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 .
Claim Rejections - 35 USC § 102
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)(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, 19, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by
Radhakrishnan (Pub. No.: US 2025/0029409A1).
With respect to claim 1:
Radhakrishnan discloses a system comprising a memory device (fig. 3, item, 310); and at least one processor communicatively coupled to the memory device (fig. 3, item 308), wherein the at least one processor is configured to: receive image data characterizing a captured image (fig. 16A, item 1506 include an image data); apply a first trained machine learning process to the image data and, based on the application of the first trained machine learning process to the image data, generate first output data characterizing regions of the image data that include at least one object (fig. 16A, item 1604 is a first trained learning process receiving an input image from 1506); apply a second trained machine learning process to the first output data and, based on the application of the second trained machine learning process to the first output data (fig. 16A, item 1612 is a second machine learning process receiving the first output), generate second output data (fig. 16A, Refined data is the second output) characterizing a classification of the at least one object in at least one of the regions (parag. 0061 discloses classification/ ROI); and store the second output data in a data repository (parag. 0171 discloses that the output data is uploaded into item 1506 which is repository).
With respect to claim 19:
Radhakrishnan discloses a method by at least one processor comprising receiving image data characterizing a captured image (fig. 16A, item 1506 include an image data); applying a first trained machine learning process to the image data and, based on the application of the first trained machine learning process to the image data, generate first output data characterizing regions of the image data that include at least one object (fig. 16A, item 1604 is a first trained learning process receiving an input image from 1506); applying a second trained machine learning process to the first output data and, based on the application of the second trained machine learning process to the first output data (fig. 16A, item 1612 is a second machine learning process receiving the first output), generate second output data (fig. 16A, Refined data is the second output) characterizing a classification of the at least one object in at least one of the regions (parag. 0061 discloses classification/ ROI); and storing the second output data in a data repository (parag. 0171 discloses that the output data is uploaded into item 1506 which is repository).
With respect to claim 20:
Radhakrishnan discloses a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising: receiving image data characterizing a captured image (fig. 16A, item 1506 include an image data); applying a first trained machine learning process to the image data and, based on the application of the first trained machine learning process to the image data, generate first output data characterizing regions of the image data that include at least one object (fig. 16A, item 1604 is a first trained learning process receiving an input image from 1506); applying a second trained machine learning process to the first output data and, based on the application of the second trained machine learning process to the first output data (fig. 16A, item 1612 is a second machine learning process receiving the first output), generate second output data (fig. 16A, Refined data is the second output) characterizing a classification of the at least one object in at least one of the regions (parag. 0061 discloses classification/ ROI); and storing the second output data in a data repository (parag. 0171 discloses that the output data is uploaded into item 1506 which is repository).
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.
Claims 2-6, 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan (Pub. No.: US 2025/0029409A1) as applied to claim 1 above, and further in view of Gruenstein (Pub. No.: US 2016/0379113 A1).
With respect to claim 2:
The rejection of claim 1 is incorporated; Radhakrishnan does not explicitly disclose wherein the first output data comprises a confidence value for each of the regions, and wherein the at least one processor is configured to determine that the confidence value for at least one of the regions is beyond a region detection threshold.
Gruenstein discloses wherein the first output data comprises a confidence value for each of the regions, and wherein the at least one processor is configured to determine that the confidence value for at least one of the regions is beyond a region detection threshold (abstract and parag. 0003-0004 discloses that the posterior probability vector satisfy threshold value). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to utilize the teaching of Gruenstein into the teaching of Radhakrishnan in order to
reduce central processing unit (CPU) usage, power consumption, and/or network bandwidth usage.
With respect to claim 3:
Gruenstein discloses the system of claim 2, wherein the at least one processor is configured to determine that the confidence value for at least one of the regions is not beyond the region detection threshold; and adjust the first output data to remove the corresponding region based on the determination (abstract and parag. 0003-0004 discloses that the posterior probability vector satisfy threshold value).
With respect to claim 4:
Gruenstein discloses the system of claim 1, wherein the second output data comprises a confidence value for each classification, and wherein the at least one processor is configured to determine that the confidence value for at least one of the classifications is beyond an object detection threshold (abstract and parag. 0003-0004 discloses that the posterior probability vector satisfy threshold value).
With respect to claim 5:
Gruenstein discloses the system of claim 4, wherein the at least one processor is configured to: determine that the confidence value for at least one of the classifications is not beyond the object detection threshold; and adjust the second output data to remove the classification based on the determination (abstract and parag. 0003-0004 discloses that the posterior probability vector satisfy threshold value).
With respect to claim 6:
Radhakrishnan discloses the system of claim 1, wherein each of the regions comprise a corresponding portion of the image data (parag. 0026).
With respect to claim 8:
Radhakrishnan discloses the system of claim 1, wherein the at least one object is of any of a predetermined number of classes (parag. 0027 discloses pre-defined set of classes).
With respect to claim 9:
Radhakrishnan discloses the system of claim 1, wherein the second trained machine learning process is based on a pixel segmentation network (parag. 0054).
With respect to claim 10:
Radhakrishnan discloses the system of claim 9, wherein the second output data comprises, for each classification, a pixel location, a class value, and a confidence value (parag. 0049).
With respect to claim 11:
Radhakrishnan discloses the system of claim 1, wherein the second trained machine learning process is based on an object detection network (abstract, parag. 0030, 0045).
With respect to claim 12:
Radhakrishnan discloses the system of claim 11, wherein the second output data comprises, for each classification, a bounding box, a class value, and a confidence value (abstract and parag. 0026).
With respect to claim 13:
Radhakrishnan discloses the system of claim 1, wherein the classification of the at least one object is one of a vehicle and infrastructure (parag. 0024, 0079).
With respect to claim 14:
Radhakrishnan discloses the system of claim 1, wherein the at least one processor is configured to: generate at least one graphical user interface element based on the second output data; and transmit the at least one graphical user interface element for display (parag. 0036, 0090).
Claims 7, 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan (Pub. No.: US 2025/0029409A1) as applied to claim 1 above, and further in view of GU (Pub. No.: US 2022/0281177 A1).
With respect to claim 7:
The rejection of claim 1 is incorporated; Radhakrishnan does not explicitly disclose wherein the first trained machine learning process is based on a residual network.
GU discloses wherein the first trained machine learning process is based on a residual network (parag. 0056). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to utilize the teaching of GU into the teaching of Radhakrishnan for calibration purpose.
With respect to claim 15:
GU discloses the system of claim 1, wherein the at least one processor is configured to train the first trained machine learning process based on epochs of training image data comprising labelled regions (parag. 0056).
With respect to claim 16:
GU discloses the system of claim 15, wherein the at least one processor is configured to validate the first trained machine learning process based on epochs of validating image data comprising regions (parag. 0056).
With respect to claim 17:
GU discloses the system of claim 1, wherein the at least one processor is configured to train the second trained machine learning process based on epochs of training image data comprising labelled objects within regions (parag. 0056 discloses image data is trained for a sufficient number of epochs).
With respect to claim 18:
GU discloses the system of claim 17, wherein the at least one processor is configured to validate the second trained machine learning process based on epochs of validating image data comprising objects within labelled regions (parag. 0056 discloses epochs of validation).
Conclusion
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/AJIBOLA A AKINYEMI/Primary Examiner, Art Unit 2649
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