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 .
Notice to Applicants
This communication was filed in response to the action filed on 12/04/2023.
Claims 1-21 are currently pending.
Information Disclosure Statement
The information disclosure statemen (IDS) filed on 04/24/2024 has been fully considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 21 is rejected under 35 U.S.C. 101 because the claim is directed to “a computer-readable storage medium” and can be interpreted as a signal per se and not a hardware embodiment, where a machine claim is directed towards a system, apparatus, or arrangement. Paragraph [0122] of the specification provides examples of non-transitory embodiments for a computer-readable storage medium, but fail to define computer-readable storage medium to exclude transitory signal embodiments. It is advised that the applicant amend the phrase of the claim from “a computer-readable storage medium” to read as “a non-transitory computer-readable storage medium” in order to overcome 101 rejection to the claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-5, 7-9, 12-16, 18-21 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 2023/0206613 A1 to MAHBUB et al. (hereinafter “MAHBUB”).
As per claim 1, MAHBUB discloses a system comprising (a computing system adapted to perform automatic object detection within image frames; fig 1; abstract;): one or more memories configured to store frames of data received from a sensor (the computing system comprises a plurality of memory components including instruction memory 132, system memory 130, and memory controller 124 adapted to store instructions, programs, and data/images; fig 1; paragraph [0016]); and processing circuitry configured to: receive a frame of the frames (the system further comprising CPU 116 acting as the computing processor for the system to execute programs/instructions receiving the stored data including image frames; fig 1; paragraph [0016]); determine a first class for a first layer for each pixel of a plurality of pixels in the frame (the user determines threshold values for a plurality of classes including a first in order to sort pixel layers into each class including a first layer of pixels; abstract; fig 5B; paragraphs [0016], [0057-0059], [0062]); determine a second class for a second layer for each pixel of the plurality of pixels in the frame (the computing system further is adapted to determine a second class of the plurality of classes having its own respective threshold value and has Nth layers sorted/pooled into it based on the threshold value; figs 5B-6; paragraphs [0016], [0057-0062], [0064]); identify a first object in the frame based on the first class for each pixel of the plurality of pixels (each frame is divided into layers in order to allow for the system to use a generated flattening layer 210 is configured to receive Nth layer output data 209 from Nth layer 208, and perform operations to generate a feature vector 211 and includes linear layer 212 which is configured to receive the feature vector 211 from the flattening layer 210 and, generate one or more fully-connected layers that provide the object data 214 (which would include all objects including a first within the frame), the class data 216, and the bounding box data 218; figs 3, and 5B-7; paragraphs [0059], [0062-0064]); and identify a second object in the frame based on the first class for each pixel of the plurality of pixels and based on the second class for each pixel of the plurality of pixels (using object data 214 which includes all objects of the image frame the system is adapted to using neural network 200 and first layer class anchor 250 and first layer bounding box "bbox" anchor 252 that each operate on first layer output data 205, additionally neural network 200 includes second layer class anchor 254 and second layer bounding box anchor 256 that each operate on second layer output data 207 for a second object and is repeated for Nth number of objects detected in the frame; figs 3, and 5B-7; paragraphs [0059], [0063-0065], [0070-0071]), wherein a portion of the plurality of pixels correspond to both the first object and the second object (wherein the system is adapted to track an intersection over union value IOU of the bounding boxes of the two objects which identify the respective objects wherein the over lapping area would include pixels that correspond to both a first and second object if the overlap of the object specific bounding boxes existed within the particular captured and scanned image frame; fig 8; paragraphs [0019], [0067], [0093]).
As per claim 2, MAHBUB discloses the system of claim 1, wherein to determine the first class for the first layer for each pixel of the plurality of pixels in the frame and to determine the second class for the second layer for each pixel of the plurality of pixels in the frame, the processing circuitry is configured to input the frame into a dynamic neural network (neural network 200 is trained in real time to classify pixel layers and identify objects of the input image frame based on the trained neural network; paragraphs [0057-0061], [0063-0070]).
As per claim 3, MAHBUB discloses the system of claim 2, wherein the dynamic neural network comprises a neural network trained using multi-layer training data, wherein the multi-layer training data includes training frames, with each training frame including a plurality of annotated pixels, each annotated pixel having a plurality of corresponding layers, and each corresponding layer being assigned to a class from a set of classes (image capture device 100 applies the initially trained neural network 200 to a validation set (of generated images including pixel layers), and may determine whether the initially trained neural network 200 is sufficiently trained based on the object data 214, class data 216, and bounding box data 218 generated during the validation process, image capture device 100 may compute one or more metrics based on the object data 214, class data 216, and bounding box data 218 generated during the validation if the computed metrics indicate that neural network 200 is not sufficiently trained (e.g., the one or more computed metrics do not meet their corresponding thresholds), the one or more processors execute the instructions to continue training neural network 200 ( e.g., with additional generated training images including pixel layers), once neural network 200 is sufficiently trained, image capture device 100 may store configuration parameters, hyperparameters, and/or weights associated with the trained neural network 200 within, for example, instruction memory 132; paragraphs [0063-0070]).
