DETAILED ACTION
Claim 10 objected to because of the following informalities:
Claim(s) 1,3,4,6 and 15,17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation:
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of Weinzaepfel et al. (US 2020/0364509 A1) further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation:
Claim(s) 5 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation:
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of ZHANG et al. (Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes: 15 July 2020):
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of Li et al. (US 2015/0278616 A1):
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of KAINO (DE 10 2019 104 113 A1) with SEARCH machine translation:
Claim(s) 9,12,14 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2):
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of Aoba e al. (US 2019/0012790 A1) as applied to claims 9,12,14 above further in view of TAKAHITO (DE 112015000723 T) with SEARCH machine translation:
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of Koch (US 2022/0309678 A1):
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of JIANG et al. (CN 111402336 A: Date Published 2020-07-10: July 10, 2020) with SEARCH machine translation:
Claim(s) 1,3,4,6 and 15,17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation:
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of Weinzaepfel et al. (US 2020/0364509 A1) and YUREVICH (RU 2676028 C1) with SEARCH machine translation:
Claim(s) 5 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation as applied in claim 2:
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of Li et al. (US 2015/0278616 A1):
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of KAINO (DE 10 2019 104 113 A1) with SEARCH machine translation:
Claim(s) 9,12,14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2):
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of TAKAHITO (DE 112015000723 T) with SEARCH machine translation:
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of Koch (US 2022/0309678 A1):
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of JIANG et al. (CN 111402336 A: Date Published 2020-07-10: July 10, 2020) with SEARCH machine translation:
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Applicant’s claim for the benefit of a prior-filed application under 35 U.S .C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/885,774 08/12/2019, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application:
Claims 5,7,8,10,11,13,16 not given the benefit of Application No. 62/885,774 08/12/2019 and thus given the 35 USC 102(a) date of 8/12/2020 of CON of 16/991,242 08/12/2020 PAT 11,613,201:
Re claims 5 &10, The claimed “stationary” of claims 5 and 10 is not in Application No. 62/885,774 08/12/2019.
Re claims 7,11 The claimed “dim region masks” of claim 7 is not in Application No. 62/885,774 08/12/2019 that instead discloses “a binary mask”, 1st page, [0004]: penultimate sentence is not in Application No. 62/885,774 08/12/2019; and
Re claims 8 & 16, The claimed “recursively weighting” of claims 8 and 16 is not in Application No. 62/885,774 08/12/2019.
Re claim 13, The claimed “consistent assignments” of claim 13 is not in Application No. 62/885,774 08/12/2019.
Thus claims 5,7,8,10,11,13,16 not given the benefit of Application No. 62/885,774 08/12/2019 and thus given the 35 USC 102(a) date of 8/12/2020 of CON of 16/991,242 08/12/2020 PAT 11,613,201:
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Claim Objections
Claim 10 objected to because of the following informalities:
Claim 10, line 4’s comma of “NNs,” ought be deleted for grammar:
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Appropriate correction is required.
Response to Arguments
Rejections based on 35 USC 101
Applicant’s arguments, see remarks, pg. 11, filed 4/7/2026, with respect to 35 USC 101 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 9-14 has been withdrawn.
Rejections based on 35 USC 103
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive.
Applicants state in page 13:
Regarding Wang, the combination of Yi with Wang does not remedy the deficiencies of Wang. For example, in contrast to "segmentation mask confidences that one or more labels apply to corresponding pixels in the one or more images, the one or more labels classifying the corresponding pixels as depicting one or more actors in one or more states represented by the one or more labels," Yi describes calculating a spatial reliability map that "identifies the probability of a pixel belonging to the foreground mask" and is "computed to distinguish the target from the background." See Section 3.2 of Yi. Whether a pixel belongs to a tracked object is significantly different than "one or more labels apply to corresponding pixels in the one or more images, the one or more labels classifying the corresponding pixels as depicting one or more actors in one or more states represented by the one or more labels," as recited in amended claim 1.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The combination of Wang and Li thus teaches:
segmentation (foreground-image) mask confidences (prob_o) that one or more labels (understood given a mask1 that has a “1” and “0” label) apply to corresponding pixels in the one or more images, the one or more labels classifying the corresponding pixels as depicting one or more actors (being tracked in the foreground, i.e., forlikehood) in one or more states represented by the one or more labels (as label “1” for forelikehood pixel or label “0” otherwise: backlikehood pixel):
Since WANG teaches tracking (“image”-“tracked”-“target object” [0036] penult S), one of skill in the art of tracking can make WANG’s be as YI’s predictably recognizing the change as “improved” “target tracking”, YI, pg. 3, 12 txt blk, by:
A) making WANG’s “mask filtering”, pg. 13,ll. 10-15, be as YI’s foreground segmentation filtering mask, i.e., “forlikehood” in the numerator and denominator, shown below as “prob_o”:
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;
B) collecting environmental information, at WANG’s fig. 4:S402: “Collecting environmental information…”, based on the WANG’s “masking filtering” being as said forlikehood prob_o equation; and
C) track an object as shown in WANG’s fig. 4:S408: “Tracking the object…”.
Applicants state in page 13:
Thus, as Yi does not teach or suggest the segmentation mask confidences of amended claim 1, it also cannot teach or suggest "determining, based at least on temporally filtering the segmentation mask confidences over the one or more images, that one or more pixels of the one or more images depict the one or more actors in the one or more states represented by the one or more labels," as recited in amended claim 1. Instead, the combination of Yi with Wang would at best result in using the approach of Yi for tracking an object across frames using foreground masks, then analyzing the foreground masks to compute object motion of that tracked object.
The examiner respectfully disagrees since YI teaches (in above said prob_o equation: [0074]) multiplying (*) a prior temporal confidence (p_o) by the foreground selection mask2 (said forlikehood) or likewise Yi teaches (page 7, 9th txt blk) “filtering3 process the algorithm idea can basically be understood that some kind of image convolution using the prior probability”.
Applicant’s arguments with respect to claim(s) 1 and 2 and El-Khamy et al. (US 2019/0057507 A1) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument:
Claim(s) 1,3,4,6 and 15,17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation;
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of Weinzaepfel et al. (US 2020/0364509 A1) and YUREVICH (RU 2676028 C1) with SEARCH machine translation.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 14, line 1: “walking” & page 14, line 11: “account4 for any state”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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 disclosed5 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.
Claim(s) 1,3,4,6 and 15,17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation:
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) should always be the one used in rejecting the claims. Sometimes the best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) will have a publication date67 (07 May 2020) less than a year prior to the application filing date (8/12/2019), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (IDS cited Stam et al.: US 2004/0143380 A1) exists which cannot be so overcome and which, though inferior, is an adequate basis for rejection, the claims should be additionally rejected thereon:
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Re 1. (Currently Amended), WANG teaches A method (likewise) comprising8:
determining (via “analyze” [0022] 4th S, resulting in “recognized” “object”, pg. 6, [0022]), using one or more neural networks (NNs) (“CNN”, pg. 6 [0022]) and based at least on image data representative of one or more (“series”, WANG [0036] last S) images of an environment, segmentation mask9 (or likewise “mask filtering”, pg. 13: [0035] last S) confidences10 (or likewise teaches filtering/ determining a plurality of confident mask features, wherein the plurality of features have a confident quality, via “filter11 certain12 features in…image…mask filtering”,pg. 13,ll.10-15) [[of]] that13 one or more labels (understood given “mask”) apply to corresponding pixels in the one or more images, the one or more labels classifying (understood given mask) the corresponding pixels as depicting one one “pedestrian14” denoting deportment of one actor, pg. 13,ll. 15-20) in one or more states (or likewise said pedestrian in a possible detectable state via “may15 be detectable”, pg. 13,ll.15-20) represented by the one or more labels (or likewise via said pedestrian in “an image…mask” labeled as “1”,pg. 13,ll. 10-15);
determining16 , based at least on temporally1718 (“mask”, pg. 13 [0035]) filtering (Wang teaches “mask filtering” but does not teach the (adjective) modifier “temporally”) the segmentation mask confidences19 (or likewise mask filtering confident image feature states via “filter certain20 features21 in…image.…mask filtering”,pg. 13,ll.10-15) over (or “filter…in”, pg. 13 [0035]) the one or more images, that one or more pixels (expressing the action of the verb (determine) or its result, product (“the determined one or more pixels” in next limitation), material, etc.) of the one or more images depict the one or more actors in the one or more states represented by labels (understood given mask: certain/ confident image features that are labeled--“1” or “0”-- via said mask filtering);
determining a beam configuration for a lighting system (“adjustment” [0043] 1st S) based at least on the determined one or more pixels
controlling the lighting system based at least on the beam configuration (via a “controller 104…lighting adjustment…according to the lighting adjustment configuration” [0022] last S).
WANG does not teach the difference of claim 1 of:
segmentation (mask confidences)22…23
(that one or more labels) apply to corresponding pixels…
the corresponding pixels…
temporally2425 (filtering) …segmentation (mask confidences) …one or more pixels…
the determined one or more pixels.
YI teaches the difference of claim 1:
segmentation (mask) (or “segmentation” “masking” “solving space confidence map”, pg. 7, 5th txt blk, shown below as posterior confidence “prob_o”) (confidences)26…27 (via:
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(that one or more labels) apply to corresponding pixels (or likewise “the pixel belonging to28 the foreground mask”)…
the corresponding pixels (or said likewise “the pixel belonging to the foreground mask”) …
(determining, based at least on) temporally2930 (or block a prior foreground/backgnd pixel via “prior probability”3132-“filtering”33, pg. 7, 9th txt blk ) (filtering) …the one or more segmentation mask (or “segmentation” “masking” “solving space confidence map”, pg. 7, 5th txt blk, shown below as posterior probability-confidence “prob_o”) (confidences) … (via:
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, one or more pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”, pg. 7, 6th txt blk: said “forlikehood”34) …
the determined one or more pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”, pg. 7, 6th txt blk: said “forlikehood”).
Since WANG teaches tracking (“image”-“tracked”-“target object” [0036] penult S), one of skill in the art of tracking can make WANG’s be as YI’s predictably recognizing the change as “improved” “target tracking”, YI, pg. 3, 12 txt blk, by:
A) making WANG’s “mask filtering”, pg. 13,ll. 10-15, be as YI’s foreground segmentation filtering mask: “forlikehood” in the numerator and denominator, shown below as “prob_o”:
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B) collecting environmental information, at WANG’s fig. 4:S402: “Collecting environmental information…”, based on the WANG’s “masking filtering” being as said forlikehood; and
C) track an object as shown in WANG’s fig. 4:S408: “Tracking the object…”:
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Re 3. (Previuosly Presented),WANG of the combination of WANG,YI teaches The method of claim 1, wherein the determining the beam configuration includes one or more of:
adjusting the beam configuration to reduce (via “decreasing” [0023] 2nd S) illumination for one or more first portions of the one or more images;
adjusting the beam configuration to increase (via “increasing” [0023] 2nd S) illumination for one or more second portions of the one or more images; or
adjusting the beam configuration to selectively pivot one or more beam lights using one or more motors based at least on the one or more pixels.
Re 4. (Previously Presented), WANG of the combination of WANG,YI teaches The method of claim 1, wherein the determining the beam configuration is based at least on generating one or more two-dimensional mappings (or a “stereo image” “map”, [0039] 2nd S) from one or more (“sensing” [0070) locations of one or more (“combination of” [0021] 1st S) sensors used to generate the (environmental) image data to (via said sensor fusion “match” [0019] last S) one or more beam locations (via “laser” “distance” [0019] 3rd S) corresponding to the (adjusting) beam configuration.
