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 .
Priority
Applicant claims the benefit of US Provisional Application No. 64/547,460, filed 6 November 2024. Claims 1-20 have been afforded the benefit of this filing date.
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 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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-11 and 13-17 (all claims except 5-6, 12 and 18-20) are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by US Patent Publication 2019 0005162 A1, (Kwon et al.). References are listed in the Notice of Cited References when they were first cited. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
[AltContent: textbox (Kwon et al. Fig. 1, showing using images converted to point clouds to control a space vehicle.)]
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Claim 1
Regarding Claim 1, Kwon et al. disclose a system of space object characterization ("A typical space vehicle uses onboard targeting and imaging systems to determine a position of the space vehicle relative to a target," paragraph [0013]) comprising:
a. one or more imaging devices operable to produce a set of data indicating properties of a target space object comprising ("For example, the image sensor may measure one or more parameters of an object, a target, etc.," paragraph [0016]): shape ("the pose determiner 240 may determine a position and/or an orientation of the space vehicle 102, the target 104, the illumination source 116, etc., of FIG. 1," paragraph [0047] where orientation of a space vehicle is the shape), location ("the pose determiner 240 may determine a position and/or an orientation of an object," paragraph [0047]), and orientation ("the pose determiner 240 may determine a position and/or an orientation of an object," paragraph [0047]);
b. a processor ("program for execution by a processor such as the processor 912 shown in the example processor platform 900 discussed below in connection with FIG. 9," paragraph [0063]) configured to:
i. receive image data from the one or more imaging devices ("the image sensor 126 represents an electro-optical image sensor. Additionally or alternatively, any other type of image sensor may be represented such as a light sensor array. Although only one image sensor 126 is depicted, more than one image sensor 126 may be used. In the illustrated example, the optic assembly 128 represents a six-degree of freedom movable mechanical bracket assembly that can be moved translationally with respect to the X-axis 120, the Y-axis 122, and the Z-axis 124," paragraph [0031]);
ii. assemble one or more partial three-dimensional maps of the target space object ("the point cloud generator 220 may generate a synthetic point cloud of the target 104 based on a set of data points obtained by the image processor 130," paragraph [0041] where a point cloud is a three dimensional map);
iii. combine said one or more partial three-dimensional maps to produce a more complete three-dimensional map ("Generating the spatial transformation includes merging multiple data sets into a globally consistent model (e.g., a model within a defined computer generated geometric coordinate system, etc.), and mapping a new measurement to the registration model to identify features or to estimate a pose," paragraph [0022]); and
iv. formulate a system model based on the combined three-dimensional map for characterization of the target space object ("the PCM 100 simulates an image processor 130 by converting the light reflection obtained by the image sensor 126 into a digital representation. In some examples, the PCM 100 calculates a pose of the target 104 in the computer-generated environment 106 based on one or more digital representations," paragraph [0030]).
Claim 2
Regarding Claim 2, Kwon et al. disclose the system according to claim 1, further comprising a tool to interact with the target space object ("Typical space vehicles such as aircraft and satellites use actuators such as thrusters and/or momentum devices to adjust position. In some examples, a space vehicle uses actuators to move relative to a target such as another space vehicle. In such examples, a space vehicle can conduct an autonomous space rendezvous, a proximity maneuver, a docking maneuver, etc., relative to another space vehicle such as a satellite, a space station, etc.," paragraph [0012]).
Claim 3
Regarding Claim 3, Kwon et al. disclose the system according to claim 1, wherein the set of data produced by the one or more imaging devices is a two-dimensional data set ("The imaging system may generate a point cloud image of the target based on processing multiple reflections and mapping the multiple reflections to a coordinate system in a computer-generated environment," paragraph [0013] where reflections are two-dimensional).
Claim 4
Regarding Claim 4, Kwon et al. disclose the system according to claim 1, wherein the set of data produced by the one or more imaging devices is a three-dimensional data set ("the illumination source may be a laser ( e.g., an infrared laser, an ultraviolet laser, an X-ray laser, a gamma-ray laser, etc.). In such an example, the laser may be used to perform light detection and ranging (LIDAR). LIDAR is a remote sensing method that uses light in the form of a pulsed laser to measure ranges ( e.g., variable distances, etc.) to an object (e.g., a space vehicle, etc.)," paragraph [0014]).