As per claim 4, MAHBUB discloses the system of claim 1, wherein to determine the first class for the first layer for each pixel of the plurality of pixels in the frame, the processing circuitry is configured to assign a respective first class for each pixel to one class from a set of classes and to determine the second class for the second layer for each pixel of the plurality of pixels in the frame (the stored data includes stored image frames having pixel layers classified based on stored class data 216 includes a first value corresponding to a first class type, a second value corresponding to a second class type; fig 2; paragraphs [0056-0058], [0060]), the processing circuitry is configured to assign a respective second class for each pixel to the one class or another class from the set of classes (class data 216 includes a first value corresponding to a first class type, and a second value corresponding to a second class type; fig 2; paragraphs [0056-0058], [0060]).
As per claim 5, MAHBUB discloses the system of claim 1, wherein the processing circuitry is further configured to determine a third class for a third layer for each pixel of the plurality of pixels in the frame (the system is adapted to perform this for Nth number of layers using Nth number of classes of pixel layers in the image frame; fig 3; paragraph [0063], [0092]).
As per claim 7, MAHBUB discloses the system of claim 1, wherein a portion of the first object occludes a portion of the second object, and wherein the processing circuitry is further configured to: determine that pixels occluded by the first object correspond to the second object based on the occluded pixels being assigned to the first class for the first layer and to the second class for the second layer (the bounding boxes may intersect to form intersection over union which is found as a value to show the intersection of the object bounding boxes relating to the first and second object; figs 4B, 8; paragraphs [0065-0067], [0091-0093]).
As per claim 8, MAHBUB discloses the system of claim 1, wherein the processing circuitry is further configured to determine a number of layers for each pixel of the plurality of pixels in the frame based on a scenario of the frame (the computing system is adapted to identify objects to the Nth amount of objects which allows the system to be adaptable to any scenario or environment and the object parameters may be adjusted based on said scenario; paragraph [0092]).
As per claim 9, MAHBUB discloses the system of claim 1, wherein the sensor comprises one or more of a LiDAR sensor or a camera (the sensor is a camera image sensor; paragraph [0057]).
As per claim 12, MAHBUB discloses a method comprising: receiving a frame from a sensor (the system further comprising CPU 116 acting as the computing processor for the system to execute programs/instructions using the stored data; fig 1; paragraph [0016]); determining a first class for a first layer for each pixel of a plurality of pixels in the frame (the user determines threshold values for a plurality of classes including a first in order to sort pixel layers into each class including a first layer of pixels; abstract; fig 5B; paragraphs [0016], [0057-0059], [0062]); determining a second class for a second layer for each pixel of the plurality of pixels in the frame (the computing system further is adapted to determine a second class of the plurality of classes having its own respective threshold value and has Nth layers sorted/pooled into it based on the threshold value; figs 5B-6; paragraphs [0016], [0057-0062], [0064]); identifying a first object in the frame based on the first class for each pixel of the plurality of pixels (each frame is divided into layers in order to allow for the system to use a generated flattening layer 210 is configured to receive Nth layer output data 209 from Nth layer 208, and perform operations to generate a feature vector 211 and includes linear layer 212 which is configured to receive the feature vector 211 from the flattening layer 210 and, generate one or more fully-connected layers that provide the object data 214 (which would include all objects including a first within the frame), the class data 216, and the bounding box data 218; figs 3, and 5B-7; paragraphs [0059], [0062-0064]); and identifying a second object in the frame based on the first class for each pixel of the plurality of pixels and based on the second class for each pixel of the plurality of pixels (using object data 214 which includes all objects of the image frame the system is adapted to using neural network 200 and first layer class anchor 250 and first layer bounding box "bbox" anchor 252 that each operate on first layer output data 205, additionally neural network 200 includes second layer class anchor 254 and second layer bounding box anchor 256 that each operate on second layer output data 207 for a second object and is repeated for Nth number of objects detected in the frame; figs 3, and 5B-7; paragraphs [0059], [0063-0065], [0070-0071]), wherein a portion of the plurality of pixels correspond to both the first object and the second object (wherein the system is adapted to track an intersection over union value IOU of the bounding boxes which identify the respective objects wherein the over lapping area would include pixels that correspond to both a first and second object if the overlap existed within the particular captured and scanned image frame; fig 8; paragraphs [0019], [0093], [0131]).