Re 6. (Previously Presneted), WANG of the combination of WANG,YI teaches The method of claim 1, wherein the one or more (“moving” [0036] last S) states include an active state for at least one actor of the one or more actors and the beam configuration maintains or increases(“/decreasing”, WANG: [0023], 2nd S) illumination for the one or more pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”,YI: pg. 7, 6th txt blk: said “forlikehood”) based at least on the one or more pixels corresponding to (via “motion detection…comparing35 the new pixel and36 the background model”, YI: pg. 6, 6th txt blk) the active (“moving” [0036] last S) state for the at least one actor.
Claim 15 is rejected like claim 1:
Re 15. (Currently Amended), WANG of the combination of WANG,YI teaches At least one processor (fig. 1:104: “Controller”) comprising:
one or more circuits (fig. 1:110: “Communication Circuit”) to control a lighting system based at least on a beam configuration (via fig. 1:106: “Lighting system”) of the lighting system, the beam configuration being determined based at least on:
segmentation mask confidences that one or more labels apply to corresponding pixels in one or more images, the one or more labels classifying the corresponding pixels as depicting in one or more states represented by the one or more labels, the segmented mask confidences determined (i.e., said confident feature states mask filtered “for further analysis37” –i.e., for further CNN extracting—pg. 13,ll.10-15) based38 (or likewise the confidences established by some calculation or the like) at least on39 one or more (“object recognition” [0022] 5th S) neural networks (NNs) (“CNN” [0022]) processing (indicative) image (information) data representative of the one or more (indicative) images of an (information) environment; and
one or more pixels of the one or more (“series”, WANG [0036] last S) images that depict the one or more actors in the one or more states represented by the one or more labels the one or more pixels determined, based at least on temporally filtering the segmentation mask confidences over (or “filter…in”, pg. 13 [0035]) the one or more (“series”, WANG [0036] last S) images.
.
Claim 17 is rejected like claim 3:
Re 17. (Previusly Presented), WANG of the combination of WANG,YI teaches The at least one processor of claim 15, wherein the determining the beam configuration includes one or more of:
adjusting the beam configuration to reduce illumination for one or more first portions of the one or more images;
adjusting the beam configuration to increase illumination for one or more second portions of the one or more images; or
adjusting the beam configuration to selectively pivot one or more beam lights using one or more motors based at least on the one or more pixels.
Claim 18 is rejected like claim 4:
Re 18. (Previouisly Presneted), WANG of the combination of WANG,YI teaches The at least one processor of claim 15, wherein the based configuration determined, at least, by generating one or more two-dimensional mappings from one or more locations of one or more sensors used to generate the image data to one or more beam locations corresponding to the beam configuration.
Claim 20 is rejected like claim 14:
Re 20., (Previously Presented) WANG of the combination of WANG,YI teaches The at least one processor of claim 15, wherein the processor is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing light transport simulation;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of Weinzaepfel et al. (US 2020/0364509 A1) further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation:
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Re 2. (Currently Amended) , WANG of the combination of WANG,YI teaches The method of claim 1, wherein the one or more labels include one or more class40 labels (via “segmentation” “masking”41 “solving space confidence map”, YI: pg. 7, 5th txt blk, shown below as “prob_o”) of one or more segmentation masks, the one or more class labels representing that the corresponding pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”, YI: pg. 7, 6th txt blk: said “forlikehood”) 42 the one or more actors in an inactive state of the one or more states.
WANG of the combination of WANG,YI does not teach the difference of claim 2 of:
A) class (labels)..
B) class (labels)…
C) inactive (state).
Weinzaepfel teaches the difference (“A)” & “B)”) of claim 2 of
A) class43 (“class label o, i.e., the identifier of the detected object-of-interest and a confidence score” [0044] 2nd S)…
B) class (labels)…
C) inactive (state).
Since WANG of the combination of WANG,YI teaches detection, one of skill in the art of detection can make WANG’s of the combination of WANG,YI be as Weinzaepfel’s predictably recognizing the change “enabling improved detection and matching of objects-of-interest at test time with novel viewpoints”, Weinzaepfel [0093].
WANG of the combination of WANG,YI,Weinzaepfel does not teach the last difference of claim 2 of:
C) inactive (state)44.
YUREVICH teaches/makes obvious, in the below rejection of claim 5, the last difference of claim 2 of:
C) inactive (state)45.
Claim(s) 5 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation:
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Re 5. (Currently Amended), WANG of the combination of WANG,YI teaches The method of claim 1, wherein the one or more (“moving” [0036] last S) states include an inactive stationary (“moving” [0036] last S) state46 for47 at least one (“moving” [0036] last S) actor (via a “proximity sensor” [0041] 2nd S) of the one or more (“moving” [0036] last S) actors and the beam configuration maintains or increases (via “increasing” [0023] 2nd S) illumination for the one or more pixels based48 at least on49 (“the command”, WANG [0023] 2nd S) the one or more pixels corresponding50 to (via a form-of-“be”-word) the inactive stationary (“moving” [0036] last S) state51 for52 the at least one (“moving” [0036] last S) actor (via a “proximity sensor” [0041] 2nd S).
WANG of the combination of WANG,YI does not teach the difference of claim 5 of:
“inactive stationary … inactive stationary”.
YUREVICH teaches the difference of claim 5:
inactive stationary (or “pixel”-“sense”-“inactive”-“stationary” via “The problem of detecting abandoned stationary objects is…inactive in the sense of changing the brightness of pixels over time”, pg, 2, 3rd txt blk, “subject to the following conditions: - the object is not detected”, pg. 4, 5th txt blk) (state53)54 …
inactive stationary (“fixed object…density…is higher than the specified”, pg. 4, 5th txt blk) (state)55.
Since WANG of the combination of WANG,YI teaches a moving object, one of ordinary skill in the art of moving objects can make WANG’s of the combination of WANG,YI be as YUREVICH’s predictably recognizing the change “to improve the quality of detection of left objects in the video stream by reducing the number of false positives and ensuring the reliability of the analysis results”, YUREVICH, pg. 2, 4th txt blk.
Claim 19 is rejected like claim 5:
Re 19., (Currently Amended) WANG of the combination of WANG,YI teaches The at least one processor of claim 15, wherein the beam configuration is determined, at least, by determining the one or more (“moving” [0036] last S) states as corresponding to one or more inactive (“adjustment”, pg. 20: [0050]) actors and the beam configuration maintains or increases illumination (or “light intensity”, pg. 20: [0050]) for56 the one or more pixels based at least on the one or more (“moving” [0036] last S) states corresponding to the one or more inactive (“adjustment”, pg. 20: [0050]) actors.
WANG of the combination of WANG,YI does not teach the difference of claim 19 of:
“inactive …57inactive”.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of ZHANG et al. (Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes: 15 July 2020):
Re claim 7:
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference should always be the one used in rejecting the claims (claim 7). Sometimes the best reference (ZHANG et al. (Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes)) will have a publication date (15 July 2020) less than a year prior to the application filing date (8/12/2020), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (Li et al. (US 2015/0278616 A1)) exists which cannot be so overcome and which, though inferior, is an adequate basis for rejection, the claims (claim 7) should be additionally rejected thereon.
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Re 7. (Currently Amended), WANG of the combination of WANG,YI teaches The method of claim 1, comprising:
generating,58 using the 59,60 one or more dim region masks (maps to “mask filtering” [0035] last S) indicating the one or more pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”, YI: pg. 7, 6th txt blk: said “forlikehood”) for adjustment of illumination, wherein the (adjusting) beam configuration is determined using the one or more dim region masks (maps to “mask filtering” [0035] last S).
Wang does not teach the difference of claim 7 of:
--dim region (masks)61 …dim region (masks)--.
Zhang teaches the difference of claim 7:
7. (Currently Amended) The method of claim 1, comprising:
generating, using theB. FOREGROUND DETECTION, 1st S) dim region masks (fig. 7:description thereof: “night” “detection masks” and fig. 8: description thereof: “night video” “detection masks”, pg. 130374) indicating the one or more pixels for adjustment (“to adapt to scenes that change after segmenting the foreground pixels” as binary 1, pg. 130372, C. BACKGROUND UPDATES, 1st S) of illumination (given that video comprises “the turning on/off of high or low beam lights”, pg. 130375, lcol, penult para, 2nd S), wherein the (high-low) beam configuration is determined (or classified as 1 or 0) using the one or more dim (night) region (video) masks.
Since Wang suggest using a mask via “mask filtering”, [0035] last S, one of skill in the art of mask filtering can make Wang’s be as Zhang’s predictably recognizing the change detecting vehicles “completely and precisely” while resisting more noise as compared to others, Zhang, pg. 130375, lcol, 1st full S:
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Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of Li et al. (US 2015/0278616 A1):
Re claim 7:
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference should always be the one used in rejecting the claims (claim 7). Sometimes the best reference (ZHANG et al. (Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes)) will have a publication date (15 July 2020) less than a year prior to the application filing date (8/12/2020), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (Li et al. (US 2015/0278616 A1)) exists which cannot be so overcome and which, though inferior, is an adequate basis for rejection, the claims (claim 7) should be additionally rejected thereon.
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Re 7. (Currently Amended), WANG of the combination of WANG,YI teaches The method of claim 1, comprising:
generating, using the 62, one or more dim region masks (maps to “mask filtering” [0035] last S) indicating the one or more (bounding) pixels (via said prob_o: “solving…the pixel belonging to the foreground mask”, YI: pg. 7, 6th txt blk: said “forlikehood”) for adjustment of illumination, wherein the (adjusting) beam configuration is determined using the one or more dim region masks (maps to “mask filtering” [0035] last S).
Wang does not teach the difference of claim 7 of:
-- dim region (masks) …dim region (masks)--.
Li teaches the difference of claim 7:
Re 7. (Currently Amended), The method of claim 1, comprising:
generating, using the th S) dim region masks (or a “shadow”-“foreground”-“mask” [0036] 2nd S: FIG. 5: S516: “GENERATE FIRST MASK”) indicating the one or more (background shadow) pixels for (an “updated” [0052] 2nd S) adjustment (figs. 3,5:S306,S506: “UPDATE CURRENT BACKGROUND MODEL USING INCOMING FRAME”) of illumination (as shown in fig. 1), wherein the beam configuration (fig. 1:cars with beam configurations) is determined (via said headlight & shadow mask) using the one or more dim region masks (fig. 10B: mask of a headlight).
Since Wang suggest using a mask via “mask filtering”, [0035] last S, one of skill in the art of mask filtering can make Wang’s be as Li’s predictably recognizing the change detecting vehicles “with high accuracy”, Li [0051] 2nd S:
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Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation as applied in claims 1,3,4,6 and 15,17,18,20 above further in view of KAINO (DE 10 2019 104 113 A1) with SEARCH machine translation:
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Claim 8 is rejected like claim 1:
Re 8. (Currently Amended), WANG of the combination of WANG,YI teaches The method of claim 1, wherein the filtering (WANG teaches “mask filtering” but does not teach the (adjective) modifier “temporally”; however, WANG of the combination of WANG,YI teaches “temporally”) includes recursively weighting the
WANG of the combination of WANG,YI does not teach the difference of claim 8 of “recursively63 weighting”.
KAINO teaches the difference of claim 8:
recursively weighting (or “recursively” “weighting”, pg. 2, last txt blk).
