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 .
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. 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 1-4 and 7-9 are rejected under 35 U.S.C. sec. 103 as being
unpatentable as obvious in view of NPL, Dikshroom, Nick,
Simultaneous localization and mapping with the AR. Drone, University
of Amsterdam, Master Thesis, and in view of Merz et al., Current Collision Avoidance Service by ESA Space Debris Office's algorithm
of 2017, Proc. 7th European Conference on Space Debris, Darmstadt,
Germany, 18-21 April 2017, published by the ESA Space Debris Office
Ed. T. Flohrer & F. Schmitz, (http://spacedebris2017.sdo.esoc.esa.int,
June 2017) (hereinafter "Mertz". :
Dijkshoorm discloses"._.1. A method performed by one or more computing systems to guide movement of a platform, the method comprising, for each of a plurality of intervals, (see section 2.4 and page 8 where the robot pose is stored relative to the global coordinate frame and the pose point has a velocity for each pose point; see page 17-20 where the coordinate system is then converted to a projective transformation) (see page 13~ 16)
PNG
media_image1.png
650
590
media_image1.png
Greyscale
PNG
media_image2.png
476
800
media_image2.png
Greyscale
Dijkshoorm is silent but Mertz teaches "....receiving time-of-arrival (“TOA”) information derived from TOAs (this is shown by the radar trackcing the objects and providing this to a machine learning)
determined based on times between signals transmitted by transmitters and return signals received by receivers wherein a return signal is reflected from an observed object at an object location; and (See page 2-11, first column where the message COM time of arrival is recorded as monte carlo training data to predict the time of arrival of the next COM message in section 2,2 via CORAM software and also external tracking data to provide an RF signal and determine where the space debris is and where the trajectory is going based on the other debris and orbital tracking and the chaser orbits that is placed in the CORAM and the flight dynamics and the JSPTOC radar) (This reference is using the time of arrival of the signal to determine the location and the tracking using a machine learning)
determining guidance information by applying a machine learning system that inputs TOA information and outputs guidance information, the machine learning system being trained using training data that includes TOA information and guidance information. (See page 1-11 where the message COM time of arrival is recorded as training data to predict the time of arrival of the next COM message and then in section 2.2 then a probability model is construction for an orbit and a collision probability where using the time of arrival then the one element can be provided as a target element and the chasing element then based on the time of arrival signals and the COM messages then the trajectory of the threat can be predicted using a monte carlo mode! using existing COM and future CDMS and an alert can provided and it can be visualized; see algorithm 5-7); (This reference is using the time of arrival of the signal to determine the location and the tracking using a machine learning)
It would have been obvious to one of ordinary skill in the art before the time
of the effective filing date of the present disclosure to combine the teachings of Mertz with the teachings of Dijkshoorm since Mertz teaches that a ground control station or drone UAV can monitor the "conjunction data messages data" or COM messages over time in FIG, 2 and then using 2. A monte carlo machine !earning neural network in algorithms 5-7 can predict "hypothetical" COM messages in the future. This is creating using a time of arrival of the next COM message. More specifically, they train a special kind of recurrent neural network1 a long short-term memory (LSTM network to predict the features and time of arrival of the next COM given all previous CDMs in an evolving conjunction event Then in figure i-4 using the neural network mode! and library a time to closet approach TCA can be interpolated from the target vehicle to the chasing vehicle. This can be used to forecast a possible avoidance or a collision event and then steps can be taken in advance to avoid a collision using a navigation procedure where an orbit is changed. The drones can be controlled to perform a collision avoidance that is more than 1 day in advance based on neural network future data that has not even occurred yet and is for increased safety, See pages 1 -11.
TOA (Time of Arrival) in radar is the measured time it takes for a radar pulse to travel from the transmitter, reflect off a target, and return to the receiver, fundamentally used to calculate target range (distance) by knowing the signal's speed (speed of light). In radar systems, TOA data from multiple sensors (or a single sensor with known positions) allows for precise target localization helping the MERTZ system to identify what, where, and how many targets are present, even distinguishing between different radar signals.
