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 is in response to Applicant’s case, no. 18/672,666, with an effective filing date of 5/23/2024. Claims 8 and 16 are currently pending. Claims 1-7, 9-15, and 17-20 have been canceled.
Response to Arguments
It appears the inventor(s) filed the current application pro se (i.e., without the benefit of representation by a registered patent practitioner). While inventors named as applicants in a patent application may prosecute the application pro se, lack of familiarity with patent examination practice and procedure may result in missed opportunities in obtaining optimal protection for the invention disclosed. The inventor(s) may wish to secure the services of a registered patent practitioner to prosecute the application, because the value of a patent is largely dependent upon skilled preparation and prosecution. The Office cannot aid in selecting a patent practitioner.
A listing of registered patent practitioners is available at https://oedci.uspto.gov/OEDCI/. Applicants may also obtain a list of registered patent practitioners located in their area by writing to Mail Stop OED, Director of the U.S. Patent and Trademark Office, P.O. Box 1450, Alexandria, VA 22313-1450.
Applicant should submit an argument under the heading “Remarks” pointing out disagreements with the Examiner’s contentions. Applicant must also discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them.
The Examiner acknowledges the changes made regarding the Specification and Claim Objection sections in Applicant’s response which subsequently renders the prior objections to those sections moot. However, based on the amendments made to both the abstract of the disclosure and to the claims, new objections are made toward the Specification and Claim Objection sections as detailed below.
Regarding the 35 USC § 103 rejection of claims 1-20 as being unpatentable over Freiheit (US Pat. Pub. No. 2022/0406196 A1) in view of Haney et al. (US Pat. Pub. No. 2022/0234765 A1), the Applicant has elected to amend the aforementioned claims. Therefore, the Examiner’s rejection in the previous Office Action based on 35 USC § 103 is rendered moot. However, due to said amendments, new references Catledge et al. (US Pat. Pub. No. 2025/0256864 A1) [hereinafter referred to as Catledge] and Cella et al. (WIPO Pat. Pub. No. 2024/226801 A2)[hereinafter referred to as Cella] have been necessitated which upon closer examination fully replaces the Donahue article “Mars Ascent-Stage Design Utilizing Nuclear Propulsion”. However, the Applicant has not currently provided particular details of which sections of the claim that they believe the Haney and Freiheit references do not disclose or teach, it becomes exceptionally difficult to respond. Rather than provide mere conclusory statements alleging the claims are allowable, the Examiner strongly encourages the Applicant to provide the details of their arguments particularly pointing out and explaining which limitations of the instant claims the Applicant believes the references to be deficient in. Subsequently, a new rejection based on 35 USC § 103 has been made and is discussed in detail below.
Regarding claim 8, the Applicant amended the limitations to recite at least two artificial intelligence computer programs. However, Haney discloses in [0019] where the data is processed by a deep convolutional neural network for computer vision; and a reinforcement learning agent which takes actions to command thrust vector controls for the rocket which is construed as at least two artificial intelligence programs. Further, Cella teaches in [0299] 2 artificial intelligence systems that can be operated independently or collaboratively. The Applicant further amended the limitations to recite radiation hardened processor further comprising at least one graphics processing unit and at least one computational processing unit. However, Freiheit teaches in [0049] logic component may contain one or more central processing units including central processors and graphical processing units. Lastly, the Applicant further amended the limitations to recite the sensor data being processed through a sensor fusion system, the sensor fusion system cleaning and aggregating the sensor data. However, Cella teaches in [0499] that the overarching system employs deep learning systems in collaboration with data and sensory fusion systems to which a person of ordinary skill in computer and data science may deploy. Therefore, this argument is moot.
Claim 16 recites a computing device having substantially the same features of claim 8 above, therefore claim 16 is rejected for the same reasons as claim 8 as discussed above.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because: line 1 includes phraseology that may be implied, (e.g., “The disclosure provides”). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 8 and 16 are objected to because of the following informalities:
Claim 8 (line 16) and claim 16 (lines 8 and12) contain a typographical error where real time sensor data should be corrected to real-time sensor data; and
Claim 8 (line 6) and claim 16 (line 4) recite the limitation at least one computational processing unit which lacks antecedent basis in the Applicant’s disclosure, and this phrase should be corrected to at least one central processing unit so to avoid the introduction of new matter and improve clarity of the record.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 8, the limitation recites real time sensor data in line 16, and it is unclear if this sensor data is the same as the sensor data as recited in line 12 or a different sensor data altogether.
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.
The factual inquiries 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 non-obviousness.
