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 .
Election/Restrictions
Applicant’s election without traverse of Group I in the reply filed on 09/08/2026 is acknowledged.
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.
Claims 1–2, 6–11, 15–16 and 21–24 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhou et al. (U.S. 11,701,996 B2) in view of Longland et al. (U.S. 10,909,951 B2).
Regarding claim 1, Zhou discloses a method comprising:
receiving image data obtained using one or more cameras, the image data representative of one or more images depicting one or more fields of view associated with a machine in an environment; (Per Fig. 2, Zhou’s cameras 202a collects images an environment of a vehicle 200. Zhou col. 7 lines 32–67. [c]amera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision).)
computing, using the image data, a set of contrast values1 corresponding to a bounding shape of an object detected in the one or more fields of view. (Per Fig. 8, After Zhou’s perception system 402 executes an object feature network 702 in his neural network system, his system generates a bounding box corresponding to detected object(s) from a perception output 814A. Ibid. col. 24 lines 17–39. Zhou’s raw sensor data as described in this section is understood to include contrast values within the bounding boxes, as claimed in claim 1.)
However, Zhou fails to specifically disclose detecting glare corresponding to the object based at least one analyzing the set of contrast values; and
causing the machine to perform one or more operations based at least on the glare.
In related art, Longland discloses detecting glare corresponding to the object based at least one analyzing the set of contrast values; and (Per Fig. 1, Longland’s system 110 detects glare from light source 108 adjusting pixel brightness values. Longland col. 4 lines 34–43. [t]he screen manager 110 can modify pixel brightness values, contrast values, or other values that may improve the ability of user 106 to clearly see the portions of display 102 affected by glare from light source 108.)
causing the machine to perform one or more operations (One or more operations construed as mitigating glares. See Applicant’s Spec. ¶45.) based at least on the glare. (Per Fig. 2B, Longland discloses a reduced glare area 202B based on pixel values of glare detection. Ibid. col. 5 lines 33–45. The second brightness/contrast can be pixel values associated with brightness, contrast, saturation, hue, color, luminance, luma, and/or other values that can improve the clarity of modified glare area 202B relative to glare area 202A.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Longland into the teachings of Zhou because a person of ordinary skill in the art would readily recognize that tracking residual glare provides a straightforward metric for evaluating system performance. That is, the foundational elements of glare reduction based on pixel values are fully anticipated by the prior art, configuring the system to detect the presence of that same phenomenon is merely a conventional architecture within the grasp of an ordinary artisan.
Regarding claim 10, Zhou discloses a method comprising:
one or more processors to perform operations including: (Fig. 3, 304 a processor)
receiving image data obtained using one or more cameras, the image data representative of one or more images depicting one or more fields of view associated with a machine in an environment; (Per Fig. 2, Zhou’s cameras 202a collects images an environment of a vehicle 200. Zhou col. 7 lines 32–67. [c]amera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision).)
computing, using the image data, a set of contrast values corresponding to a bounding shape of an object detected in the one or more fields of view. (Per Fig. 8, After Zhou’s perception system 402 executes an object feature network 702 in his neural network system, his system generates a bounding box corresponding to detected object(s) from a perception output 814A. Ibid. col. 24 lines 17–39.)
However, Zhou fails to specifically disclose detecting glare corresponding to the object based at least one analyzing the set of contrast values; and
transmitting data to cause the machine to perform one or more operations based at least on the glare.
In related art, Longland discloses detecting glare corresponding to the object based at least one analyzing the set of contrast values; and (Per Fig. 1, Longland’s system 110 detects glare from light source 108 adjusting pixel brightness values. Longland col. 4 lines 34–43. [t]he screen manager 110 can modify pixel brightness values, contrast values, or other values that may improve the ability of user 106 to clearly see the portions of display 102 affected by glare from light source 108.)
transmitting data to cause the machine to perform one or more operations (One or more operations construed as mitigating glares. See Applicant’s Spec. ¶45.) based at least on the glare. (Per Fig. 2B, Longland discloses a reduced glare area 202B based on pixel values of glare detection. Ibid. col. 5 lines 33–45. The second brightness/contrast can be pixel values associated with brightness, contrast, saturation, hue, color, luminance, luma, and/or other values that can improve the clarity of modified glare area 202B relative to glare area 202A.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Longland into the teachings of Zhou because a person of ordinary skill in the art would readily recognize that tracking residual glare provides a straightforward metric for evaluating system performance. That is, the foundational elements of glare reduction based on pixel values are fully anticipated by the prior art, configuring the system to detect the presence of that same phenomenon is merely a conventional architecture within the grasp of an ordinary artisan.
Regarding claim 21, Zhou discloses a method comprising:
processing circuitry to perform operations including: (Fig. 3, 304 a processor)
receiving image data obtained using one or more cameras, the image data representative of one or more images depicting one or more fields of view associated with a machine in an environment; (Per Fig. 2, Zhou’s cameras 202a collects images an environment of a vehicle 200. Zhou col. 7 lines 32–67. [c]amera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision).)
computing, using the image data, a set of contrast values corresponding to a bounding shape of an object detected in the one or more fields of view. (Per Fig. 8, After Zhou’s perception system 402 executes an object feature network 702 in his neural network system, his system generates a bounding box corresponding to detected object(s) from a perception output 814A. Ibid. col. 24 lines 17–39.)