Dijkshoorm is silent but Mertz teaches"2. The method of claim 1 wherein the TOA information is the TOAs of a look and the guidance information is a collection of object locations for each look. (See page i -1 i where the message COM fone of arrival is recorded as
training data to predict the time of arrival of the next COM message and
then in section 2.2 then a probability mode! is construction for an orbit and
a collision probability where using the time of arrival then the one element
can be provided as a target element and the chasing element then based
on the time of arrival signals and the COM messages then the trajectory of
the threat can be predicted using a MONTE CARLO or algorithm 5-7 LSTM
mode! using existing COM and future CDMS and an alert can be provided
and it can be visualized) ;
!t would have been obvious to one of ordinary ski!! in the art before the time
of the effective filing date of the present disclosure to combine the teachings of Mertz with the teachings of Dijkshoorm since Mertz teaches that a ground control station or drone UAV can i. Remotely monitor the "conjunction data messages data" or COM messages over time in FIG. 2 and then using 2. A rnonte car!o machine !earning neural network in algorithms 5-7 can predict "hypothetical" COM messages in the future. This is creating using a time of arrival of the next COM message. More specifically, they train a special kind of recurrent neural network1 a long short-term memory (LSTM network to predict the features and time of arrival of the next COM given all previous CDMs in an evolving conjunction event Then in figure 1-4 using the neural network mode! and library a time to closet approach TCAcan be interpolated from the target vehicle to the chasing vehicle. This can be used to forecast a possible avoidance or a collision event and then steps can be taken in advance to avoid a collision using a navigation procedure where an orbit is changed. The drones can be controlled to perform an avoidance that is more than 1 day in advance based on neural network future data that has not even occurred yet and is for increased safety. See pages 1-i 1.
Dijkshoorm is silent but Mertz teaches" 3. The method of claim 1 wherein the TOA information is a collection of object locations corresponding to TOAs of a look and the guidance information is a guidance instruction”. (see page 1 ~ 11 where for each object a prediction for a collision event in terms of days to a TCA event is provided as red as a time of predicted closest approach in page 1- 11 ; !t would have been obvious to one of ordinary ski!! in the art before the time
of the effective filing date of the present disclosure to combine the teachings of Mertz with the teachings of Dijkshoorm since Mertz teaches that a ground control station or drone UAV can i. Remotely monitor the "conjunction data messages data" or COM messages over time in FIG. 2 and then using 2. A rnonte car!o machine !earning neural network in algorithms 5-7 can predict "hypothetical" COM messages in the future. This is creating using a time of arrival of the next COM message. More specifically, they train a special kind of recurrent neural network1 a long short-term memory (LSTM network to predict the features and time of arrival of the next COM given all previous CDMs in an evolving conjunction event Then in figure 1-4 using the neural network mode! and library a time to closet approach TCA can be interpolated from the target vehicle to the chasing vehicle. This can be used to forecast a possible avoidance or a collision event and then steps can be taken in advance to avoid a collision using a navigation procedure where an orbit is changed. The drones can be controlled to perform an avoidance that is more than 1 day in advance based on neural network future data that has not even occurred yet and is for increased safety. See pages 1-i 1.
Mertz teaches “...4. The method of claim 1 wherein the TOA information is TOAs of a look and the guidance information is a guidance instruction”. (see page i -1 i where
for each object a prediction for a collision event in terms of days to a TCA
event is provided as red as a time of predicted closest approach in page 2-
1 i; see page i -1 i where the warning is triggered and the trajectory can be
altered to avoid a collision in section i .3); !t would have been obvious to one of ordinary ski!! in the art before the time
of the effective filing date of the present disclosure to combine the teachings of Mertz with the teachings of Dijkshoorm since Mertz teaches that a ground control station or drone UAV can i. Remotely monitor the "conjunction data messages data" or COM messages over time in FIG. 2 and then using 2. A rnonte car!o machine !earning neural network in algorithms 5-7 can predict "hypothetical" COM messages in the future. This is creating using a time of arrival of the next COM message. More specifically, they train a special kind of recurrent neural network1 a long short-term memory (LSTM network to predict the features and time of arrival of the next COM given all previous CDMs in an evolving conjunction event Then in figure 1-4 using the neural network mode! and library a time to closet approach TCAcan be interpolated from the target vehicle to the chasing vehicle. This can be used to forecast a possible avoidance or a collision event and then steps can be taken in advance to avoid a collision using a navigation procedure where an orbit is changed. The drones can be controlled to perform an avoidance that is more than 1 day in advance based on neural network future data that has not even occurred yet and is for increased safety. See pages 1-i 1.