Since WANG of the combination of WANG,YI teaches recognition of a traffic light, one of skill in the art of recognition can make WANG’s of the combination of WANG,YI be as KAINO’s predictably recognizing the change “to improve the recognition rate”, KAINO, pg, 17, 9th txt blk.
Claim 16 is rejected like claim 8:
Re 16. (Currently Amended), WANG of the combination of WANG,YI,KAINO teaches The at least one processor of claim 15, wherein the
filtering includes recursively weighting the .
Claim(s) 9,12,14 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2):
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018) should always be the one used in rejecting the claims (1-20). Sometimes the best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) will have a publication date (07 May 2020) less than a year prior to the application filing date (8/12/2019), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (Stein et al. (US 2007/0221822 A1)) exists which cannot be so overcome and which, though inferior (or used as a secondary reference under35 USC 103), is an adequate basis for rejection, the claims (9,10,11,12,13,14) should be additionally rejected (via the above 35 USC 102(a)(1) rejection of Stein) thereon.
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Claim 9 is rejected like claim 1:
Re 9. (Currently Amended), WANG of the combination of WANG,YI teaches A system comprising:
one or more circuits (fig. 1:110: “Communication Circuit) to perform operations including:
computingoutputs (or likewise outputs of neural networks and respective algorithms via “deep learning”64, pg. 14,ll.5-10) of one or more (“convolutional” [0022] 5th S) neural networks (NNs) (or “CNN”) 65 technique may be employed for identifying objects in a given image, such as deep learning or machine
learning algorithms”, pg. 14,ll.5-10), the outputs including segmentation masks having segmentation mask confidences that one or more segmentation labels apply to corresponding (or likewise “depth information corresponding to an object”,pg. 15,ll.5-10) pixels in the segmentation masks , the one or more segmentation labels classifying the corresponding pixels as depicting 66 [0036] last S) actors (tracked-target-object-actor) in one or more inactive or (being) active states67represented by the one or more segmentation labels;
determining, based at least on temporally filtering the segmentation mask confidences over the one or more images, that one or more pixels of a segmentation mask of the segmentation masks depict the one or more actors in the one or more inactive or active states represented by the one or more segmentation labels;
determining a beam (adjusting) configuration (via said “associated with the object according to the lighting adjustment configuration” [0022], last S) based at least on the segmentation mask; and
controlling a lighting system based at least on the beam configuration (via said “associated with the object according to the lighting adjustment configuration” [0022], last S).
WANG of the combination of WANG,YI does not teach the difference of claim 9 of:.
segmentation68 masks69 having…70segmentation (mask confidences)71…(one or more) segmentation (labels) …pixels in the segmentation masks …(the one or more) segmentation (labels)… the (corresponding) pixels … (the one or more) segmentation (labels) …
temporally (filtering the) segmentation (mask confidences) … one or more pixels …(the) segmentation (mask)…(the) segmentation masks …(the one or more) segmentation (labels) …
(the)…segmentation (mask).
Thiebaut teach the difference of claim 9 of:.
segmentation72 masks73 (or likewise “A binary mask per detection scale”, c,4,ll.60-65, wherein a detection scale “may be represented by a 4-tuple, for example x, y, width, height”, c.1,ll.20-25:fig. 2 shows two binary masks: one for each detection scale) having…74segmentation (segmentation is understood/redundant given the definition of mask: see below mask-footnote) (mask confidences)75(or likewise “mask”-creation”-“probability”76, c.5,ll. 15-20, of 1s and 0s: fig. 3:21: “Binary mask by pixel and scale (initialized at 1)”)…(one or more) segmentation (labels) …pixels in the segmentation masks (or likewise “ pixels in the area or areas of interest, by virtue of the fact that the values of the attention map are taken into account in the creation of the mask” c.5,ll.15-20)…(the one or more) segmentation (labels)… the (corresponding) pixels … (the one or more) segmentation (labels) …
temporally (filtering the) segmentation (or likewise :”temporal_fil-ter…box.. segmentation”, c.3,l.. 57-63 to col. 4,ll.12-15: fig. 2 shows two segmentation boxes to be temporally filtered: fig. 3:14: “Updating of the probability map by pixel and scale” is the temporal filter) (mask confidences) … one or more pixels (or likewise “the number or pixels”, c. 1,ll. 20-35) …(the) segmentation (mask)…(the) segmentation masks …(the one or more) segmentation (labels) …
(the)…segmentation (mask).
Since WANG of the combination of WANG,YI suggests to one of ordinary skill in the art image enhancement of other types, “etc.”, (i.e., the reason to combine references) of “mask filtering” for “collecting environmental information”, via WANG, pgs. 12,13:
[0035] In some embodiments, collecting environmental information may further include collecting initial environmental information and processing the initial environmental information to obtain the environment information. The initial environmental information may be collected by the sensing system 102. At least one image sensor may be used to capture the image of the environment of the vehicle. The image sensor may be placed at any suitable location on the vehicle and face any suitable direction from the vehicle to obtain views related to the vehicle, such as front view, rear view, side view, surround view, etc. In some embodiments, raw images taken by multiple image sensors or by one image sensor rotated at different angles may be used to generate a combined image that covers a wider angle of view than each individual raw image. In one example, a panoramic image may be produced based on the raw images. In another example, multiple image sensors may be mounted at the front, sides and rear of the vehicle to create a 360 degree “bird’s eye” full-visibility view around the vehicle. When combining the raw images, the computing system (e.g., controller 104) may adjust brightness of the raw images and geometrically align the raw images to generate the combined image. In some embodiments, settings of the multiple image sensors may be dynamically adjusted based on surrounding lighting conditions. In some embodiments, an image sensor having a wide-angle lens or an ultra-wide “fisheye” lens may be used to capture a raw image. Image processing techniques, such as barrel lens distortion correction and image plane projection, may be employed to compensate the wide-angle lens effect and produce an image with straight lines and natural view for further analysis. In some embodiments, image processing techniques may be employed to enhance or filter certain features in an image for further analysis, such as noise filtering, contrast adjustment, mask filtering, histogram equalization, etc.
said one of skill in the art of filtering can make WANG’s (fig. 4) of the combination of WANG,YI be as Thiebaut’s (fig. 3) seeing from the change, “Advantageously, in the example of the detection of pedestrians, wider regions of interest around the detected objects are defined to take account of the motion of these objects on the image, and thus ensure that, on the subsequent image, the analysis is focused preferentially on these regions.”, Thiebaut, c.8,ll. 65 by combining programs of WANG’s figure 4 with YI’s figure 1 and Thiebaut’s fig. 3:
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Claim 12 is rejected like claim 10, below:
Re 12. (Currently Amended), WANG of the combination of WANG,YI,Thiebaut teaches The system of claim 9, wherein the one or more states include an active state in which the one or more actors are in motion (or likewise “the computing system may determine whether a tracked object is moving77 and movement information of the tracked object ( e.g., moving direction, moving speed) based on the series of images”, pg. 14, 2nd S).
Re 14. (Original), WANG of the combination of WANG,YI,Thiebaut teaches The system of claim 9, wherein the system us comprised in at least one of:
a control system for an autonomous or semi-autonomous machine (with “an automatic driving system”, Wang: [0022] 2nd to last S) ;
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing light transport simulation;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of Aoba e al. (US 2019/0012790 A1) as applied to claims 9,12,14 above further in view of TAKAHITO (DE 112015000723 T) with SEARCH machine translation:
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Re 10. (Currently Amended), WANG of the combination of WANG,YI,Thiebuat teaches The system of claim 9, wherein a class label of the one or more [[class]] segmentation labels represents a confidence (or said likewise teaches filtering/determining a plurality of confident mask features, wherein the plurality of features have a confident quality, via “filter78 certain79 features in…image…mask filtering”, WANG, pg. 13,ll. 9-11), predicted by the one or more NNs , that a corresponding pixel (or likewise “the corresponding pixel of the image is analyzed in the step 10”, Thiebaut, c.9,ll. 50-55) in an image input to the one or more NNs (or likewise “a convoluted neural network (CNN) algorithm may be implemented to perform the object recognition on captured images”, WANG pg. 6,ll.10-15), depicts the one or more actors in an inactive state of the one or more states in which the one or more actors are stationary.
WANG of the combination of WANG,YI,Thiebuat does not teach the difference of claim 10 of:
a class label…represents…(a confidence), predicted by (the one or more NNs) , that… inactive …stationary.
Aoba teach the difference of claim 10 of:
a class label (or likewise “a class label ci” [0116])…represents (or likewise “a CRF model” [0115] last S)…(a confidence), predicted by (or said likewise “a CRF model80” [0115] last S) (the one or more NNs) , that… inactive …stationary.
Since WANG of the combination of WANG,YI,Thiebuat suggests other “trained classification” models “or the like” via
[0030] The at least one storage medium 202 can include a non-transitory computer-readable storage medium, such as a random-access memory (RAM), a read only memory, a flash memory, a volatile memory, a hard disk storage, or an optical medium. The at least one storage medium 202 coupled to the at least one processor 204 may be configured to store instructions and/or data. For example, the at least one storage medium 202 may be configured to store data collected by the sensing system 102 (e.g., image captured by the image sensor), trained classification model for object recognition, light adjustment configurations corresponding to different types of objects and/or operation scenarios, computer executable instructions for implementing a process of adjusting a lighting system, and/or the like.
one of skill in the art of classification can make WANG’s of the combination of WANG,YI,Thiebuat be as Aoba’s seeing in the change precise region segmentation via “obtaining a detailed81 region segmentation result by performing class determination on a pixel basis using a distribution estimation result for each region”, Aoba [0112], 2nd S.
WANG of the combination of WANG,YI,Thiebuat,Aoba does not teach the last difference of claim 10 of:
that8283… inactive …stationary.
TAKAHITO teaches the last difference of claim 10:
that8485…inactive (“located” “pedestrian standing”, pg., 9, last text blk) … stationary (or the “standing” via fig. 1:50: “VEHICLE FACING DETECTION PART”):
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Since WANG of the combination of WANG,YI,Thiebaut,Aoba teaches detecting pedestrians, one of skill in the art of pedestrian detection can make WANG’s of the combination of WANG,YI,Thiebaut,Aoba be as TAKAHITO’s predictably recognizing the change “addresses the problems…that restricts the difficulty in recognizing an obstacle such as a pedestrian in front of an own vehicle by a driver of an approaching vehicle, and confirms a situation around the own vehicle by one Driver of the own vehicle relieved, while the own vehicle stops”, TAKAHITO, pg. 2, 10th txt blk:
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Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of Koch (US 2022/0309678 A1):
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Re 11. (Currently Amended), WANG of the combination of WANG,YI,Thiebaut teaches The system of claim 9, wherein the beam configuration (via said “associated with the object according to the lighting adjustment configuration”, WANG: [0022], last S) includes generating one or more of a dim region86 mask or an illuminating mask from the segmentation mask (or said likewise “A binary mask per detection scale”, Thiebaut: c,4,ll.60-65, wherein a detection scale “may be represented by a 4-tuple, for example x, y, width, height”, Thiebaut: c.1,ll.20-25:fig. 2 shows two binary masks: one for each detection scale).
WANG of the combination of WANG,YI,Thiebaut does not teach the difference of claim 11 of:
dim region87 (mask)88…89illuminating (mask)…from (the segmentation mask).
