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 .
Response to Amendment
The Amendment filed 01/22/2026 has been entered. Claims 1, 4-10, 13-17 and 19-20 are pending in the application, claims 1, 5-6, 10, 13, 15, 17 and 19-20 are amended and claims 2-3, 11-12 and 18 are cancelled.
Response to Arguments
Applicant’s arguments, see pages 7-14, filed 01/22/2026, with respect to the rejection(s) of claim(s) 1, 10 and 17 under 35 U.S.C 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Elluswamy (US20200249685A1).
Applicant’s arguments, see pages 7-14, filed 01/22/2026, with respect to claim 6 have been fully considered and are persuasive. The rejection of claim 6 has been withdrawn.
Claim Rejections - 35 USC § 103
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.
Claim 1, 4-9, 13 and 17, 19-20 is rejected under 35 U.S.C 103 as being unpatentable over Sheen (US20200320731A1) in view of Elluswamy (US20200249685A1).
Regarding claim 1 Sheen discloses: A radar sensor comprising (Para [0029]: “In one implementation described below with respect to FIG. 1, motion of the passenger is optically tracked as he or she passes by one or more stationary RF, microwave, or millimeter-wave arrays. Multiple linear arrays may be used illuminate the passenger from a wide variety of angles to provide full coverage of the body. The radar data are then correlated with spatial information from the position capture system by employing generalized synthetic aperture focusing or back-projection technique”): at least one memory comprising instructions (Figure 1, computer system); and at least one processor coupled to the at least one memory (Figure 1, computer system), wherein the at least one processor is configured to execute the instructions and cause the at least one processor to: receive position data associated with at least one object
(Paragraph [0036]: “Position capture cameras 112 are configured to monitor locations and movement of target 102 for example walking by the antenna system 110. The cameras 112 capture images for a plurality of frames during movement of the target 102. Position capture system 116 is configured to generate position information at a plurality of moments in time and which is indicative of different locations of the target during movement of the target 102 with respect to the antenna array and during scanning of the target 102.”), wherein the position data is based on sensor data obtained by at least one sensor
(Figure 1, Position Capture Camera);
Sheen does not teach “predict, based on processing the position data using a machine learning model, a future position of the at least one object with respect to a future time point or future time interval.
However, Elluswamy in the analogous arts teaches: predict, based on processing the position data using a machine learning model, a future position of the at least one object with respect to a future time point or future time interval (Para 0016: “In some embodiments, a system comprises a processor and memory coupled to the processor. The processor is configured to receive image data based on an image captured by a camera of a vehicle. For example, a camera sensor affixed to a vehicle captures an image of the vehicle's environment. The camera may be a forward facing camera, a pillar camera, or another appropriately positioned camera. Image data captured from the camera is processed using a processor, such as a GPU or AI processor, on the vehicle. In some embodiments, the image data is used as a basis of an input to a trained machine learning model trained to predict a three-dimensional trajectory of a vehicle lane. For example, the image data is used as an input to a neural network trained to predict vehicle lanes. The machine learning model infers a three-dimensional trajectory for a detected lane. Instead of segmenting the image into lanes and non-lane segments of a two-dimensional image, a three-dimensional representation is inferred. In some embodiments, the three-dimensional representation is a spline, a parametric curve, or another representation capable of describing curves in three-dimensions. In some embodiments, the three-dimensional trajectory of the vehicle lane is provided in automatically controlling the vehicle. For example, the three-dimensional trajectory is used to determine lane lines and corresponding drivable space.”)