Claim 5 is rejected under 35 U.S.C. sec. 103 as being
unpatentable as obvious in view of NPL, Dikshroom, Nick,
Simultaneous localization and mapping with the AR.Drone, University
of Amsterdam, Master Thesis, in view of Mertz et al, Current
Collision Avoidance Service by ESA Space Debris Office's algorithm
of 2017, Proc. 7th European Conference on Space Debris, Darmstadt,
Germany, 18-21 April 2017, published by the ESA Space Debris Office
Ed. T. Flohrer & F. Schmitz, (http://spacedebris2017.sdo.esoc.esa.int,
June 2017) (hereinafter "Mertz") and in further in view of U.S. Patent
Application Pub. No.: US 2010/0273504 A1 to Bull et al. that was filed
in 2009.
Bull teaches “...5. The method of claim 1 wherein the machine learning system includes a first machine learning system that inputs TOAs of a look and outputs a collection of object locations and a second machine learning system that inputs a collection of object locations and output a guidance instruction”. (see FIG. 4a where each of the subscribers can include a location update as to where the transmitter and receiver is located and that is matched to a location application that stores the location with the position identifier in blocks 401 -407; see paragraph 7-11, 14 where the location of a transmitter and an angle is provided from the signal of interest; see paragraph 54 where the receiver can also be located);
The second machine leaning is a duplication of parts.
It would have been obvious to one of ordinary skill in the art before the time
of the effective filing date of the present disclosure to combine the
teachings of BULL with the teachings of Dijkshoorrn since BULL teaches
that a mobile device can provide a location of the mobile device passively
in an uplink with the device provides a wireless identification and location
and velocity of the mobile device using a RF signal without disrupting the
wireless signal. This can provide an improved longitude, latitude and
altitude of the mobile device transmitter and receiver passively without sacrificing network performance. See claims 1 -2.
Claim 6 is rejected under 35 U.S.C. sec. 103 as being
unpatentable as obvious in view of NPL, Dikshroom, Nick,
Simultaneous localization and mapping with the AR.Drone, University
of Amsterdam, Master Thesis, and in view of Merz, Current
Collision Avoidance Service by ESA Space Debris Office's algorithm
of 2017, Proc. 7th European Conference on Space Debris, Darmstadt,
Germany, 18-21 April 2017, published by the ESA Space Debris Office
Ed. T. Flohrer & F. Schmitz, (http://spacedebris2017.sdo.esoc.esa.int,
June 2017) (hereinafter "Mertz") and in view of Uriot, Thomas, et al.,
Spacecraft collision avoidance challenge: Design and results of a
machine learning competition, Astrodynamics, (2020).
The primary reference is silent but Uriot teaches “...6. The method of claim 1 wherein the platform is a component of a robot control system. {see page 3~4 where the operation center provides a
machine !earning and monitor of a target with a chaser satellite and a
position and a velocity from the COM data and if an alarm indicates a
collision then the flight dynamics will provide a collision avoidance move to
stop the risk)
!t would have been obvious to one of ordinary skill in the art before the time
of the effective filing date of the present disclosure to combine the
teachings of URIOT with the teachings of Dijkshoorm since URIOT teaches
that a COM messages can be monitored to determine a collision probability
and a satellite device can include a collision avoidance procedure to avoid
a possible collision. See pages 1-4. This can provide a navigation through
a dense cloud of space objects to preserve the expensive device. See
pages 1-4.