Koch teach the difference of claim 11 of:
dim region90 (mask)91…92illuminating (mask)…from (or likewise copying a segmentation mask to assert or de-asset illumination via “For this purpose, the modulator generates a new spatial pattern mask by simply copying the segmentation mask, so that the illumination flag of each location is asserted or deasserted when the location belongs to a detection segment or to a non-detection segment, respectively.” [0057] 2nd S) (the segmentation mask).
Since WANG of the combination of WANG,YI,Theibout suggests to one of ordinary skill in the art image enhancement of other types, “etc.”, (i.e., the reason to combine references) of “mask filtering” for “collecting environmental information”, via WANG, [0035]:
In some embodiments, collecting environmental information may further include collecting initial environmental information and processing the initial environmental information to obtain the environment information. The initial environmental information may be collected by the sensing system 102. At least one image sensor may be used to capture the image of the environment of the vehicle. The image sensor may be placed at any suitable location on the vehicle and face any suitable direction from the vehicle to obtain views related to the vehicle, such as front view, rear view, side view, surround view, etc. In some embodiments, raw images taken by multiple image sensors or by one image sensor rotated at different angles may be used to generate a combined image that covers a wider angle of view than each individual raw image. In one example, a panoramic image may be produced based on the raw images. In another example, multiple image sensors may be mounted at the front, sides and rear of the vehicle to create a 360 degree “bird’s eye” full-visibility view around the vehicle. When combining the raw images, the computing system (e.g., controller 104) may adjust brightness of the raw images and geometrically align the raw images to generate the combined image. In some embodiments, settings of the multiple image sensors may be dynamically adjusted based on surrounding lighting conditions. In some embodiments, an image sensor having a wide-angle lens or an ultra-wide “fisheye” lens may be used to capture a raw image. Image processing techniques, such as barrel lens distortion correction and image plane projection, may be employed to compensate the wide-angle lens effect and produce an image with straight lines and natural view for further analysis. In some embodiments, image processing techniques may be employed to enhance or filter certain features in an image for further analysis, such as noise filtering, contrast adjustment, mask filtering, histogram equalization, etc.
said one of skill in the art of mask filtering can make WANG’s of the combination of WANG,YI,Theibaut be as Koch’s mask (of “mechanical parts, and so on” [0070]) seeing in the change an asserted/confident segmentation mask via “Each segmentation mask is formed by a matrix of cells with the same size as the… images, each storing a segmentation flag (i.e., a binary value) for the corresponding location of the body93-part; the segmentation flag is asserted94”, Koch [0047] 4th S.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over WANG (WO 2020/087352 A1: filed 31 Oct 2018) in view of YI (CN 109948474 A) with SEARCH machine translation further in view of Thiebaut et al. (US 10,867,211 B2) as applied in claims 9,12,14 further in view of JIANG et al. (CN 111402336 A: Date Published 2020-07-10: July 10, 2020) with SEARCH machine translation:
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Re 13. (Currently Amended), WANG of the combination of WANG,YI,Theibaut teaches The system of claim 9, wherein the determining the beam configuration (via said “associated with the object according to the lighting adjustment configuration” [0022], last S) is determined based at least on correspond to consistent assignments of95 the one or more [[class]] segmentation labels over a threshold quantity (or “value”, WANG: pg. 16 [0041] last S or “percentage”, WANG: pg. 17 [0043] or “distance”, WANG: pg. 24 [0063]) of frames (or likewise “an image frame extracted from a captured video”, WANG: pg. 12, 2nd S).
WANG of the combination of WANG,YI,Theibaut does not teach the difference of claim 13 of:
correspond96 to consistent assignments of97…over.
JIANG teaches the difference of claim 13:
correspond98 to consistent assignments99 (via “corresponding to…the… same100…type101”, pg. 3, 6th txt blk: fig. 2: “Movement point judgement”) of102…over:
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Since WANG of the combination of WANG,YI,Thiebaut teaches tracking, one of skill in the art of tracking can make WANG’s of the combination of WANG,YI,Theibaut be as JIANG’s predictably recognizing the change “provides…tracking…successfully”, JIANG, pg. 4, 7th txt blk.
Claim(s) 1,3,4,6 and 15,17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation:
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) should always be the one used in rejecting the claims (1,3,4,5,6,7 and 15,16,17,18,19,20). Sometimes the best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) will have a publication date (07 May 2020) less than a year prior to the application filing date (8/12/2019), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (IDS cited Stam et al.: US 2004/0143380 A1) exists which cannot be so overcome and which, though inferior, is an adequate basis for rejection, the claims (1,2,3,4,5,6,7 and 15,16,17,18,19,20) should be additionally rejected thereon:
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Re 1., Stam teaches A method (fig. 5) comprising:
determining (clearly by pointing out via fig. 12:1208: “Analyze through Headlamp Classification Network”), using one or more neural networks ([0137] 1st S) (NNs) and based at least on image data representative of one or more images (or “image kernel”, ([0137] 1st S) of an environment (comprising “a rain drop” [0145] 1st S), th S) mask103 confidences (“used to variably control the rate of change of the controlled vehicle's exterior lights, with a higher confidence causing a more rapid change”, [0095] penult S) [[of]] that104 one or more labels apply to corresponding pixels in the one or more images (or likewise “several different pixel response ranges are identified and a corresponding correction look-up-table is created” [0045] 6th S), the one or more labels classifying the corresponding pixels as depicting st S) actors (or likewise “motion of an object” [0090] 1st S) in one or more states (or likewise “redness105 that is at least as high as this threshold” [0093] 2nd S) represented by the one or more labels;
determining106, based107 at least on108 temporally filtering109 (resulting in a “prior steps”- “filtered image”, [0081] 2nd S: fig. 10: prior steps: maps to fig. 5:502: “Extract Features of Light Sources From Image”) the th S) mask confidences110 (or likewise red-filtered computed (computed is a form of being confident) ratio and red-filtered sum (sum is another form of being confident) via “a new color ratio is computed111 from the sum112…from the red filtered images”, [0132] penult S, “used to variably control the rate of change of the controlled vehicle's exterior lights, with a higher confidence causing a more rapid change”, [0095] penult S: fig. 12:1208: “Analyze through Headlamp Classification Network”) over the one or more images (“such that the kernel may be temporarily centered on every pixel within the image” [0137] 3rd S) , that113 one or more pixels (“is greater than each of its neighbors” [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?”: expressing the action of the verb (determine) or its result (or any result after fig. 10:1002), product, material, etc (or anything after fig. 10:1002)) of the one or more (“corresponding” [0040] 2nd S) images depict the one or more actors in the one or more states represented by the one or more labels ;
determining (via a “switch decision” [0125] 2nd S such that “high beams are activated” [0121] 5th S: fig. 5:505: “Select and Set Desired Forward Lighting State”) a beam configuration for (activating) a lighting system based at least on the determined one or more pixels (“is greater than each of its neighbors” [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?” : expressing the action of the verb (determine) or its result (or any result after fig. 10:1002), product, material, etc (or anything after fig. 10:1002))
controlling the lighting system (to turn ON via fig. 14:1403: an “ON STATE” configuration) based at least on the beam configuration (or the OFF configuration comprised by a “configured” “headlamp control system”, [0120] 1st S, “in the OFF STATE 1401”114 [0120], 2nd S).
Stam does not teach the difference of claim 1 of:
(segmentation)115 mask (confidences)…116 that one or more labels apply to (corresponding pixels)… the one or more labels classifying (the corresponding pixels) … the one or more labels…
the (segmentation) mask (confidences) …represented by the one or more labels.
ALCOHAMI teach the difference of claim 1 of:
(segmentation)117 mask (confidences) (or likewise “a binary mask” via: “semantic segmentation marker in the primary object detector result extraction for the soft metric of the confidence score. input to the semantic unit 212 captured image is amplified and divided by semantic network directly processing. used for other color pixel of the activated row of the human colour and background to generate a binary mask.”, pg. 19, 1st txt blk: fig. 6B, reproduced below)…118 that one or more labels (understood given mask119) apply to (image of fig. 6B) (corresponding pixels)… the one or more labels classifying (understood given mask) (the corresponding pixels) … the one or more labels…
the (segmentation) mask (confidences) … (“pedestrian, vehicle or building”, pg. 11, 2nd txt blk) represented by the one or more labels (or likewise “semantic segmentation labelling”, pg. 11, 2nd txt blk).
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Since Stam suggesting picking a point of confidence other than using a percentage (i.e. the reason to combine references):
[0095] The output of the classification network may be either a Boolean, true-false, value indicative of a tail lamp or not a tail lamp or may be a substantially continuous function indicative of the probability of the object being a tail lamp. The same is applicable with regard to headlamps. Substantially continuous output functions are advantageous because they give a measure of confidence that the detected object fits the pattern associated with the properties and behavior of a head lamp or tail lamp. This probability, or confidence measure may be used to variably control the rate of change of the controlled vehicle's exterior lights, with a higher confidence causing a more rapid change. With regard to a two state exterior light, a probability, or confidence, measure threshold other than 0% and 100% may be used to initiate automatic control activity.
one of skill in the art of confidences can make Stam’s (fig. 12) be as ALCOHAMI’s (fig. 4) seeing in the change “a primary object detector 210 include a semantic segmentation marker, and secondary object detector 212 can help improve the primary object detector semantic segmentation in marker 210 identifying multiple instances of the same class.”, ALCOHAMI, pg. 21, 5th txt blk:
A) by installing ALCOHAMI’s program of fig. 4 into STAM’s computer;
B) execute STAM’s program of STAM’s fig. 12:
when reach STAM’s fig. 12:1208: “Analyze through Headlamp Classification Network call ALCOHAMI’s installed program of fig. 4;
C) output the result of ALCOHAMI’s program to STAM’s fig. 12:1209: “Is this the Last Light?”; and
D) expect an improved primary object detection from STAM’s fig. 12 because of the modification of STAM’s fig. 12:
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The illustrated combination of Stam,ALCOHAMI:
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Re 3. (Previously Presented), Stam of the combination (illustrated above) of Stam,ALCOHAMI teaches The method of claim 1, wherein the determining (or setting or deciding) the beam configuration includes one or more of:
adjusting (via “vehicle headlamp control”, Stam: [0034] last S) the (high) beam configuration to reduce (via fig. 15: “Level” going down) illumination for one or more first (kernel-pixel) portions of the one or more images;
adjusting the beam configuration to increase illumination for one or more second portions of the one or more images; or
adjusting the beam configuration to selectively pivot one or more beam lights using one or more motors based at least on the one or more pixels.
Re 4. (Previously Presented), Stam of the combination of Stam, ALCOHAMI teaches The method of claim 1, wherein the determining the beam configurations is based at least on generating one or more two-dimensional mappings (or classification “probability functions”, Stam: [0094], 1st S) from one or more (pixel) locations of one or more sensors used to generate the image data to one or more beam locations (via “headlamps” [0033] of fig. 1:101 being in the front location) corresponding to the (high) beam configuration.
Re 6. (Previously Presented), Stam of the combination of Stam,ALCOHAMI teaches The method of claim 1, wherein the one or more (“oncoming”, Stam: [0098] 2nd S) states include an active (“oncoming”) state for at least one actor of the one or more (car) actors and the beam configuration (or the OFF configuration comprised by a “configured” “headlamp control system”, [0120] 1st S, “in the OFF STATE 1401”120 [0120], 2nd S) maintains or increases (via a “lamp” “TRANSITION STATE 1402” [0121]: fig. 14: fig. 12:1211: “Determine Appropriate Lighting Setting Based Upon Most Significant Light”) illumination for121 the one or more pixels (“is greater than each of its neighbors” [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?”) based122 at least on the one or more pixels (“is greater than each of its neighbors” [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?”) corresponding to the active (oncoming) state for the at least one (car) actor.