Claims 8 and16 are rejected under 35 U.S.C. 103 as being unpatentable over Haney (US Pat. Pub. No. 2022/0234765 A1) in view of Freiheit (US Pat. Pub. No. 2022/0406196 A1), Catledge et al. (US Pat. Pub. No. 2025/0256864 A1), and Cella et al. (WIPO Pat. Pub. No. 2024/226801 A2)
Regarding claims 1-7, 9-15, and 17-20, Applicant has elected to cancel these claims and are therefore no longer under consideration.
Regarding claim 8, Haney discloses:
A computing device for rocket reaction control comprising at least two artificial intelligence computer programs ([0019] where the data is processed by a deep convolutional neural network for computer vision; and a reinforcement learning agent which takes actions to command thrust vector controls for the rocket which is construed as at least two artificial intelligence programs and [0021] sentence (s.) 4 rocket's control systems, which typically include the attitude control system, reaction control system and other control systems are unified into a single control system, directly controlling trajectory by manipulating thrust), the computing device comprising:
the first artificial intelligence computing program being simulation trained embedded on a radiation hardened processor ([0024] includes an agent which has been trained in a simulation environment interacting with the rocket's data collected by sensors which represent the rocket's physical landing zone and [0019]radiation hardened processor),
the radiation hardened processor further comprising at least one graphics processing unit (claim 2 of the disclosure where the field programmable gate array comprises a graphics processing unit)
connected to the rocket's thrust vectors further comprising a fuel injector, the fuel injector injecting fuel to one or more engines according to the commands produced by the first artificial intelligence computer program ([0026] s.3, control system autonomously commands the rocket by processing real time data about the landing zone and adapting the rocket's mechanics, positioning, and trajectory accordingly by manipulating the rocket's thrust vector output which in claim 4 of the reference is described as controls a valve, releasing the rocket's thrust chamber, ejecting explosive propellant from the rocket's nozzle which is construed as a fuel injector injecting fuel into the engine),
the first artificial intelligence program further comprising at least one neural network, the neural network being trained to process sensor data, including GPS data, LiDAR data, or camera data ([0026] s.4, uses multiple LiDAR sensors, GPS sensors, and inertial navigation sensors on the rocket),
the first artificial intelligence computer program, stored by the radiation hardened processor, generalizing about the rocket's trajectory environment by processing real time sensor data ([0020] s.1, sensors mounted on the rocket in various positions collecting data about the rocket's environment and s. 5 rocket's control is then guided along an optimal landing trajectory which is construed as generalizing a rocket’s trajectory environment by processing data and [0021] s.1, where LiDAR sensors gather real-time data),
the second artificial intelligence computer program further comprising at least one deep reinforcement learning program processing the sensor data ([0008] Deep learning is a type of machine learning concerned with the acquisition of knowledge from large amounts of data), cleaned and aggregated ([0009] s.5, deep learning systems are developed in part by data pre-processing which necessarily includes data cleaning and aggregation), to optimize commands for reaction control, the commands for reaction control controlling thrust vectors for the rocket ([0001] command the rocket booster's control system for optimal performance and [0026] s.3, control system autonomously commands the rocket by processing real-time data about the landing zone and adapting the rocket's mechanics, positioning, and trajectory accordingly by manipulating the rocket's thrust vector output),
the second artificial intelligence computer program optimizing metrics corresponding to distance, time, and impact, informing the command sequences for controlling the thrust vectors for the rocket ([0026] s.6, an embedded reinforcement learning agent maximizes a reward function defining optimal landing metrics including distance, time, and impact trajectory and force).
Although Haney discloses use of a GPU as discussed above and the invention is directed toward the landing of a rocket, it does not explicitly disclose:
at least one graphics processing unit and at least one computational processing unit;
wherein wiring connects the radiation hardened processor; and
maintaining an optimal trajectory course throughout the rocket's flight by the deep reinforcement learning algorithm, from point-to-point, wherein the first point is the launch pad, and the final point is the landing pad.
However, Freiheit teaches in [0049] logic component may contain one or more central processing units including central processors and graphical processing units. Furthermore, Freiheit teaches in [0057] in the last sentence that the network may include any network topology and can may employ a wired and/or a wireless mode of communication. Lastly, it is taught in [0040] that flight controller incorporates all aspects of a trajectory for optimization including launch, landing and in-flight modifications. Further, in [0089] where the machine-learning process include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function and the scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in the data. This is construed as an error function that is minimized as an aspect of the invention.