Mertz teaches “...7. The method of claim 1 wherein the platform is a satellite and the object locations are locations of objects in space. {see page 1-i 1 where for each object a prediction for a collision event in terms of days to a TCA event is provided as red as a time of predicted closest approach in page 1 i; see page 1 i where the warning is triggered and the trajectory can be altered to avoid a collision in
section 5 with a spacecraft);
!t would have been obvious to one of ordinary ski!! in the art before the time of the effective filing date of the present disclosure to combine the teachings of Mertz with the teachings of Dijkshoorm since Mertz teaches that a ground control station or drone UAV can i. Remotely monitor the "conjunction data messages data" or COM messages over time in FIG. 2 and then using 2. A rnonte car!o machine !earning neural network in algorithms 5-7 can predict "hypothetical" COM messages in the future. This is creating using a time of arrival of the next COM message. More specifically, they train a special kind of recurrent neural network1 a long short-term memory (LSTM network to predict the features and time of arrival of the next COM given all previous CDMs in an evolving conjunction event Then in figure 1-4 using the neural network mode! and library a time to closet approach TCA can be interpolated from the target vehicle to the chasing vehicle. This can be used to forecast a possible avoidance or a collision event and then steps can be taken in advance to avoid a collision using a navigation procedure where an orbit is changed. The drones can be controlled to perform an avoidance that is more than 1 day in advance based on neural network future data that has not even occurred yet and is for increased safety. See pages 1-i 1.
Uriot teaches “...8. The method of claim 1 wherein the platform is an unmanned vehicle”. (see page 3-4 where the operation center provides a machine !earning and monitor of a target with a chaser satellite and a position and a velocity from the COM data and if an alarm indicates a collision then the flight dynamics will provide a collision avoidance move to stop the risk)
It would have been obvious to one of ordinary skin in the art before the time
of the effective filing date of the present disclosure to combine the
teachings of URIOT with the teachings of Dijkshoorm since URIOT teaches
that a COM message can be monitored to determine a collision probability
and a device can have a collision avoidance procedure to avoid
a possible collision. See pages1-4. This can provide a navigation through
a dense cloud of space objects to preserve the expensive device. See
pages 1-4.
Mertz teaches “...9. The method of claim 1 further comprising guiding the platform based on the guidance information. {see page 1 to 11 where for each object a prediction for a collision event in terms of days to a TCA event is provided as red as a time of predicted closest approach in page 1-11 and a warning is triggered and the trajectory can be altered to avoid a collision in section with a spacecraft);
See motivation statement above.
Double Patenting
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 §§ 706.02(l)(1) - 706.02(l)(3) 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-I.jsp.
Claims 1-9 are rejected under obviousness double patenting in view of claim 1 of U.S. Patent No.: 11,685,050 that recites “ [a] method performed by one or more computing systems for generating a machine learning system to generate guidance information based on locations of objects, the method comprising:
accessing training data that includes a plurality of training times-of-arrival (TOAs) for each of a plurality of looks and guidance information for the plurality of looks....and training a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information. ”.
The only different feature is the looks represent object locations times and are not located on the object locations via a transmitter array with the parent case. It would have been obvious to train using reflections and also return signals. The office takes official notice that using toa for tracking and machine learning is well known in the art. See U.S. Patent No.: US11511420B2 to Toda that provides “..a machine learning device, which learns an operation program of a robot, includes a state observation unit which observes as a state variable at least one of a shaking of an arm of the robot and a length of an operation trajectory of the arm of the robot; a determination data obtaining unit which obtains as determination data a cycle time in which the robot performs processing; and a learning unit which learns the operation program of the robot based on an output of the state observation unit and an output of the determination data obtaining unit” and a trajectory of the arm can be provided. See claims 1-8.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A. Browne can be reached at 571-270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEAN PAUL CASS/Primary Examiner, Art Unit 3666