Claim 15 is rejected like claim 1:
Re 15. (Currently Amended), Stam of the combination of Stam, ALCOHAMI teaches At least one processor comprising:
one or more (Stam: “board” [0035] 3rd S) circuits to control[[ling]] a lighting system based at least on a (high) beam (by-levels) configuration of the lighting system, the (OFF/ON) beam configuration being determined based at least on:
mask confidences that one or more labels apply to corresponding pixels in one or more images, the one or more labels classifying the corresponding pixels as depicting (“used to variably control the rate of change of the controlled vehicle's exterior lights, with a higher confidence causing a more rapid change”, [0095] penult S: fig. 12:1208: “Analyze through Headlamp Classification Network”) in one or more states represented by the one or more labels, the segmentation mask confidences determined based at least on one or more neural networks (NNs) (comprising said kernel) processing (for classifying) image data representative of the one or more images of an environment (comprising said rain-drop or “foggy” [0149] 6th S); and
one or more pixels (“for the purpose of classifying the type of light source associated with the peak”-“pixel”, [0137] last S) of the one or more images that depict the one or more actors in the one or more states represented by the one or more labels 123 at least on temporally filtering124 (resulting in a “prior steps”- “filtered image”, [0081] 2nd S: fig. 10: prior steps: maps to fig. 5:502: “Extract Features of Light Sources From Image”: expressing the action of the verb (filter) or its result, product, material, etc.125) segmentation mask confidences (“used to variably control the rate of change of the controlled vehicle's exterior lights, with a higher confidence causing a more rapid change”, [0095] penult S: fig. 12:1208: “Analyze through Headlamp Classification Network”) over the one or more images (“such that the kernel may be temporarily centered on every pixel within the image” [0137] 3rd S).
.
Claim 17 is rejected like claim 3:
Re 17. (Previously Presented), Stam of the combination of Stam, ALCOHAMI teaches The at least one processor of claim 15, wherein the determining the beam configuration includes one or more of:
adjusting the beam configuration to reduce illumination for one or more first portions of the one or more images;
adjusting the beam configuration to increase illumination for one or more second portions of the one or more images; or
adjusting the beam configuration to selectively pivot one or more beam lights using one or more motors based at least on the one or more pixels.
Claim 18 is rejected like claim 4:
Re 18. (Previously Presented), Stam of the combination of Stam, ALCOHAMI teaches The at least one processor of claim 15, wherein the based configuration is determined, at least, by generating one or more two-dimensional mappings from one or more locations of one or more sensors used to generate the image data to one or more beam locations corresponding to the beam configuration.
Re 20. (Previously Presented), Stam of the combination of Stam, ALCOHAMI teaches The at least one processor of claim 15, wherein the processor is comprised in at least one of:
a control system (or “automatic headlamp control system” [0120]) for an autonomous or semi-autonomous machine (or a machine being independent regarding operating a vehicle exterior light via “automatic vehicle exterior light controller(s)”126127 [0185] last S);
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing light transport simulation;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of Weinzaepfel et al. (US 2020/0364509 A1) and YUREVICH (RU 2676028 C1) with SEARCH machine translation:
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Re 2. (Currently Amended), Stam of the combination of Stam, ALCOHAMI teaches The method of claim 1, further comprising:
wherein the one or more labels include one or more class labels (labels is understood given the definition of mask) of one or more segmentation masks, the one or more class labels representing that the corresponding pixels an inactive state of the one or more states.
Stam of the combination of Stam, ALCOHAMI does not teach the difference of claim 2 of:
(A) class (labels)…
(B) class (labels)…
(C) inactive (state).
Weinzaepfel teaches the difference (“(A)” & “(B)”) of claim 2 of
A) class (“class label o, i.e., the identifier of the detected object-of-interest and a confidence score” [0044] 2nd S)…
B) class (labels)…
C) inactive (state).
Since Stam teaches detection and problems ([0002]) thereof (i.e., the reasons to combine references):
[0002] It has long been desirable to provide automatic control of vehicle lighting both to improve driving safety and provide convenience for the driver. Such automatic lighting control may include automatic activation and deactivation of a controlled vehicle's high beam headlights as a function of driving conditions. This function has been widely attempted using various types of optical sensors to detect the ambient lighting conditions, the head lamps of oncoming vehicles and the tail lamps leading vehicles. Most recently, sensors utilizing an electronic image sensor have been proposed. Such systems are disclosed in commonly assigned U.S. Pat. No. 5,837,994 entitled Control system to automatically dim vehicle head lamps and U.S. Pat. No. 6,049,171 entitled Continuously variable headlamp control and commonly assigned U.S. patent application Ser. No. 09/799,310 entitled Image Processing System to control Vehicle Headlamps or other Vehicle Equipment, Ser. No. 09/528,389 entitled Improved Vehicle Lamp Control, and Ser. No. 09/800,460 entitled System for Controlling Exterior Vehicle Lights. The disclosures of each of these documents are incorporated in their entireties herein by reference. Light source detection within image sensing presents many challenges. For example, it may be difficult to discriminate between oncoming vehicle head lamps and reflections of the controlled vehicle's head lamps off of signs or other objects. Additionally, it may be difficult to detect distant tail lamps in proximity of other light sources, such as overhead street lamps for example, because these light sources may blur together in the image diluting the red color of the tail lamps.
, one of skill in the art of classification can make Stam’s of the combination of Stam, ALCOHAMI be as Weinzaepfel’s seeing in the change “the generation of more viewpoints of the objects-of-interest, and thus, enabling improved detection and matching of objects-of-interest at test time with novel viewpoints”, Weinzaepfel [0093].
Stam of The combination of Stam, ALCOHAMI,Weinzaepfel does not teach the last difference of claim 2 of:
C) inactive (state).
YUREVICH teaches the difference of claim 2:
inactive stationary (or “pixel”-“sense”-“inactive”-“stationary” via “The problem of detecting abandoned stationary objects is…inactive in the sense of changing the brightness of pixels over time”, pg, 2, 3rd txt blk, “subject to the following conditions: - the object is not detected”, pg. 4, 5th txt blk) (state128)129 …
inactive stationary (“fixed object…density…is higher than the specified”, pg. 4, 5th txt blk) (state)130.
Since Stam teaches a moving object, one of ordinary skill in the art of moving objects can make Stam’s of The combination of Stam, ALCOHAMI,Weinzaepfel be as YUREVICH’s predictably recognizing the change “to improve the quality of detection of left objects in the video stream by reducing the number of false positives and ensuring the reliability of the analysis results”, YUREVICH, pg. 2, 4th txt blk.
Claim(s) 5 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of YUREVICH (RU 2 676 028 C1) with SEARCH machine translation as applied in claim 2:
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Re 5. (Previously Presented), Stam of the combination of Stam,ALCOHAMI teaches The method of claim 1, wherein the one or more (“oncoming” [0098]/ “is applied” [0045] last S) states include an inactive stationary (“oncoming” [0098]/ “is applied” [0045] last S) state for at least one (“detected”-car “107”,[0033] 1st S) actor of the one or more actors and the (high) beam configuration maintains or increases illumination (“due to the closing distance” [0134]) for the one or more pixels based at least on the one or more pixels corresponding to the inactive stationary (“oncoming” [0098]/ “is applied” [0045] last S) state for the at least one (“detected”-car “107”,[0033] 1st S) actor.
Stam of the combination of Stam,ALCOHAMI does not teach the difference of claim 5 of:
inactive stationary (state)131 …132 inactive stationary (state)133.
YUREVICH teaches/makes obvious the difference of claim 5 in the rejection of claim 2.
Claim 19 is rejected like claim 5:
Re 19. (Previously Presented), Stam of the combination of Stam,El-Khamy teaches The at least one processor of claim 15, wherein the beam configuration is determined, at least, by determining the one or more states134 (“if they are vehicular head lamps”, Stam [0040] penult S) as corresponding to one or more inactive (“reflection”135, Stam: [0090] 9th S) actors and the beam configuration maintains or increases illumination for the one or more pixels (“is greater than each of its neighbors”, Stam: [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?”: expressing the action of the verb (determine) or its result (or any result after fig. 10:1002), product, material, etc (or anything after fig. 10:1002)) based at least on the one or more states (“if they are vehicular head lamps”, Stam [0040] penult S) corresponding to the one or more inactive (“reflection”136, Stam: [0090] 9th S) actors.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of Li et al. (US 2015/0278616 A1):
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) should always be the one used in rejecting the claims (1,3,4,5,6,7,8 and 15,16,17,18,19,20). Sometimes the best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) will have a publication date (07 May 2020) less than a year prior to the application filing date (8/12/2019), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (IDS cited Stam et al.: US 2004/0143380 A1) exists which cannot be so overcome and which, though inferior, is an adequate basis for rejection, the claims (1,2,3,4,5,6,7,8 and 15,16,17,18,19,20) should be additionally rejected thereon:
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Re 7. (Currently Amended) Stam of the combination (illustrated above in the rejection of claim 1) of Stam, ALCOHAMI teaches The method of claim 1, comprising:
generating, using the 137, one or more dim region masks indicating the one or more pixels (“is greater than each of its neighbors”, Stam: [0076] 4th S: fig. 10:1002: “Is Pixel > Neighbors?”: expressing the action of the verb (determine) or its result (or any result after fig. 10:1002), product, material, etc (or anything after fig. 10:1002)) for adjustment (i.e., control) of illumination, wherein the (high) beam configuration is determined (or set or decided) using (via the illustrated combination of Stam,ALCOHAMI) the one or more dim region (car/pedestrian instance) masks.
Stam of the combination of Stam, ALCOHAMI does not teach the difference of claim 7 of:
dim region … dim region.
Li teaches the difference of claim 7:
Re 7. (Currently Amended), The method of claim 1, comprising:
generating, using the th S) dim region (classification “to distinguish” “shadow”-“foreground “objects” [0036] 2nd S: fig. 7A,8A: shadows) masks (FIG. 5: S516: “GENERATE FIRST MASK”: figs. 7C,8C: mask) indicating the one or more pixels for (an “updated” [0052] 2nd S) adjustment (figs. 3,5:S306,S506: “UPDATE CURRENT BACKGROUND MODEL USING INCOMING FRAME”) of illumination (as shown in fig. 1), wherein the beam configuration (fig. 1:cars with beam configurations) is determined (via said headlight & shadow mask) using the one or more dim region masks (fig. 10B: mask of a headlight).
Since Stam of the combination of Stam,ALCOHAMI suggests a selection of segments, Stam: [0137]: “variety of image segments”, one of skill in the art of segmentation can make Stam’s of the combination of Stam,ALCOHAMI be as Li’s predictably recognizing the change detecting vehicles “with high accuracy”, Li [0051] 2nd S.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Stam et al. (US 2004/0143380 A1) in view of ALCOHAMI et al. (CN 107971117 A) with SEARCH machine translation as applied to claims 1,3,4,6 and 15,17,18,20 further in view of KAINO (DE 10 2019 104 113 A1) with SEARCH machine translation:
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Re 8. (Currently Amended), Stam of the combination of Stam,ALCOHAMI teaches The method of claim 1, wherein the filtering (resulting in a “prior steps”- “filtered image”, Stam: [0081] 2nd S: fig. 10: prior steps: maps to fig. 5:502: “Extract Features of Light Sources From Image”) includes recursively weighting (closest mapping to the adjective form of weight: “weighting factors”, Stam: [0138], last S; thus, Stam does not teach “recursively weighting”) the rd S).