Claim 7
Regarding Claim 7, Kwon et al. disclose the system according to claim 1, further comprising a control system capable of changing an attitude and position of the one or more imaging devices using information from a target spacecraft system model to maintain a relative pose between the one or more imaging devices and the target space object ("Typical space vehicles such as aircraft and satellites use actuators such as thrusters and/or momentum devices to adjust position. In some examples, a space vehicle uses actuators to move relative to a target such as another space vehicle. In such examples, a space vehicle can conduct an autonomous space rendezvous, a proximity maneuver, a docking maneuver, etc., relative to another space vehicle such as a satellite, a space station, etc.," paragraph [0012]).
Claim 8
Regarding Claim 8, Kwon et al. disclose the system according to claim 7, wherein said control system facilitates direct interaction between said system according to claim 7 and the target by means of a tool configured to interact with the target object ("Typical space vehicles such as aircraft and satellites use actuators such as thrusters and/or momentum devices to adjust position. In some examples, a space vehicle uses actuators to move relative to a target such as another space vehicle. In such examples, a space vehicle can conduct an autonomous space rendezvous, a proximity maneuver, a docking maneuver, etc., relative to another space vehicle such as a satellite, a space station, etc.," paragraph [0012] where the tool is the actuator).
Claim 9
Regarding Claim 9, Kwon et al. disclose the system according to claim 1, further comprising one or more sensors capable of assessing a spectral signature of materials on a surface of the target object ("the image sensor simulator 212 may simulate a conversion gain, a sensitivity, noise (e.g., fixed pattern, temporal, etc.), spectral response, quantum efficiency, exposure time, etc., of the image sensor 126," paragraph [0038]).
Claim 10
Regarding Claim 10, Kwon et al. disclose a method for space object identification ("A typical space vehicle uses onboard targeting and imaging systems to determine a position of the space vehicle relative to a target," paragraph [0013]) , the method comprising:
a. creating one or more image datasets of a target space object ("the image sensor 126 represents an electro-optical image sensor. Additionally or alternatively, any other type of image sensor may be represented such as a light sensor array. Although only one image sensor 126 is depicted, more than one image sensor 126 may be used. In the illustrated example, the optic assembly 128 represents a six-degree of freedom movable mechanical bracket assembly that can be moved translationally with respect to the X-axis 120, the Y-axis 122, and the Z-axis 124," paragraph [0031]);
b. determining a change in relative position and attitude of the target space object based on the one or more image datasets ("The example PCM 100 may re-scan the target 104 based on a change in pose of the target 104, the illumination source 116, or any other object in the computer generated environment 106," paragraph [00]);
c. compiling one or more partial three-dimensional maps of the target space object ("the point cloud generator 220 may generate a synthetic point cloud of the target 104 based on a set of data points obtained by the image processor 130," paragraph [0041] where a point cloud is a three dimensional map);
d. aligning said three-dimensional maps into a more complete three-dimensional map ("Generating the spatial transformation includes merging multiple data sets into a globally consistent model (e.g., a model within a defined computer generated geometric coordinate system, etc.), and mapping a new measurement to the registration model to identify features or to estimate a pose," paragraph [0022]); and
e. formulating one or more system models of the target space object for identification of the target space object ("the PCM 100 simulates an image processor 130 by converting the light reflection obtained by the image sensor 126 into a digital representation. In some examples, the PCM 100 calculates a pose of the target 104 in the computer-generated environment 106 based on one or more digital representations," paragraph [0030]).
Claim 11
Regarding Claim 11, Kwon et al. disclose the method according to claim 10, further comprising rendering an accurate three-dimensional model of the target space object ("the database 270 records data (e.g., a 3-D CAD model of an object, a reference synthetic point cloud, a synthetic point cloud, a configuration of the illumination source 116, a light reflection parameter of the first surface 132 of the target 104, etc.).," paragraph [0053] "the PCM 100 may generate the first through the fifth synthetic point cloud images 300, 302, 304, 306, 308 based on a configuration of the target 104, a parameter or a set of parameters corresponding to a component of the target 104 (e.g., a parameter corresponding to a light reflection characteristic of the first surface 132, the second surface 134, etc.)," paragraph [0055]).