Sheen further teaches: identify at least one subspace corresponding to the predicted future position, from a field of view of the radar sensor (Para [0036]: “Position information may include rotation information in some embodiments. The determined position information may be further processed with respect to time to provide motion information regarding the movement of the target 102 during the scanning. The determined positional or motional information of target 102 may be used to focus the radar data as described below.”); and collect radar sensing data from the at least one subspace(Para [0159]: “ In another embodiment, one or more components of the position capture system 306 such as one or more cameras 362 may be coupled with the support structure 361 and generated image data therefrom may be communicated using circuitry 324 to position capture system 306 for use in generating position information regarding locations and rotations of the UAV 358 during radar scanning in an area of interest.”), wherein the radar sensing data corresponds to the at least one object (Para [0162]: “Different position or motion capture systems which may be utilized in some embodiments of the disclosure are described above. However, these examples are not limiting and any suitable system for providing position information during movement of the target or antenna array may be utilized to focus the captured radar data in other embodiments.”).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Sheen with Elluswamy to incorporate the feature of: predict, based on processing the position data using a machine learning model, a future position of the at least one object with respect to a future time point or future time interval. Sheen and Elluswamy are all considered analogous arts as they all disclose the use of multiple sensors to detect objects. However, Sheen fails to disclose a feature of collecting radar data at a projected location of a target. This feature is disclosed by Agrawal. It would have been obvious to someone in the art prior to the effective filling date of the claimed invention to modify Sheen with Elluswamy to incorporate the feature of: predict, based on processing the position data using a machine learning model, a future position of the at least one object with respect to a future time point or future time interval as such a feature would increase the efficiency of the system.
Claims 10 and 17 recites limitations that are similar to those of claim 1, therefore claims 10 and 17 are rejected under the same rationale.
Regarding claim 4 the combination of Sheen and Elluswamy discloses all the limitations of claim 1. Sheen further teaches: wherein the position data includes at least one of an elevation parameter, an azimuth parameter, and a range parameter (Para [0123]: “Application of the above-described image reconstruction technique uses position information of the moving target at different moments in time and an optical position capture system described above may be utilized in some embodiments to generate position and/or motion information regarding the moving target. “).
Claims 13 and 19 recites limitations that are similar to those of claim 4, therefore claims 13 and 19 are rejected under the same rationale.
Regarding claim 7 the combination of Sheen and Elluswamy discloses all the limitations of claim 1. Sheen further teaches: wherein the at least one sensor includes at least one of an image sensor, a LIDAR sensor, and an ultrasonic sensor (Para [0030]: “Active microwave- and millimeter-wave imaging may be performed using mathematical techniques to focus the radar or imaging data. Mathematical focusing utilizes precise measurement of the phase of the wave that is scattered from the imaging target and embodiments described herein use a position capture system to determine position information of a moving imaging array or target during scanning using optical cameras or other position determination techniques.”).
Regarding claim 8 the combination of Sheen and Elluswamy discloses all the limitations of claim 1. Sheen further teaches: wherein the at least one processor is further configured to: send the radar sensing data to a perception stack of an autonomous vehicle (Para [0127]: “Some aspects of disclosure are discussed above in example implementations where an antenna array moves along a path to scan an aperture about a stationary target (e.g., the handheld scanning embodiment discussed with respect to FIG. 13 above). In other aspects of the disclosure, an antenna array may be associated with a moveable vehicle, such as an unmanned aerial vehicle (UAV) or ground-traversing vehicle, and uses to emit electromagnetic energy which is reflected by objects which are buried in the ground or placed behind or within walls of a structure. The reflected electromagnetic energy and position information of the vehicle (and antenna array) are used to generate images of the concealed objects which are not otherwise visible.”).
Regarding claim 9 the combination of Sheen and Elluswamy discloses all the limitations of claim 1.Sheen further teaches: wherein the at least one processor is further configured to: receive one or more parameters that define an area of interest within the field of view of the radar sensor (Para [0029]: “In one implementation described below with respect to FIG. 1, motion of the passenger is optically tracked as he or she passes by one or more stationary RF, microwave, or millimeter-wave arrays. Multiple linear arrays may be used illuminate the passenger from a wide variety of angles to provide full coverage of the body. The radar data are then correlated with spatial information from the position capture system by employing generalized synthetic aperture focusing or back-projection techniques. These methods accurately reconstruct the image by integrating the measured response multiplied by the conjugate of the expected response from a point scatterer anywhere within a 3D image volume. This process yields optimally focused images revealing contents concealed by a target.”); and collect additional radar sensing data corresponding to the area of interest (Para [0029] : Multiple linear arrays may be used illuminate the passenger from a wide variety of angles to provide full coverage of the body. The radar data are then correlated with spatial information from the position capture system by employing generalized synthetic aperture focusing or back-projection techniques. These methods accurately reconstruct the image by integrating the measured response multiplied by the conjugate of the expected response from a point scatterer anywhere within a 3D image volume. This process yields optimally focused images revealing contents concealed by a target.”)