Stam of the combination of Stam,ALCOHAMI does not teach the difference of claim 8 of “recursively weighting”.
KAINO teaches the difference of claim 8:
recursively weighting (or “recursively” “weighting”, pg. 2, last txt blk).
Since Stam of the combination of Stam,ALCOHAMI teaches recognition, one of skill in the art of recognition can make Stam’s of the combination of Stam,ALCOHAMI be as KAINO’s predictably recognizing the change “to improve the recognition rate”, KAINO, pg, 17, 9th txt blk.
Claim 16 is rejected like claim 8:
Re 16. (Currently Amended), Stam of the combination of Stam,ALCOHAMI teaches The at least one processor of claim 15, wherein the filtering includes recursively weighting the .
Claim(s) 9,12,14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2):
MPEP 904.03 Conducting the Search [R-07.2022]
The best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) should always be the one (best reference) used in rejecting the claims (1-20 such as shown in the below 35 USC 103 rejection of claims 9,12,14). Sometimes the best reference (WANG (WO 2020/087352 A1:filed 31 Oct 2018)) will have a publication date (07 May 2020) less than a year prior to the application filing date (8/12/2019), hence it will be open to being overcome under 37 CFR 1.130 or 1.131. In such circumstances, if a second reference (Stein et al. (US 2007/0221822 A1)) exists which cannot be so overcome and which, though inferior (regarding all claims 1-20), is an adequate basis for rejection (via 35 USC 102(a)(1)), the claims (9,12,14) should be additionally rejected thereon (via an additional 35 USC 102(a)(1) rejection of claims 9,12,14):
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Re 9. (Currently Amended), Stein teaches A system comprising:
one or more circuits (“as a chip or circuit” [0037] 3rd S) to perform operations including:
computing, outputs of one or more neural networks (NNs) (“classifiers” [0046] 5th S) and based at least on image data representative of one or more images (“obtained from a camera mounted on a vehicle” [0002]: fig. 4: “Dark Road Scene”) of an environment, the outputs including segmentation masks having segmentation mask confidences (“of the classification” [0043], 2nd to last S: fig. 3:313: “CLASSIFY SPOT”) that one or more segmentation labels apply to corresponding pixels in the segmentation , the one or more segmentation labels classifying the corresponding pixels as depicting one or more inactive or active (“oncoming”138 [0038] 6th S) statesrepresented by the one or more segmentation labels;
determining, based at least on temporally filtering the segmentation mask confidences over the one or more images, that one or more pixels of a segmentation mask of the segmentation masks depict the one or more actors in the one or more inactive or active states represented by the one or more segmentation labels;
determining a (“low” [0041] 1st S) beam configuration (such that “shape of each cluster are computed”139140 [0054] 9th S: fig. 3:309: FILTER/PROCESS DATA: fig. 3:313: “CLASSIFY SPOT”: fig. 3:23: “CONTROL HEADLIGHT ACTIVATE/DEATIVATE HIGH BEAMS”) based141 at least on142 the segmentation mask; and
controlling a lighting system (fig. 3:23: “CONTROL HEADLIGHT ACTIVATE/ DEATIVATE HIGH BEAMS”) based at least on the (filtered) beam (shape) configuration.
Stein does not teach the difference of claim 9 of:
outputs (of one or more neural networks (NNs))143…144the outputs including segmentation masks having segmentation mask (confidences) … that one or more segmentation labels apply to corresponding pixels in the segmentation , the one or more segmentation labels (classifying the) corresponding (pixels)… the one or more segmentation labels…
based at least on temporally filtering the segmentation mask (confidences) … a segmentation mask of the segmentation masks… the one or more segmentation labels…
the…segmentation mask.
Ge teach the difference of claim 9 of:
outputs (or likewise “The combined output” [0051] 2nd to last S: fig. 3A: ) (of one or more neural networks (NNs))145…146the outputs including segmentation masks having … segmentation mask (or likewise “Each instance segmentation mask included in the first set of instance segmentation masks may include one or more sets of pixel-wise labels for the first image.” [0079] penult S) (confidences) … that one or more segmentation labels apply to corresponding pixels (or likewise “each pixel that contributes to a corresponding object” [0030] 2nd S) in the segmentation , the one or more segmentation labels (classifying the) corresponding (pixels)… the one or more segmentation labels…
based at least on temporally filtering the segmentation mask (confidences) … a segmentation mask of the segmentation masks… the one or more segmentation labels…
the…segmentation mask.
Since Stein teaches training a classifier, one of skill in the art of training classifiers can make Stein’s “confidence of the classification (step 313)”, [0043] 2nd to last S, be as Ge’s “object score (e.g., a likelihood and/or confidence score) for each object”, [0031] last S, segmentation proposal mask—i.e., be as Ge’s segmentation proposal mask confidence score--predictably recognizing the change “improves the training” (Ge [0064] 3rd S) of classifiers:
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The combination of Stein,Ge does not teach the last difference of claim 9 of:
based at least on temporally filtering (the segmentation mask confidences)147.
Thiebaut teach the difference of claim 9 of:
based at least on temporally filtering (or likewise “attention map…given by…temporal_fil-ter”, c. 3,ll. 55-65) (the segmentation mask confidences)148.
Since Stein of the combination of Stein,Ge suggests different detection methods (i.e., the reason to combine references) of a scene:
[0044] Step 301: Dark Scene Detection
[0045] A lit scene can be detected by measuring the ambient light. One method useful for detecting the brightness, simply counts the number of pixels in image frames 15 that are above a previously defined brightness threshold. If the number of pixels above the threshold is larger than a previously defined threshold number, the scene is classified as bright and the headlight beams are switched (step 23) to low.
[0046] Another method for lit scene/dark scene detection is to tessellate one or more image frames 15 into patches and classify the ambient light based on histograms of pixel values in each patch. In one implementation, the subsampled 160.times.120 size image was tessellated into 5.times.5 rectangles of 32.times.24 pixels each. A 16 bin histogram was created for each bin, Thus, 5.times.5.times.16=400 values were created for each image. A classifier was then trained on example images of dark and lit scenes. Neural networks and homogeneous kernel classifiers were both used and gave good results. The second method also works well for nearby cars (typically a distance under 20 m) which are lit by headlights of vehicle 18.
one of skill in the art can make Stein’s of the combination of Stein,Ge be as Thiebaut’s seeing in the change “greater detection quality “, ThiebautL c. 2, ll. 65, by making the neural network (i.e., Ge’s fig. 1: “Multi-Label Classification Module”) of the combination of Stein,Ge be as Theibaut’s fig. 3:10: “Detection”:
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Re 12. (Currently Amended), Stein of the combination (illustrated above) of Stein,Ge,Theibaut teaches The system of claim 9, wherein the one or more states include an active state in which the one or more actors are in motion (or likewise “the spots are classified as coming from oncoming149 vehicle headlights”, Stein [0036] last S, wherein the oncoming headlights is understood to mean headlights moving toward a place).
Re 14. (Original), Stein of the combination (illustrated above) of Stein,Ge, Theibaut teaches The system of claim 9, wherein the system us comprised in at least one of:
a control system (via “vehicle control systems” Stein: [0036] 4th S) for an autonomous or semi-autonomous machine (or a machine being independent regarding operating a vehicle headlight via “automatic vehicle headlight control”150151 [0003], last S);
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing light transport simulation;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of TAKAHITO (DE 112015000723 T) with SEARCH machine translation:
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Re 10. (Currently Amended), Stein as combined (illustrated above) with Ge,Theibaut teaches The system of claim 9, wherein 152 the predicted image-level class labels”, Ge: [0056] 1st S: fig. 2B:206:220: “Images Labels”-“Multi-Label Classification Module”: Labels153- Multi-Label: Classes-Multi-Label:Class-Multi-Label: Class-Labels) of the one or more [[class]] segmentation (multi-)labels represents154 a confidence (or likewise a “A probability155…may be generated by applying a…function to” “Image-level multi-label classification results (e.g., image-level labels)”, Ge: pg. 8, lcol, 2nd S & 5th S), predicted by the one or more NNs (or likewise a label-class NN prediction via “a model156 … via one or more NNs...may implement a multi-label classification (MLC) model.” Ge [0004] 9th S), that a corresponding pixel in an image (or likewise said “each pixel that contributes to a corresponding object” [0030] 2nd S) input to the one or more NNs, depicts the one or more (“oncoming”157, Stein: [0038] 6th S) actors in an inactive state of the one or more states in which the one or more (“oncoming”158, Stein: [0038] 6th S) actors are stationary.
Stein of the combination (illustrated above) of Stein,Ge,Thiebaut does not teach the last difference of claim 10 of:
B) an inactive state in which … stationary.
TAKAHITO teaches the last difference of claim 10:
--(output159 {“a signal”, pg. 5, 6th txt blk, via fig. 1:30: “VEHICLE SPEED SENSOR”} data)160…161in an inactive (“located” “pedestrian standing”, pg., 9, last text blk) state in which … stationary (or the “standing” via fig. 1:50: “VEHICLE FACING DETECTION PART”)--162:
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Since Stein of the combination (illustrated above) of Stein,Ge,Thiebaut teaches detecting pedestrians (Stein: “pedestrian detection”-“applications”-“sensor” [0011]), one of skill in the art of pedestrian detection can make Stein’s of the combination (illustrated above) of Stein,Ge,Thiebaut be as TAKAHITO’s predictably recognizing the change “addresses the problems…that restricts the difficulty in recognizing an obstacle such as a pedestrian in front of an own vehicle by a driver of an approaching vehicle, and confirms a situation around the own vehicle by one Driver of the own vehicle relieved, while the own vehicle stops”, TAKAHITO, pg. 2, 10th txt blk.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of Koch (US 2022/0309678 A1):
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Re 11. (Currently Amended), Stein of the combination (illustrated above) of Stein,Ge,Theibaut teaches The system of claim 9, wherein the determining the (filtered) beam (shape) configuration includes generating one or more of a dim region mask or an illuminating mask from the segmentation mask (via said likewise “Each instance segmentation mask included in the first set of instance segmentation masks may include one or more sets of pixel-wise labels for the first image.” Ge [0079] penult S).
Stein of the combination (illustrated above) of Stein,Ge,Theibaut does not teach the difference of claim 11 of:
dim region…illuminating.
Koch teach the difference of claim 11 of:
dim region (mask) …illuminating (mask)…from (or likewise copying a segmentation mask to assert or de-asset illumination via “For this purpose, the modulator generates a new spatial pattern mask by simply copying the segmentation mask, so that the illumination flag of each location is asserted or deasserted when the location belongs to a detection segment or to a non-detection segment, respectively.” [0057] 2nd S) (the segmentation mask).
