloses wherein the PUD comprises an extravehicular mobility unit (EMU) on (page 2) “Designing the Exploration Extravehicular Mobility Unit (xEMU) spacesuit”
Kapp and Warner are analogous art because they are from the “same field of endeavor” space simulation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kapp and Warner before him or her, to modify the simulation of Kapp to include the analysis feature for EMU of Warner because this combination improves performance of the simulation.
The suggestion/motivation for doing so would have been Warner (page 3) “Motivated by the xEMU, a case study was conducted to demonstrate the application of uncertainty quantification (UQ) to estimate spacesuit reliability.”
Therefore, it would have been obvious to combine Kapp and Warner to obtain the invention as specified in the instant claim(s).
Claim 15. The computer system of claim 14, Kapp discloses wherein the space environment comprises a hard-to-predict space environment having multi-factor stressors comprising debris.
Kapp: [0031] “Space is becoming increasingly congested and contested with more and more countries (allies and adversaries alike) developing the capability to place assets on orbit. Concurrently with those capabilities, many countries are pursuing counter-space capabilities that put other's space-based assets at risk. These capabilities are becoming ever more numerous and complex. As such, satellite operators, for example, face an increasingly difficult situation should a space-based conflict begin. There are also space environment issues such as space debris, space junk, and solar flares that may adversely affect the life and operation of a space asset. The technology described herein is designed to assist satellite operators in their decision-making with the ultimate goal of increasing the survivability of space-based assets. In certain embodiments of the present disclosure, a system using reinforcement machine learning is used to mitigate threats, whether man-made or natural, to satellites. The present system is capable of learning to stay on mission as long as possible while still dodging (or otherwise mitigating) threats. The capability for threat mitigation in the space domain can potentially be translated to the air domain. The solution of the present disclosure may be optimized to fit on embedded systems such as satellite, space craft, aircraft, drones, missiles, and the like.”
Claim 16. The computer system of claim 15, Kapp discloses wherein the debris is circulating
Kapp: [0052] “Additionally, the system enhancements could include adding additional threats; jamming, dazzlers, cyber, debris, other satellites and the like. Additional action could be added, as well as maintaining attitude (e.g., magnetic torquer, spin stabilization, mass-expulsion control (MEC), or the like). The threats could also be more realistic. Initially, the missile followed a simple parabolic trajectory, but in the real world an anti-satellite missile most likely would be capable of maneuvering to intercept the satellite within some range. In some cases, a reinforcement learning system for the missile could be used to optimize the likelihood of hitting the satellite and a reinforcement learning system could be used on the satellite which simultaneously tries to dodge the missile.”
Warner discloses debris comprises lunar regolith.
(page 21) “soil model calibration for simulating impact on lunar regolith”
Claim 19. The computer system of claim 14, Kapp discloses wherein debris-related properties of the EMU in the space environment comprise a score representing how difficult it would be for a cleaning system to remove some or all of debris from the EMU.
Kapp: [0048] I”n one embodiment, the environment was limited to a scenario with a single fixed missile trajectory and a single fixed initial satellite trajectory over 1200 seconds. In this embodiment, due to time and space constraint, time steps were augmented down by a factor of five such that each step the agent sees is equal to five seconds. The environment terminated the scenario either at the last time step or when the distance between the satellite and missile come below a certain safety threshold set in the beginning. In this example, that was 5000 meters. At each time step, the environment sent the agent its state and reward. In one embodiment, only the xyz positions and velocities of the satellite and missile were provided as state information. The agent then decided either to do nothing or perform a given evasive maneuver. The agent initially received five points for staying on mission, zero for performing a maneuver, and a large negative reward for failing to move out of the danger threshold. The goal is to train the agent to stay on mission as long as possible, but move before the impact point.”
It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the scenario to include how difficult it would be for a cleaning system to remove some or all of debris present on the PUD, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable actions involves only routine skill in the art. In re Aller, 105 USPQ 233.