Claims 14-16 are rejected under 35 U.S.C 103 as being unpatentable over Sheen (US20200320731A1) in view of Elluswamy (US20200249685A1) and further in view of Jenssens (US20230014601A1).
Regarding claim 14 the combination of Sheen and Elluswamy discloses all the limitations of claim 10. Sheen does not teach “wherein the at least one processor is further configured to: identify, based on the image data, at least one occlusion; determine an area of interest proximate to the at least one occlusion “.
However, Jenssens in the analogous arts teaches: wherein the at least one processor is further configured to: identify, based on the image data (Para [0005]: “The operation of the traffic signal may be adaptive, responsive, pre-timed, fully-actuated, or semi-actuated depending upon the hardware available at the intersection and the amount of automation desired by the operator (e.g., a municipality). For instance, cameras, loop detectors, or radar may be used to detect the presence, location and/or movement of one or more vehicles. For example, video tracking methods may be used to identify and track objects that are visible in a series of captured images.”), at least one occlusion (Para [0026]: “In various embodiments disclosed herein, tracking systems are described that are inherently capable of handling multiple sensor inputs where the abstraction from a specific sensor can be transformed to world coordinates. These tracking systems are capable of predicting and handling occlusions to keep track of the location of objects even if all sensors lost sight of the object”); determine an area of interest proximate to the at least one occlusion (Para [0040]: “FIG. 7 illustrates two examples of object tracking with occlusion. In a first example, illustrated by images (a), (b), (c) and (d), a first object 710 (e.g. vehicle) is standing still in the camera field of view. The camera location is at the bottom of each image and the image indicates the field of view of the camera. The first object 710 is illustrated as a bounding box with a point at the bottom-middle of the bounding box for tracking the location on the image. The area behind the bounding box relative to the camera location is a potential occlusion area 730. If another object enters the scene, such as second object 720, it is possible for it to enter this occlusion area 730 (as shown in image sequence (a) through (d)). Depending on the height of the first object, it can be calculated exactly when this object will be occluded, and even how much it will be occluded.”); send, to the at least one radar sensor, location data corresponding to the area of interest (Para [0036]: “Position information may include rotation information in some embodiments. The determined position information may be further processed with respect to time to provide motion information regarding the movement of the target 102 during the scanning. The determined positional or motional information of target 102 may be used to focus the radar data as described below.”); and receive, from the at least one radar sensor, additional radar sensor data corresponding to the area of interest (Para [0159]: “ In another embodiment, one or more components of the position capture system 306 such as one or more cameras 362 may be coupled with the support structure 361 and generated image data therefrom may be communicated using circuitry 324 to position capture system 306 for use in generating position information regarding locations and rotations of the UAV 358 during radar scanning in an area of interest.”).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Sheen with Jenssens to incorporate the feature of: wherein the at least one processor is further configured to: identify, based on the image data, at least one occlusion; determine an area of interest proximate to the at least one occlusion. Sheen and Jenssens are all considered analogous arts as they all disclose the use of sensor technology to detect objects. However, Sheen fails to disclose a feature of target tracking in the presence of occlusions. This feature is disclosed by Jenssens. It would have been obvious to someone in the art prior to the effective filling date of the claimed invention to modify Sheen with Jenssens to incorporate the feature of: wherein the at least one processor is further configured to: identify, based on the image data, at least one occlusion; determine an area of interest proximate to the at least one occlusion as such a feature would increase the effectiveness and efficiency of the system.
Regarding claim 15 the combination of Sheen and Elluswamy discloses all the limitations of claim 10. Sheen does not teach “wherein to determine the position data associated with the at least one object the at least one processor is further configured to: provide the image data to a machine learning model configured to detect and identify objects in a vicinity of the AV “.