Since Ge of the combination of Stein,Ge,Theibaut teaches other possible masks such as a pixel label mask via:
[0024] As used herein, the term “object proposal,” may refer to a data element that indicates at least an approximation location, within an image, of which pixels contribute to the depiction of a classified object within the image. An object proposal may include an “object bounding box,” or simply a “bounding box,” which is a structure, whose boundaries separates pixels who may contribute to the visualization of an object from pixels that are not believed to contribute to the object. An object proposal may include a weight for each bounding box, where the weight indicates a confidence level in the bounding box. An object proposal may include a, instance segmentation mask, which masks the pixels that are believed to contribute to the visualization of the object. An instance segmentation mask may include a set of pixel-wise labels for the image.
one of skill in the art of masks can make Ge’s of the combination of Stein,Ge,Theibaut be as Koch’s (machine parts and so on [0070]) seeing in the change an asserted/confident segmentation mask via “Each segmentation mask is formed by a matrix of cells with the same size as the … images, each storing a segmentation flag (i.e., a binary value) for the corresponding location of the body163-part; the segmentation flag is asserted164”, Koch [0047] 4th S.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stein et al. (US 2007/0221822 A1) in view of Ge et al. (US 2021/0027098 A1) and Thiebaut et al. (US 10.867,211 B2) as applied in claims 9,12,14 further in view of JIANG et al. (CN 111402336 A: Date Published 2020-07-10: July 10, 2020) with SEARCH machine translation:
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Re 13. (Currently Amended), Stein of the combination of Stein,Ge,Theibaut teaches The system of claim 9, wherein the (filtered) beam (shape) configuration is determined (resulting in a “computed” “cluster”, Stein: [0054] 8th S, “which is approximately radially symmetric with a bright point in the center” [0054] 2nd S) based at least on165 correspond to consistent assignments of the one or more [[class]] segmentation labels over a (“previously defined”, Stein: [0045], last S) threshold quantity of (“image”) frames (“15”, Stein: [0007] penult S:fig. 2:15).
Stein of the combination of Stein,Ge,Theibaut does not teach the difference of claim 13 of:
“correspond to consistent assignments of”.
JIANG teaches the difference of claim 13:
correspond166 to consistent assignments167 (via “corresponding to…the… same168…type169”, pg. 3, 6th txt blk: fig. 2: “Movement point judgement”) of170…over…of (“current…and previous”, pg. 3, 2nd txt blk) frames (“for feature matching”: fig. 2: “Input the current frame and the previous frame image.”):
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Since Stein of the combination of Stein,Ge,Thiebaut teaches tracking (“The motion of the spot is tracked (in image space)”, Stein: [0016] 13th S), one of skill in the art of tracking can make Stein’s of the combination of Stein,Ge,Thiebaut be as JIANG’s predictably recognizing the change “provides…tracking…successfully”, JIANG, pg. 4, 7th txt blk.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action”:
Citation
Relevance
Gavrilyuk et al. (Actor and Action Video Segmentation from a Sentence)
Gavrilyuk teaches confidently multiplying “a binary ground truth171 segmentation mask Y” with a temporal (“timestep t” in eqn (2)) filter response “S”:
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as the closest to the claimed “temporally filtering the… segmentation mask confidences” of claim 1.
Zhang et al. (Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection)
Zhang teaches in 2 Related Work that “temporal filter responses” (corresponding to Gavrilyuk’s temporal timestep filter response “S”) can be used to segment a lane with problems thereof and teaches multiplying a confidence ground-truth mask “g” (i.e., an image of a road color-coded or marked/labeled as red) with a probability confidence mask “y” (i.e., an image of a road color coded or marked/labeled green):
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as the closest to the claimed “temporally filtering the… segmentation mask confidences” of claim 1.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 mask: mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label (Dictionary.com)
2 mask: mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label , wherein specific is defined: specified, precise, or particular, wherein particular is defined: exceptionally selective, attentive, or exacting; fastidious; fussy. (Dictionary.com)
3 filter: A computer software program that selectively screens out incoming information. (Dictionary.com)
4 account VERB (USED WITHOUT OBJECT) to give an explanation (usually followed by for).
to account for the accident. (Dictioanry.com)
5 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
6 What is “publication date”? See MPEP: 2154.01 Prior Art Under AIA 35 U.S.C. 102(a)(2) "U.S. Patent Documents" [R-11.2013]
AIA 35 U.S.C. 102(a)(2) sets forth three types of patent documents that are available as prior art as of the date they were effectively filed with respect to the subject matter relied upon in the document if they name another inventor: (1) U.S. patents; (2) U.S. patent application publications; and (3) certain WIPO published applications. These documents are referred to collectively as "U.S. patent documents." These documents may have different prior art effects under pre-AIA 35 U.S.C. 102(e) than under AIA 35 U.S.C. 102(a)(2). Note that a U.S. patent document may also be prior art under AIA 35 U.S.C. 102(a)(1) if its issue or publication date is before the effective filing date of the claimed invention in question.
If the issue date of the U.S. patent or publication date of the U.S. patent application publication or WIPO published application is not before the effective filing date of the claimed invention, it may be applicable as prior art under AIA 35 U.S.C. 102(a)(2) if it was "effectively filed" before the effective filing date of the claimed invention in question with respect to the subject matter relied upon to reject the claim. MPEP § 2152.01 discusses the "effective filing date" of a claimed invention. AIA 35 U.S.C. 102(d) sets forth the criteria to determine when subject matter described in a U.S. patent document was "effectively filed" for purposes of AIA 35 U.S.C. 102(a)(2).
7 “effective filing date”?
8 BROAD CLAIM LANGUAGE: “ing” (of “comprising” or any “-ing” word in the claim set): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
9 mask: mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label (Dictionary.com)
10 confidence (i.e. state of being confident): certitude; assurance, wherein certitude is defined: freedom from doubt, especially in matters of faith or opinion; certainty, wherein certainty is defined: the state of being certain, wherein certain is defined: free from doubt or reservation; confident; sure. (Dictionary.com)
11 filter: A computer software program that selectively screens out incoming information, wherein screen is defined: to test or check (an individual or group) so as to determine suitability for a task, etc (Dictionary.com: CULTURAL)
12 certain: free from doubt or reservation; confident; sure. (Dictionary.com)
13 “that” is a pronoun referring to “mask”
14 pedestrian: a person travelling on foot; walker, wherein -er (of walker) is defined: a termination of nouns denoting action or process: dinner; rejoinder; remainder; trover , wherein action is defined: the gestures or deportment of an actor or speaker. (Dictionary.com)
15 may: (used to express possibility), wherein possibility is defined: the state or fact of being possible. (Dictionary.com)
16 -ing (of determining): a suffix of nouns formed from verbs (determine), expressing the action of the verb (determine) or its result, product (“the determined one or more pixels”), material, etc. (the art of building; a new building; cotton wadding ). It is also used to form nouns from words other than verbs (offing; shirting ). Verbal nouns ending in -ing are often used attributively (the printing trade ) and in forming compounds (drinking song ). In some compounds (sewing machine ), the first element might reasonably be regarded as the participial adjective, -ing2, the compound thus meaning “a machine that sews,” but it is commonly taken as a verbal noun, the compound being explained as “a machine for sewing.” (Dictionary.com)
17 Re “temporally”: Applicant’s Disclosure:[00219]… However, the description itself is not intended to limit the scope of this disclosure. …
18 DISCLOSURE/CLAIM SCOPE: “temporally” (ADJECTIVE: not adverb) is a modifier of “filtering”: temporal- (of “temporally”): of or relating to time: -ly (of “temporally”: an adjective suffix meaning “-like”: saintly; cowardly, wherein SCOPE is defined: Linguistics, Logic. the range of words or elements of an expression over which a modifier (e.g., one of ordinary skill in the art) or operator (e.g., patent examiner) has control. (Dictionary.com)
19 confidence (i.e. state of being confident): certitude; assurance, wherein certitude is defined: freedom from doubt, especially in matters of faith or opinion; certainty, wherein certainty is defined: the state of being certain, wherein certain is defined: free from doubt or reservation; confident; sure. (Dictionary.com)
20 certain: free from doubt or reservation; confident; sure. (Dictionary.com)
21 feature: a prominent or conspicuous part or characteristic, wherein prominent is defined: standing out so as to be seen easily; particularly noticeable; conspicuous, wherein standing is defined: the act of a person or thing that stands, wherein stands is defined: to be set, placed, fixed, located, or situated, wherein set is defined: to put into some condition, wherein condition is defined: a particular mode of being of a person or thing; existing state; situation with respect to circumstances. (Dictionary.com)
22 (italics) represent claim limitations already taught
23 ellipses (…) represent claim limitations already taught
24 Re “temporally”: Applicant’s Disclosure:[00219]… However, the description itself is not intended to limit the scope of this disclosure. …
25 DISCLOSURE/CLAIM SCOPE: “temporally” (ADJECTIVE: not adverb) is a modifier of “filtering”: temporal- (of “temporally”): of or relating to time: -ly (of “temporally”: an adjective suffix meaning “-like”: saintly; cowardly, wherein SCOPE is defined: Linguistics, Logic. the range of words or elements of an expression over which a modifier (e.g., one of ordinary skill in the art) or operator (e.g., patent examiner) has control. (Dictionary.com)
26 (italics) represent claim limitations already taught
27 ellipses (…) represent claim limitations already taught
28 to: (used for expressing addition or accompaniment) with. wherein with is defined: in correspondence, comparison, or proportion to. (Dictionary.com)
29 Re “temporally”: Applicant’s Disclosure:[00219]…. However, the description itself is not intended to limit the scope of this disclosure. …
30 DISCLOSURE/CLAIM SCOPE: “temporally” (ADJECTIVE: not adverb) is a modifier of “filtering”: temporal- (of “temporally”): of or relating to time: -ly (of “temporally”: an adjective suffix meaning “-like”: saintly; cowardly, wherein SCOPE is defined: Linguistics, Logic. the range of words or elements of an expression over which a modifier (e.g., one of ordinary skill in the art) or operator (e.g., patent examiner) has control. (Dictionary.com): i.e., time-like filtering
31 prior: preceding in time or in order; earlier or former; previous, wherein earlier is defined: in or during the first part of a period of time, a course of action, a series of events, etc.. (Dictioanry.com)
32 probability: Statistics. the relative frequency with which an event occurs or is likely to occur, wherein frequency is defined: Statistics. the number of items occurring in a given category, wherein category is defined: any general or comprehensive division; a class. (Dictionary.com)
33 filter: Computers. an algorithm that categorizes, sorts, prioritizes, or blocks data through rule-based protocols.(Dictionary.com)
34 likelihood: the condition of being likely or probable; probability, wherein probability is defined: statistics a measure or estimate of the degree of confidence one may have in the occurrence of an event, measured on a scale from zero (impossibility) to one (certainty). (Dictionary.com)
35 comparing: to consider or describe as similar; liken, wherein liken is defined: to represent as similar or like; compare, wherein like is defined: corresponding or agreeing in general or in some noticeable respect; similar; analogous. (Dictionary.com)
36 and: (used to connect grammatically coordinate words, phrases, or clauses) along or together with; as well as; in addition to; besides; also; moreover, wherein with is defined: in correspondence, comparison, or proportion to. (Dictionary.com)
37 analysis: the separating of any material or abstract entity into its constituent elements (opposed to synthesis), wherein separating is defined: to take by parting or dividing; extract (usually followed by from or out ).
38 BROAD CLAIM LANGUAGE (i.e., “etc.” & “like”): based: the simple past tense and past participle (“based” is participating in the action(s) of “the segmented mask confidences determined”) of base, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
39 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com)
40 “class” is a modifier of “labels”
41 mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: Also called binary digit. a single, basic unit of digital information that is represented by one of two values, such as 1 or 0, True or False, or Yes or No, wherein represent is defined: to present in words; set forth; describe; state, wherein describe is defined: to pronounce, as by a designating term, phrase, or the like; label. (Dictionary.com)
42 depict: to represent by or as if by painting or other visual image; portray; delineate. (Dictionary.com)
43 “class” is a modifier of “labels”
44 (italics) represent claim limitations already taught
45 (italics) represent claim limitations already taught
46 state: the condition of a person or thing, as with respect to circumstances or attributes, wherein condition is defined: a particular mode of [being] of a person or thing; existing state; situation with respect to circumstances, wherein mode is defined: Grammar. mood, wherein mood is defined: Grammar.