Claim 13
Regarding Claim 13, Kwon et al. disclose the method according to claim 10, wherein aligning said partial three-dimensional maps includes using a filter method ("In some disclosed examples, the point set registration is conducted using an iterative closest point (ICP) method. For example, the PCM apparatus may use the ICP method to minimize a difference between two point clouds. In such an example, the PCM apparatus may use the ICP method to keep one point cloud fixed ( e.g., the reference synthetic point cloud of the target, etc.), while transforming a second point cloud (e.g., the synthetic point cloud of the target, etc.) to best match the first point cloud," paragraph [0022]).
Claim 14
Regarding Claim 14, Kwon et al. disclose the method according to claim 10, wherein features on the partial three-dimensional maps of the target space object are tracked between time steps to aid in alignment of the partial three-dimensional maps ("the example method 800 may be used to optimize an object in real-time operation. The example method 800 begins at block 802 when the example PCM 100 configures a targeting system," paragraph [0089] where optimizing in real time requires tracking between time steps).
Claim 15
Regarding Claim 15, Kwon et al. disclose the method according to claim 10, wherein the partial three-dimensional maps are aligned through the use of an Iterative Closest Point (ICP) algorithm ("In some disclosed examples, the point set registration is conducted using an iterative closest point (ICP) method. For example, the PCM apparatus may use the ICP method to minimize a difference between two point clouds. In such an example, the PCM apparatus may use the ICP method to keep one point cloud fixed ( e.g., the reference synthetic point cloud of the target, etc.), while transforming a second point cloud (e.g., the synthetic point cloud of the target, etc.) to best match the first point cloud," paragraph [0022]).
Claim 16
Regarding Claim 16, Kwon et al. disclose the method according to claim 10, further comprising identifying segments of the target space object having distinct functions or appearances in the partial three-dimensional maps by utilizing probabilistic identification ("For example, the PCM apparatus used by an aircraft approaching a docking station of a space station may generate a point cloud of the docking station. In such an example, the PCM apparatus may process the point cloud and determine an ensuing movement based on the processed point cloud. For example, the PCM apparatus may determine that the aircraft needs to adjust in position (e.g., move closer to the docking station, move away from the docking station, etc.) based on the processed point cloud," paragraph [0018]).
Claim 17
Regarding Claim 17, Kwon et al. disclose the method according to claim 16, wherein the identified segments are components of a satellite or space vehicle ("For example, the PCM apparatus used by an aircraft approaching a docking station of a space station may generate a point cloud of the docking station. In such an example, the PCM apparatus may process the point cloud and determine an ensuing movement based on the processed point cloud. For example, the PCM apparatus may determine that the aircraft needs to adjust in position (e.g., move closer to the docking station, move away from the docking station, etc.) based on the processed point cloud," paragraph [0018]).
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 5-6, 12 and 18-20 (all remaining claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0005162 A1, (Kwon et al.) in view of US Patent Publication 20216 0253233 A1, (Rubenstein et al.). The references are listed in a PTO-892 from the Office Action in which they are first used. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
Claim 5
Regarding Claim 5, Kwon et al. teach the system according to claim 1, as noted above.
Kwon et al. is not relied upon to explicitly teach all of assess inertial or kinematic properties.
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[AltContent: textbox (Rubenstein et al. Fig1, showing using machine learning to identify space objects.)] However, Rubenstein et al. teach wherein the processor is further configured to assess inertial or kinematic properties of the target space object ("Past motion of each modeled object is then used to predict the future position of the object by use of a Kalman filter or the like that extrapolates past motion together with potential uncertainty in the future position and velocity," paragraph [0065]).
Therefore, taking the teachings of Kwon et al. and Rubenstein et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify spacecraft point clouds for automatic docking as taught by Kwon et al. to use “Methods for Automatic Object Detection, Identification and Tracking” as taught by Rubenstein et al., showing that Kwon et al. and Rubenstein et al. are analogous art because both are spacecraft identifying outside objects. The suggestion/motivation for combination is that, “there is a need for improved systems and methods for automatic object detection, identification, and tracking. Embodiments of the present disclosure are directed to this and other considerations.” as noted by the Rubenstein et al. disclosure in paragraph [0005], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that there are many types of space objects and different techniques will be more efficient with different objects; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 6
Regarding claim 6, Kwon et al. teach the system according to claim 1, as noted above.