However, Zhou fails to specifically disclose detecting glare corresponding to the object based at least one analyzing the set of contrast values; and
transmitting data to cause the machine to perform one or more operations based at least on the glare.
In related art, Longland discloses detecting glare corresponding to the object based at least one analyzing the set of contrast values; and (Per Fig. 1, Longland’s system 110 detects glare from light source 108 adjusting pixel brightness values. Longland col. 4 lines 34–43. [t]he screen manager 110 can modify pixel brightness values, contrast values, or other values that may improve the ability of user 106 to clearly see the portions of display 102 affected by glare from light source 108.)
transmitting data to cause the machine to perform one or more operations (One or more operations construed as mitigating glares. See Applicant’s Spec. ¶45.) based at least on the glare. (Per Fig. 2B, Longland discloses a reduced glare area 202B based on pixel values of glare detection. Ibid. col. 5 lines 33–45. The second brightness/contrast can be pixel values associated with brightness, contrast, saturation, hue, color, luminance, luma, and/or other values that can improve the clarity of modified glare area 202B relative to glare area 202A.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Longland into the teachings of Zhou because a person of ordinary skill in the art would readily recognize that tracking residual glare provides a straightforward metric for evaluating system performance. That is, the foundational elements of glare reduction based on pixel values are fully anticipated by the prior art, configuring the system to detect the presence of that same phenomenon is merely a conventional architecture within the grasp of an ordinary artisan.
Regarding claim 2, Zhou as modified by Longland, discloses the method, further including determining the bounding shape based at least on applying one or more portions of the image data to one or more machine learning models to output data indicating at least one of a size or a location of the bounding shape. (Per Fig. 4A, Zhou’s localization system 406 discloses a location of an object. Zhou col. 13 line 65 – col. 14 line 28. [l]ocalization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area.)
Regarding claim 11, it has been rejected in the same manner as claim 2.
Regarding claim 22, it has been rejected in the same manner as claim 2.
Regarding claim 6, Zhou as modified by Longland, discloses the method, wherein the detecting of the glare is based at least on relative magnitudes between contrast values from the set of contrast values corresponding to the bounding shape. (Per Fig. 2B, Longland discloses a first brightness in a first portion and a second brightness in a modified glare area 202B. Longland col. 5 lines 33–45. Display 102 includes a first portion of the screen 200B at a first brightness/contrast and the modified glare area 202B at a second brightness/contrast.)
Regarding claim 15, it has been rejected in the same manner as claim 6.
Regarding claim 7, Zhou as modified by Longland, discloses the method, further comprising determining that one or more dimensions of the bounding shape satisfy one or more size thresholds, wherein the one or more operations are performed based at least on the one or more dimensions satisfying the one or more size thresholds. (Per Fig. 8A at an operation 806, Longland discloses a threshold is determined to identify a glare area. Longland col. 10 lines 1–9. [i]f the angle 702 is within the threshold (e.g. YES at operation 806), then the method 800 proceeds to operation 808 and identifies the area including the incidence point 600 as a glare area.)
Regarding claim 8, Zhou as modified by Longland, discloses the method, further comprising:
determining positional information associated with the bounding shape of the object detected in the one or more fields of view; (Per Fig. 2, Zhou’s cameras 202a collects images an environment of a vehicle 200. Zhou col. 7 lines 32–67. [c]amera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision).)
wherein the one or more operations include one or more glare mitigation operations associated with the object based at least on determining the positional information indicates a likelihood that light emitted by one or more lighting elements of the machine reflects off of the object in a direction of at least one occupant or at least one sensor of the machine. (Per Fig. 2, Zhou’s LiDAR sensor 202b detects a light which is emitted from an object. Ibid. 8 lines 17–44. LiDAR sensors 202b also include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object.)
Regarding claim 9, Zhou as modified by Longland, discloses the method, wherein the one or more operations include one or more glare mitigation operations associated with the object based at least on determining, with respect to positional information associated with the bounding shape, an angle relative to a direction of travel of the machine. (Per Fig. 5 at step 502, Longland discloses an angle between his camera 104A and an object. Longland col. 7 lines 4–11. Operation 502 includes determining a first angle 406 between the first camera 104A and the object (e.g., light source 108, user 106, etc.).)
Regarding claim 16, Zhou as modified by Longland, discloses the system, wherein the system is comprised in at least one of: a perception system for an autonomous or semi-autonomous machine. (Fig. 4A, 402 a perception system)
Regarding claim 23, Zhou as modified by Longland, discloses the at least one processor, wherein the detecting of the glare is performed using one or more external sensors associated with one or more fields of view or sensor fields external to an autonomous or semi-autonomous vehicle. (Fig. 2, 202b LiDAR sensors)
Regarding claim 24, it has been rejected in the same manner as claim 16.