However, Jenssens in the analogous arts teaches: wherein to determine the position data associated with the at least one object the at least one processor is further configured to: provide the image data to a machine learning model configured to detect and identify objects in a vicinity of the AV (Para [0042]: “Referring to FIG. 8, an example process 800 for transforming bounding boxes into three-dimensional images will now be described, in accordance with one or more embodiments. The camera receives an image with an object, which is fed into a trained CNN of the tracking system to determine an associated bounding box (step 1 in the example). As illustrated in step 2 of the example, the tracking system has identified a bounding box and a point of the object on the ground closest to the camera (original center bottom point of the bounding box). This point is tracked in the world coordinate system. By tracking this point, the trajectory and heading of the object is known to the tracking system. In various embodiments, this point will not exactly represent the center point of the object itself, depending on the angle of the trajectory compared the camera position and other factors. The goal in various embodiments is to estimate the exact ground plane of the object and estimate its length.”).
The reason to combine modify Sheen with Jenssens in similar to one given in claim 14 above.
Regarding claim 16 the combination of Sheen, Elluswamy and Jenssens discloses all the limitations of claim 15. Jenssens further teaches: wherein the machine learning model is tuned to detect and identify objects that are associated with a low confidence metric (Para [0031]: “Referring to FIG. 2, embodiments of object localization through deep learning will now be described. Convolutional Neural Networks (CNNs) can be used to acquire the locations of objects in an image. The input of a CNN is the image and all its pixels, such as an RGB image 210 captured from a visible light sensor or a thermal image 260 captured from an infrared sensor. The output of the CNN (e.g., CNN 220 or CNN 270) is a list of bounding boxes 230 and 280 associated with each detected object, including the class type (e.g., car, truck, person, cyclist, . . . ) and a confidence level of how accurate the CNN sees the particular object of that class. The CNN is trained to be able to recognize the different objects to be detected for the particular environment and may be implemented using a variety of architectures that are capable of outputting bounding boxes for the detected objects.”).
The reason to combine modify Sheen with Jenssens in similar to one given in claim 14 above.
Allowable Subject Matter
Claims 5, 6 and 20 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.
Regarding claim 5 the combination of Sheen and Elluswamy discloses all the limitations of claim 1. Sheen further teaches: wherein to collect radar sensing data from the at least one subspace the at least one processor is further configured to: process a portion of a radar data table that includes radar sensing data for the field of view of the radar sensor, wherein the portion of the radar data table corresponds to the at least one subspace corresponding to the predicted future position of the at least one object. (Para [0073]: “The antenna array 140 provides wide-beamwidth, wide-bandwidth (10-20 GHz) radar imaging data from a 2 m vertical aperture at a repetition rate of over 300 Hz in one embodiment. This array 140 is used with a position capture system 116 that provides target position information that can be used to reconstruct or focus the image data. Acquired position data regarding locations of the target 102 moving through the volume 190 is used to focus the radar data into human-perceptible (visible) images which may reveal contents under clothing or otherwise concealed of target 102.”).
In reference to depend/independent claim 5, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 5. Specifically, the prior arts made of record fail to disclose the limitation: “process a portion of a radar data table that includes radar sensing data for the field of view of the radar sensor, wherein the portion of the radar data table corresponds to the at least one subspace corresponding to the predicted future position of the at least one object. “
Claim 20 recites limitations that are similar to those of claim 5, therefore claim 20 is allowable under the same rationale.
Regarding claim 6 the combination of Sheen and Elluswamy discloses all the limitations of claim 1. Sheen does not teach “wherein to collect radar sensing data from the at least one subspace the at least one processor is further configured to: direct one or more radar transmissions in a direction which corresponds to the at least one subspace corresponding to the predicted future position of the at least one object: and collect the radar sensing data from the at least one subspace based on the one or more radar transmissions”.
In reference to depend/independent claim 6, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 6. Specifically, the prior arts made of record fail to disclose the limitation: “wherein to collect radar sensing data from the at least one subspace the at least one processor is further configured to: direct one or more radar transmissions in a direction which corresponds to the at least one subspace corresponding to the predicted future position of the at least one object: and collect the radar sensing data from the at least one subspace based on the one or more radar transmissions “
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 Bongani J. Mashele whose telephone number is (703)756-5861. The examiner can normally be reached M-F (8 AM - 4:30 PM).
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/BONGANI JABULANI MASHELE/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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