Kwon et al. is not relied upon to explicitly teach all of assess volumetric properties.
However, Rubenstein et al. teach wherein the processor is further configured to assess volumetric properties of the target space object ("The system continues to observe and collect data, to refine the angular size and speed estimates," paragraph [0130] where size is a volumetric property).
Kwon et al. and Rubenstein et al. are combined as per claim 1.
Claim 12
Regarding claim 12, Kwon et al. teach the method according to claim 10, as noted above.
Kwon et al. is not relied upon to explicitly teach all of estimating a center of mass.
However, Rubenstein et al. teach further comprising estimating a center of mass of the target space object based on the change of relative position and attitude of the target space object ("Past motion of each modeled object is then used to predict the future position of the object by use of a Kalman filter or the like that extrapolates past motion together with potential uncertainty in the future position and velocity," paragraph [0065] and "Where possible for such connected solid body model, the position of the object may be referenced to the presumed center of light, center of mass, or other such inferred typical position for the object," paragraph [0043]).
Kwon et al. and Rubenstein et al. are combined as per claim 1.
Claim 18
Regarding claim 18, Kwon et al. teach the method according to claim 16, as noted above.
Kwon et al. is not relied upon to explicitly teach all of machine learning.
However, Rubenstein et al. teach wherein segments are further identified through the use of machine learning ("The image analysis system 320 may measure and track various details of objects detected by the artificial intelligence and machine learning detection system for each camera," paragraph [0042]).
Kwon et al. and Rubenstein et al. are combined as per claim 1.
Claim 19
Regarding claim 19, Kwon et al. teach the method according to claim 10, wherein the one or more image datasets include reflectance data ("In the illustrated example, the imaging system 114 measures one or more parameters of the target 104 (e.g., a light reflection characteristic of a surface of the target 104, a distance between the image sensor 126 and a surface of the target 104, etc.) by obtaining a reflection of light, which is emitted by the illumination source 116," paragraph [0030]); as noted above.
Kwon et al. is not relied upon to explicitly teach all of machine learning.
However, Rubenstein et al. teach wherein the reflectance data is used to estimate observed materials through the use of machine learning ("The image analysis system 320 may measure and track various details of objects detected by the artificial intelligence and machine learning detection system for each camera," paragraph [0042]); and wherein the reflectance data includes calculated volume and density estimates ("Photometry, surface brightness mensuration, and colorimetry may measure total flux of light, centroid, size, radial profile, deviations from axisymmetry, overall morphology, and color distribution," paragraph [0033]).
Kwon et al. and Rubenstein et al. are combined as per claim 1.
Claim 20
Regarding claim 20, Kwon et al. teach the method according to claim 19, as noted above.
Kwon et al. is not relied upon to explicitly teach all of calculated volume and density measurements.
However, Rubenstein et al. teach further comprising estimating a target space object mass based on the calculated volume and density measurements ("As the detections move, the solid body may be rotating about multiple axes with respect to the fields of view, so the motion of the components may represent a combination of the motion of the center of mass of the solid body, and rotation about the center of inertia, which may be different. The image analysis system 320 may predict the motion of the solid body but may also contain additional components, such as predicting the motion of the components as the object rotates," paragraph [0051]).
Kwon et al. and Rubenstein et al. are combined as per claim 1.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2022 0155452 A1 to Rawat et al. discloses detecting and modeling objects in space using LIDAR is disclosed. The method includes transmitting a laser beam to detect at least one object in space. Further, the method includes detecting one or more data related to at least one object.
US Patent Publication 2015 0371431 A1 to Korb et al. discloses geo-rectifies the images to a three-dimensional surface topography, co-registers groups of the images with fractional pixel accuracy, automates change detection, evaluates the significance of change between the images, and massively compresses imagery sets based on the statistical significance of change.
US Patent Publication 2007 0285304 A1 to Cooper discloses a directed rocket motor transfers an impulse vector to a space target object thereby altering the target's orbit. Preceding the gas blast, a lower level of rocket exhaust may be directed to the target object for profile imaging that may include center of gravity determination using a pulse Doppler radar sighted along the exhaust stream.
Conclusion
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/Heath E. Wells/Examiner, Art Unit 2664
Date: 24 September 2026