As per claim 13, MAHBUB discloses the method of claim 12, wherein determining the first class for the first layer for each pixel of the plurality of pixels in the frame and to determine the second class for the second layer for each pixel of the plurality of pixels in the frame comprises inputting the frame into a dynamic neural network (neural network 200 is trained in real time to classify pixel layers and identify objects of the input image frame based on the trained neural network; paragraphs [0057-0061], [0063-0070]).
As per claim 14, MAHBUB discloses the method of claim 13, wherein the dynamic neural network comprises a neural network trained using multi-layer training data, wherein the multi-layer training data includes training frames, with each training frame including a plurality of annotated pixels, each annotated pixel having a plurality of corresponding layers, and each corresponding layer being assigned to a class from a set of classes (image capture device 100 applies the initially trained neural network 200 to a validation set (of generated images including pixel layers), and may determine whether the initially trained neural network 200 is sufficiently trained based on the object data 214, class data 216, and bounding box data 218 generated during the validation process, image capture device 100 may compute one or more metrics based on the object data 214, class data 216, and bounding box data 218 generated during the validation if the computed metrics indicate that neural network 200 is not sufficiently trained (e.g., the one or more computed metrics do not meet their corresponding thresholds), the one or more processors execute the instructions to continue training neural network 200 ( e.g., with additional generated training images including pixel layers), once neural network 200 is sufficiently trained, image capture device 100 may store configuration parameters, hyperparameters, and/or weights associated with the trained neural network 200 within, for example, instruction memory 132; paragraphs [0063-0070]).
As per claim 15, MAHBUB discloses the method of claim 12, wherein determining the first class for the first layer for each pixel of the plurality of pixels in the frame comprises assigning a respective first class for each pixel to one class from a set of classes and determining the second class for the second layer for each pixel of the plurality of pixels in the frame (the stored data includes stored image frames having pixel layers classified based on stored class data 216 includes a first value corresponding to a first class type, a second value corresponding to a second class type; fig 2; paragraphs [0056-0058], [0060]) comprises assigning a respective second class for each pixel to the one class or another class from the set of classes (class data 216 includes a first value corresponding to a first class type, and a second value corresponding to a second class type; fig 2; paragraphs [0056-0058], [0060]).
As per claim 16, MAHBUB discloses the method of claim 12, further comprising: determining a third class for a third layer for each pixel of the plurality of pixels in the frame (the system is adapted to perform this for Nth number of layers using Nth number of classes of pixel layers in the image frame; fig 3; paragraph [0063], [0092]).
As per claim 18, MAHBUB discloses the method of claim 12, wherein a portion of the first object occludes a portion of the second object, and wherein the method further comprises: determining that pixels occluded by the first object correspond to the second object based on the occluded pixels being assigned to the first class for the first layer and to the second class for the second layer (the bounding boxes may intersect to form intersection over union which is found as a value to show the intersection of the object bounding boxes relating to the first and second object; figs 4B, 8; paragraphs [0065-0067], [0091-0093]).
As per claim 19, MAHBUB discloses the method of claim 12, further comprising: determining a number of layers for each pixel of the plurality of pixels in the frame based on a scenario of the frame (the computing system is adapted to identify objects to the Nth amount of objects which allows the system to be adaptable to any scenario or environment and the object parameters may be adjusted based on said scenario; paragraph [0092]).
As per claim 20, MAHBUB discloses the method of claim 13, wherein the sensor comprises one or more of a LiDAR sensor or a camera (the sensor is a camera image sensor; paragraph [0057]).
As per claim 21, MAHBUB discloses a computer-readable storage medium storing instructions that when executed by one or more processors cause the one or more processor to (a computing system adapted to perform automatic object detection within image frames the computing system comprises a plurality of memory components including instruction memory 132, system memory 130, and memory controller 124 adapted to store instructions, programs, and data/images; fig 1; abstract; paragraph [0016]): receive a frame from a sensor (the system further comprising CPU 116 acting as the computing processor for the system to execute programs/instructions receiving the stored data including image frames; fig 1; paragraph [0016]); determine a first class for a first layer for each pixel of a plurality of pixels in the frame (the user determines threshold values for a plurality of classes including a first in order to sort pixel layers into each class including a first layer of pixels; abstract; fig 5B; paragraphs [0016], [0057-0059], [0062]); determine a second class for a second layer for each pixel of the plurality of pixels in the frame (the computing system further is adapted to determine a second class of the plurality of classes having its own respective threshold value and has Nth layers sorted/pooled into it based on the threshold value; figs 5B-6; paragraphs [0016], [0057-0062], [0064]); identify a first object in the frame based on the first class for each pixel of the plurality of pixels (each frame is divided into layers in order to allow for the system to use a generated flattening layer 210 is configured to receive Nth layer output data 209 from Nth layer 208, and perform operations to generate a feature vector 211 and includes linear layer 212 which is configured to receive the feature vector 211 from the flattening layer 210 and, generate one or more fully-connected layers that provide the object data 214 (which would include all objects including a first within the frame), the class data 216, and the bounding box data 218; figs 3, and 5B-7; paragraphs [0059], [0062-0064]); and identify a second object in the frame based on the first class for each pixel of the plurality of pixels and based on the second class for each pixel of the plurality of pixels (using object data 214 which includes all objects of the image frame the system is adapted to using neural network 200 and first layer class anchor 250 and first layer bounding box "bbox" anchor 252 that each operate on first layer output data 205, additionally neural network 200 includes second layer class anchor 254 and second layer bounding box anchor 256 that each operate on second layer output data 207 for a second object and is repeated for Nth number of objects detected in the frame; figs 3, and 5B-7; paragraphs [0059], [0063-0065], [0070-0071]), wherein a portion of the plurality of pixels correspond to both the first object and the second object (wherein the system is adapted to track an intersection over union value IOU of the bounding boxes which identify the respective objects wherein the over lapping area would include pixels that correspond to both a first and second object if the overlap existed within the particular captured and scanned image frame; fig 8; paragraphs [0019], [0093], [0131]).