Claims 3 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhou in view of Longland and further in view of Moriya et al. (U.S. 2019/0259343 A1).
Regarding claim 3, Zhou as modified by Longland, discloses the claimed invention, but fails to disclose the method, wherein the set of contrast values includes: a first contrast value corresponding to a first ratio between luminance for at least one of first pixel within the bounding shape and a reference luminance, and a second contrast value corresponding to a second ratio between luminance for at least one second pixel within the bounding shape and the reference luminance, and the detecting of the glare is based at least on the first ratio and the second ratio.
In related art, Moriya discloses the method, wherein the set of contrast values includes:
a first contrast value corresponding to a first ratio between luminance for at least one of first pixel within the bounding shape and a reference luminance, and (Per Fig. 7, Moriya’s high luminance pixel searching unit 126 discloses a high luminance pixel based on the reference data. Moriya Spec. ¶81. The high luminance pixel searching unit 126 searches for a high luminance pixel from a plurality of pixels within the predetermined range centered on the target pixel based on the reference data Dref. Searching for a high luminance pixel is performed according to a predetermined determination criterion as described later.)
a second contrast value corresponding to a second ratio between luminance for at least one second pixel within the bounding shape and the reference luminance, and the detecting of the glare is based at least on the first ratio and the second ratio. (Per Fig. 10, Moriya discloses Moriya determines whether the luminance value L of a search pixel is larger than a first threshold value TH1 and less than a second threshold value TH2. Ibid. ¶108. [i]f the luminance value L of the search pixel is larger than the first threshold value TH1 and less than the second threshold value TH2, the value of the assumed mixing ratio R is set to the value of the function f((D/D_MAX).Math.((TH2−TH1)/(L−TH1))),)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Moriya into the teachings of Zhou and Longland to add a gradation area comparing a luminance between two thresholds, rather than one threshold which causes discomfort after the glare correction. Ibid. ¶116.
Regarding claim 12, it has been rejected in the same manner as claim 3.
Claims 4–5 and 13–14 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhou in view of Longland and further in view of Hamada (U.S. 8,749,658 B2).
Regarding claim 4, Zhou as modified by Longland, discloses the claimed invention, but fails to specifically disclose the method, further comprising computing a reference luminance corresponding to a statistical combination of luminance values of a plurality of pixels at least partially outside of the bounding shape, wherein the set of contrast values are computed relative to the reference luminance.
In related art, Hamada discloses the method, further comprising computing a reference luminance corresponding to a statistical combination of luminance values of a plurality of pixels at least partially outside of the bounding shape, wherein the set of contrast values are computed relative to the reference luminance. (Per Fig. 2, Hamada’s computing section 403 discloses a statistical amount of different values in adjacent pixel group. Hamada col. 6 lines 5–17. [r]epresentative value computing section 403 obtains the statistical amount of the difference values with respect to each pixel contained in the neighboring pixel group and designates the obtained statistical amount as the representative value with respect to the target pixel.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Hamada into the teachings of Zhou and Longland to extract a set of pixels that are similarly affected by shading. Ibid. col. 5 lines 20–34.
Regarding claim 13, it has been rejected in the same manner as claim 4.
Regarding claim 5, Zhou as modified by Longland, discloses the claimed invention, but fails to specifically disclose the method, wherein the set of contrast values includes: identifying a plurality of pixels based at least on proximities of the plurality of pixels to the bounding shape; and based at least on the identifying, computing a reference luminance corresponding to the plurality of pixels, wherein the set of contrast values are computed relative to the reference luminance.
In related art, Hamada discloses the method, wherein the set of contrast values includes: identifying a plurality of pixels based at least on proximities of the plurality of pixels to the bounding shape; and (Per Fig. 2, Hamada discloses a set of pixels corresponding to a shape of an object. Hamada col. 6 lines 5–17. Representative value computing section 403 first extracts a neighboring pixel group that is a set of pixels that are present at neighboring positions of the target pixel as those similarly influenced by the shading due to the shape of the object under illumination variation.)
based at least on the identifying, computing a reference luminance corresponding to the plurality of pixels, wherein the set of contrast values are computed relative to the reference luminance. (Per Fig. 2, Hamada’s computing section 403 discloses a statistical amount of different values in adjacent pixel group. Ibid. [r]epresentative value computing section 403 obtains the statistical amount of the difference values with respect to each pixel contained in the neighboring pixel group and designates the obtained statistical amount as the representative value with respect to the target pixel.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Hamada into the teachings of Zhou and Longland to extract a set of pixels that are similarly affected by shading. Ibid. col. 5 lines 20–34.
Regarding claim 14, it has been rejected in the same manner as claim 5.
Conclusion
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 Per Fig. 4C at step 455, Zhou’s perception system 402 renders a CNN 440 where a color image is trained to correspond to values, which is correlated to a region thereof. Ibid. col. 17 line 54 – col. 18 line 3. [t]he values representing the image can correspond to values representing a region of the image (sometimes referred to as a receptive field).