Therefore, it would have been obvious to one of ordinary skill in the art of rocketry controls, computer science, and data science before the effective filing date of the current invention to modify the rocketry control system of Haney, by incorporating the wired connection, flight optimization, and error minimization teachings of Freiheit, such that the combination would provide for the predictable result of maximizing optimization and overall efficiency within the system.
However, although Haney, as modified by Freiheit, discloses the use of two AI systems as discussed above and in [0009] s.5, deep learning systems are developed in part by data pre-processing which necessarily includes data cleaning and aggregation, it does not explicitly disclose those AI systems working collaboratively nor that:
the sensor data being processed through a sensor fusion system, the sensor fusion system cleaning and aggregating the sensor data.
However, Cella teaches data aggregation requirement in [0247] and data cleansing requirement in [0265]. Furthermore, Cella teaches in [0499] that the overarching systems employs deep learning systems in collaboration with data and sensory fusion systems. Finally, in [0299] it is taught that two types of artificial intelligence systems are utilized within one overarching system and that these AI systems can work independently or collaboratively.
Therefore, it would have been obvious to one of ordinary skill in the art of rocketry controls, computer science, and data science before the effective filing date of the current invention to modify the rocketry control system of Haney, as already modified by the wired connection, flight optimization, and error minimization teachings of Freiheit, by incorporating the data processing and hybrid AI teachings of Cella, such that the combination would provide for the predictable result of maximizing optimization and overall efficiency within the system.
However, Haney, as modified by Freiheit and Cella, does not explicitly disclose:
maintaining an optimal trajectory course throughout the rocket's flight by the deep reinforcement learning algorithm rewarding stability and fuel efficiency.
However, Catledge [0294] s.4 AI models may be trained to simulate various trajectory options and evaluate their risk levels against predefined safety criteria for weather conditions, airspace traffic, maritime traffic, orbital traffic, etc. which is construed as necessarily including stability. Furthermore, in s.5, AI models may further refine the recommendations by prioritizing adjustments that minimize fuel consumption or ensure compliance with regulatory constraints interpreted as fuel efficiency.
Therefore, it would have been obvious to one of ordinary skill in the art of rocketry controls, computer science, and data science before the effective filing date of the current invention to modify the rocketry control system of Haney, as already modified by the wired connection, flight optimization, and error minimization teachings of Freiheit and the data processing and hybrid AI teachings of Cella, by incorporating the stability and fuel efficiency teachings of Catledge, such that the combination would provide for the predictable result of improving safety and fuel efficiency, as acknowledged in [0294] of Catledge.
Regarding claim 16, Haney discloses:
A computing device for rocket reaction control comprising at least two artificial intelligence computer programs (see claim 8), the computing device comprising:
a radiation hardened processor comprising at least one graphics processing unit and at least one computational processing unit (see claim 8),
the radiation hardened processor further storing the first artificial intelligence computer program (see claim 8),
the first artificial intelligence computer program being simulation trained and containing at least one neural network for processing real time sensor data, including camera data, LiDAR data, and GPS data (see claim 8),
the first artificial intelligence computer program further generalizing about the rocket's trajectory environment and producing labelled visual data by processing the real time sensor data through a sensor fusion mechanism for the purpose of automating computer vision (see claim 8),
the second artificial intelligence computer program comprising at least one reinforcement learning program, processing the labelled visual data and producing optimal commands for reaction control (see claim 8 above and claim 2 of Haney where computing visual data from the network, and processing the visual data with a deep reinforcement learning algorithm), controlling thrust vectors for the rocket to minimize error (see claim 8, specifically Freiheit [0089] as discussed above),
the second artificial intelligence program further controlling thrust vectors to manipulate thrust control and steer the rocket's pitch, attitude, roll, and yaw ([0022] s.3, data is further processed with a deep reinforcement learning algorithm controlling the rocket through command sequences corresponding to thruster command controls to manipulate rocket positioning including roll, pitch, yaw, and attitude), and
the second artificial intelligence program further optimizing control metrics corresponding to distance, time, and impact (see claim 8).
Prior Art
The prior art made of record previously utilized but not currently relied upon is considered pertinent to applicant's disclosure. Please see:
Donahue article “Mars Ascent-Stage Design Utilizing Nuclear Propulsion” which is directed toward knowledge regarding chemical propulsion systems
Rijlaarsdam et al. (US Pat. Pub. No. 2025/0358000 A1) which is directed toward a system and a method for improving the efficiency of spacecraft by performing autonomous scheduling using nowcasts.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEITH ALLEN VON VOLKENBURG whose telephone number is (703)756-5886. The examiner can normally be reached Monday-Friday 8:30 am-5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Keith A von Volkenburg/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665