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 non-obviousness.
Claims 6, and 17 are rejected under 35 § U.S.C. 103 as being obvious over US 2023/0206613 A1 to MAHBUB et al. (hereinafter “MAHBUB”) in view of US 2008/0008349 A1 to BINNIG et al (hereinafter “BINNIG”).
As per claim 6, MAHBUB discloses the system of claim 1. MAHBUB fails to disclose wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer.
BINNIG discloses wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer (the pixel layers are sorted and grouped as foreground layers in the fore ground classification bin and sorts background pixel layers into the background class; fig 5, 13; paragraphs [0070], [0080-0082], [0089], [0094], [0097], [0099]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MAHBUB to have wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer of BINNIG reference. The Suggestion/motivation for doing so would have been to provide the system to sort pixels in order to further differentiate if the pixel is part of an object of interest as suggested by BINNIG paragraph [0079]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BINNIG with MAHBUB to obtain the invention as specified in claim 6.
As per claim 17, MAHBUB discloses the method of claim 12. MAHBUB fails to disclose wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer.
BINNIG discloses wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer (the pixel layers are sorted and grouped as foreground layers in the fore ground classification bin and sorts background pixel layers into the background class; fig 5, 13; paragraphs [0070], [0080-0082], [0089], [0094], [0097], [0099]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MAHBUB to have wherein the first layer corresponds to a foreground layer of the frame and the second layer corresponds to a background layer of the frame that is behind the foreground layer of BINNIG reference. The Suggestion/motivation for doing so would have been to provide the system to sort pixels in order to further differentiate if the pixel is part of an object of interest as suggested by BINNIG paragraph [0079]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BINNIG with MAHBUB to obtain the invention as specified in claim 17.
Claims 10-11 are rejected under 35 § U.S.C. 103 as being obvious over US 2023/0206613 A1 to MAHBUB et al. (hereinafter “MAHBUB”) in view of US 2023/0368544 A1 to NARAYAN et al. (hereinafter “NARAYAN”).
As per claim 10, MAHBUB discloses the system of claim 1. MAHBUB fails to disclose wherein the one or more processors are part of an advanced driver assistance system (ADAS).
NARAYAN discloses wherein the one or more processors are part of an advanced driver assistance system (ADAS) (the system comprising the CPU are integrated into a self-driving autonomous vehicle using object detection technology; abstract; paragraphs [0021-0022]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MAHBUB to have wherein the one or more processors are part of an advanced driver assistance system (ADAS) of NARAYAN reference. The Suggestion/motivation for doing so would have been to allow the vehicle to discern where it can travel to safely as suggested by NARAYAN paragraph [0022]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine NARAYAN with MAHBUB to obtain the invention as specified in claim 10.
As per claim 11, MAHBUB discloses the system of claim 1. MAHBUB fails to disclose wherein the one or more processors are external to an advanced driver assistance system (ADAS).
NARAYAN discloses wherein the one or more processors are external to an advanced driver assistance system (ADAS) (the system comprising the CPU are integrated into a self-driving autonomous vehicle using object detection technology, wherein the object detection methods may be performed by an on board computer and control of the vehicle is controlled by a second on board computer using the results of the first computer provided by MAHBUB; abstract; paragraphs [0009], [0021-0022], [0064], [0073]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MAHBUB to have wherein the one or more processors are external to an advanced driver assistance system (ADAS) of NARAYAN reference. The Suggestion/motivation for doing so would have been to allow the vehicle to discern where it can travel to safely as suggested by NARAYAN paragraph [0022]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine NARAYAN with MAHBUB to obtain the invention as specified in claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 12,437,412 B2
US 2023/0360386 A1
US 2024/0410981 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677