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapp et al (US 2021/0078735 A1), hereinafter Kapp, in view of Warner et al (NPL: Assessing next-gen spacesuit reliability: a probabilistic analysis case study at NASA, 2021), hereinafter Warner and further in view of Casale et al (NPL: Lyapunov Optimal Touchless Electrostatic Detumbling of Space Debris in GEO Using a Surface Multisphere Model, 2021), hereinafter Casale.
Claim 17. The computer system of claim 14, Kapp does not appear to explicitly disclose wherein the processor system operations further comprise computer-aided-design (CAD) operations that simulate the debris-related properties of the EMU in the space environment.
However, Casale discloses computer-aided-design (CAD) operations that simulate the debris-related properties of the PUD in the environment. (page 765 Section II) “Multisphere Model Generation Procedure”
Kapp, Warner and Casale are analogous art because they are from the “same field of endeavor” space simulation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kapp, Warner and Casale before him or her, to modify the simulation of Kapp to include the analysis feature for EMU of Warner and the multisphere model of Casale because this combination improve performance of the simulation.
The suggestion/motivation for doing so would have been Casale (page 765) “For this reason, the next advancement switches to generating a greater number of spheres on the conductor surface, and is thus called surface MSM (SMSM) [30]; it is faster to set up and it yields more precise torque estimations with just a small increase of computational effort. Also, it represents more accurately the behavior of real electric charges that always position themselves on the surface of the objects.”
Therefore, it would have been obvious to combine Kapp, Warner and Casale to obtain the invention as specified in the instant claim(s).
Claim 18. The computer system of claim 17, Kapp discloses wherein: the space environment comprises a hard-to-predict space environment having multi-factor stressors comprising debris;
Kapp: [0031] “Space is becoming increasingly congested and contested with more and more countries (allies and adversaries alike) developing the capability to place assets on orbit. Concurrently with those capabilities, many countries are pursuing counter-space capabilities that put other's space-based assets at risk. These capabilities are becoming ever more numerous and complex. As such, satellite operators, for example, face an increasingly difficult situation should a space-based conflict begin. There are also space environment issues such as space debris, space junk, and solar flares that may adversely affect the life and operation of a space asset. The technology described herein is designed to assist satellite operators in their decision-making with the ultimate goal of increasing the survivability of space-based assets. In certain embodiments of the present disclosure, a system using reinforcement machine learning is used to mitigate threats, whether man-made or natural, to satellites. The present system is capable of learning to stay on mission as long as possible while still dodging (or otherwise mitigating) threats. The capability for threat mitigation in the space domain can potentially be translated to the air domain. The solution of the present disclosure may be optimized to fit on embedded systems such as satellite, space craft, aircraft, drones, missiles, and the like.”
Kapp discloses the debris comprises lunar regolith.
(page 21) “soil model calibration for simulating impact on lunar regolith”
Allowable Subject Matter
Claim 20 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome 101 rejection.
The following is a statement of reasons for the indication of allowable subject matter:
Kapp et al (US 2021/0078735 A1) teach a method for using a reinforcement machine learning based solution for space applications for automated course of action recommendations for the mitigation of threats to space-based assets. The system can be used to mitigate threats to satellites, and it can be used generally as a multi-domain reinforcement machine learning environment for many different kinds of agents, performing many different kinds of actions, under many different simulated environmental conditions.
Warner et al (NPL: Assessing next-gen spacesuit reliability: a probabilistic analysis case study at NASA, 2021) teach a method for designing the Exploration Extravehicular Mobility Unit (xEMU) spacesuit based on the probability of no impact failure.
Casale et al (NPL: Lyapunov Optimal Touchless Electrostatic Detumbling of Space Debris in GEO Using a Surface Multisphere Model, 2021) teach a method of applying a Lyapunov optimal control in conjunction with a surface multisphere model. This approach allows for the analysis of general shapes, eliminating the need for analytical approximations on debris shape and expected torque, employed by previous work. Moreover, using this model, the robustness of the system to uncertainties to the debris center of mass position is tested.
These references taken either alone or in combination with the prior art of record fail to disclose limitations, including:
Claim 20: wherein the one or more PISMs comprise a generative adversarial network having a physics-informed generator and a multimodal discriminator.
in combination with the remaining elements and features of the claimed invention.
Conclusion
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189
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