A) a set of categories for which the verb [being] is inflected in many languages, and that is typically used to indicate the syntactic relation of the clause in which the verb occurs to other clauses in the sentence, or the attitude of the speaker toward what they are saying, such as certainty or uncertainty, wish or command, emphasis or hesitancy.
B) a set of syntactic devices in some languages that is similar to this set in function or meaning, involving the use of auxiliary words, such as can, may, might.
C) any of the categories of these sets.
47 for: intended to belong to, or be used in connection with (Dictionary.com)
48 “based” is a past participle participating with the action of “maintains or increases”
49 on: in connection, association, or cooperation with; as a part or element of (Dictionary.com)
50 corresponding: associated in a working or other relationship (Dictionary.com)
51 state: Grammar. a set of categories for which the verb [being] is inflected (Dictionary.com)
52 for: intended to belong to, or be used in connection with (Dictionary.com)
53 state: the condition of a person or thing, as with respect to circumstances or attributes, wherein condition is defined: a particular mode of [being] of a person or thing; existing state; situation with respect to circumstances, wherein mode is defined: Grammar. mood, wherein mood is defined: Grammar.
A) a set of categories for which the verb [being] is inflected in many languages, and that is typically used to indicate the syntactic relation of the clause in which the verb occurs to other clauses in the sentence, or the attitude of the speaker toward what they are saying, such as certainty or uncertainty, wish or command, emphasis or hesitancy.
B) a set of syntactic devices in some languages that is similar to this set in function or meaning, involving the use of auxiliary words, such as can, may, might.
C) any of the categories of these sets.
54 (Italics) represent limitations already taught above
55 (Italics) represent limitations (“state”) already taught above
56 For: intended to belong to, or be used in connection with. (Dictionary.com)
57 Ellipses (…) represent claim limitations already taught
58 comma: the punctuation mark(,) indicating a slight pause in the spoken sentence and used where there is a listing of items or to separate a nonrestrictive clause or phrase (“using the one or more segmentation mask[[s]] confidences”) from a main clause (claim 7) (Dictionary.com)
59 The phrase “using the one or more segmentation mask[[s]] confidences” does not limit/restrict claim 7
60 comma: the punctuation mark(,) indicating a slight pause in the spoken sentence and used where there is a listing of items or to separate a nonrestrictive clause or phrase (“using the one or more segmentation mask[[s]] confidences”) from a main clause (claim 7) (Dictionary.com)
61 (italics) represent claim limitations already taught
62 As discussed in another (above) rejection of claim 7, The surrounding-comma phrase “using the one or more segmentation mask[[s]] confidences” does not limit/restrict claim 7
63 Re “recursively”: Applicant’s Disclosure:[00219]As used herein, a recitation of “and/or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and/or element C” may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, “at least one of element A or element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, “at least one of element A and element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.
64 deep learning: Computers. an advanced type of machine learning that uses multilayered neural networks to establish nested hierarchical models for data processing and analysis, as in image recognition or natural language processing, with the goal of self-directed information processing., wherein neural networks is defined: Also called neural net. Computers. a hardware or software system in which weighted connections between data nodes are refined to produce increasingly accurate results in information processing, as in pattern recognition or problem solving, with the goal of algorithmic computing that requires minimal human intervention, wherein information is defined: Computers.
important or useful facts obtained as output from a computer by means of processing input data with a program, wherein algorithmic is defined: Derived word form of algorithm, wherein algorithm is defined: Computers. an ordered set of instructions recursively applied to transform data input into processed data output, such as a mathematical solution, search engine result, descriptive statistics, or predictive text suggestions. (Dictionary.com)
65 computer vision Digital Technology. a robot analogue of human vision in which information about the environment is received by one or more video cameras and processed by computer: used in navigation by robots, in the control of automated production lines, etc., wherein information is defined: Computers.
important or useful facts obtained as output from a computer by means of processing input data with a program. (Dictionary.com)
66 -ing (of “moving”): a suffix of nouns formed from verbs (move), expressing the action of the verb (move) or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active, wherein acting is defined: to perform as an actor. (Dictionary.com)
67 “states” is/are:(1) a “be” word or (2) a condition
68 segmentation (i.e., division into portion): division into segments, wherein segment is defined: one of the parts into which something naturally separates or is divided; a division, portion, or section.. (Dictionary.com)
69 mask (divide into portion): computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label, wherein isolate is defined: to set or place apart; detach or separate so as to be alone, wherein separate is defined: to sort, part, divide, or disperse (an assemblage, mass, compound, etc.), as into individual units, components, or elements, wherein unit is defined: one of the individuals or groups that together constitute a whole; one of the parts or elements into which a whole may be divided or analyzed ,where parts is defined: a portion or division of a whole that is separate or distinct; piece, fragment, fraction, or section; constituent. . .
70 ellipses (…) represent claim limitations already taught
71 (italics) represent claim limitations already taught
72 segmentation (i.e., division into portion): division into segments, wherein segment is defined: one of the parts into which something naturally separates or is divided; a division, portion, or section.. (Dictionary.com)
73 mask (divide into portion): computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label, wherein isolate is defined: to set or place apart; detach or separate so as to be alone, wherein separate is defined: to sort, part, divide, or disperse (an assemblage, mass, compound, etc.), as into individual units, components, or elements, wherein unit is defined: one of the individuals or groups that together constitute a whole; one of the parts or elements into which a whole may be divided or analyzed ,where parts is defined: a portion or division of a whole that is separate or distinct; piece, fragment, fraction, or section; constituent. . .
74 ellipses (…) represent claim limitations already taught
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76 probability: statistics a measure or estimate of the degree of confidence one may have in the occurrence of an event, measured on a scale from zero (impossibility) to one (certainty). (Dictionary.com)
77 -ing (of “moving”): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active.
78 filter: A computer software program that selectively screens out incoming information, wherein screen is defined: to test or check (an individual or group) so as to determine suitability for a task, etc (Dictionary.com: CULTURAL)
79 certain: free from doubt or reservation; confident; sure. (Dictionary.com)
80 model: a simplified representation or description of a system or complex entity, esp one designed to facilitate calculations and predictions (Dictionary.com)
81 detailed: having many details, wherein details is defined: particulars collectively; minutiae, wherein minutiae is defined: precise details; small or trifling matters. (Dictionaryc.om)
82 that: used as a function word to introduce a subordinate clause that is joined as complement to a noun or adjective, wherein function word is defined: a word (such as a preposition, auxiliary verb, or conjunction) that expresses primarily a grammatical relationship (Merriam-Webster.com)
83 that: (used to introduce a subordinate clause (claim 10: “a corresponding pixel in an image input to the one or more NNs, depicts the one or more actors in an inactive state of the one or more states in which the one or more actors are stationary”) as the subject or object of the principal verb or as the necessary complement to a statement made (claim 10: “a class label of the one or more [[class]] segmentation labels represents
84 that: used as a function word to introduce a subordinate clause (claim 10: “a corresponding pixel in an image input to the one or more NNs, depicts the one or more actors in an inactive state of the one or more states in which the one or more actors are stationary”) that is joined ( “joined’ is suggestive of 35 USC 103) as complement to a noun (or claim 10’s “a confidence”) or adjective, wherein function word is defined: a word (such as a preposition, auxiliary verb, or conjunction) that expresses primarily a grammatical relationship (Merriam-Webster.com)
85 that: (used to introduce a subordinate clause (claim 10: “a corresponding pixel in an image input to the one or more NNs, depicts the one or more actors in an inactive state of the one or more states in which the one or more actors are stationary”) as the subject or object of the principal verb or as the necessary complement to a statement made (claim 10: “a class label of the one or more [[class]] segmentation labels represents a confidence”) , or a clause expressing cause or reason, purpose or aim, result or consequence, etc.). (Dictionary.com)
86 “dim region” is a cumulative adjective
87 “dim region” is a cumulative adjective
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90 “dim region” is a cumulative adjective
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93 body: the section of a vehicle, usually in the shape of a box, cylindrical container, or platform, in or on which passengers or the load is carried. (Dictionary.com)
94 assesrt: to state with assurance, confidence, or force; state strongly or positively; affirm; aver. (Dictionary.com)
95 of: (used to indicate apposition or identity), wherein apposition is defined: Grammar. a syntactic relation between expressions (“consistent assignments of the one or more class labels”), usually consecutive, that have the same function and the same relation to other elements (“the one or more”) in the sentence, the second expression identifying or supplementing the first. In Washington, our first president, the phrase our first president is in apposition with Washington. (Dictionary.com)
96 correspond: to be similar or analogous; be equivalent in function, position, amount, etc. (usually followed byto ). (Dictionary.com)
97 of: (used to indicate apposition or identity), wherein apposition is defined: Grammar. a syntactic relation between expressions (“consistent assignments of the one or more class labels”), usually consecutive, that have the same function and the same relation to other elements (“the one or more”) in the sentence, the second expression identifying or supplementing the first. In Washington, our first president, the phrase our first president is in apposition with Washington. (Dictionary.com)
98 correspond: to be similar or analogous; be equivalent in function, position, amount, etc. (usually followed byto ). (Dictionary.com)
99 assignments: an act of assigning; appointment (Dictionary.com)
100 same: agreeing in kind, amount, etc.; corresponding, wherein agreeing is defined: to be consistent; harmonize (usually followed bywith ). (Dictionary.com)
101 type: a number of things or persons sharing a particular characteristic, or set of characteristics, that causes them to be regarded as a group, more or less precisely defined or designated; class; category, wherein designated is defined: to nominate or select for a duty, office, purpose, etc.; appoint; assign. (Dictionary.com)
102 of: (used to indicate apposition or identity), wherein apposition is defined: Grammar. a syntactic relation between expressions (“consistent assignments of the one or more class labels”), usually consecutive, that have the same function and the same relation to other elements (“the one or more”) in the sentence, the second expression identifying or supplementing the first. In Washington, our first president, the phrase our first president is in apposition with Washington. (Dictionary.com)
103 mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label (Dictionary.com)
104 “that” is a pronoun for “mask”
105 -ness (of “redness”): a native English suffix attached to adjectives and participles, forming abstract nouns denoting quality and state (and often, by extension, something exemplifying a quality or state). (Dictionary.com)
106 -ing of (“determining”): a suffix of nouns formed from verbs (determine), expressing the action of the verb (determine) or its result, product, material, etc. (the art of building; a new building; cotton wadding ).(Dictionary.com)
107 based: to have a basis; be based (usually followed by on or upon), wherein based (VERB (USED WITH OBJECT)) is defined:…(multiple dictionary senses: i.e., CLAIM SCOPE) (Dictionary.com)
108 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com)
109 -ing of (“filtering”): a suffix of nouns formed from verbs (filter), expressing the action of the verb (filter) or its result, product, material, etc. (the art of building; a new building; cotton wadding ).(Dictionary.com)
110 confidence (i.e. state of being confident): certitude; assurance, wherein certitude is defined: freedom from doubt, especially in matters of faith or opinion; certainty, wherein certainty is defined: the state of being certain, wherein certain is defined: free from doubt or reservation; confident; sure. (Dictionary.com) .
111 computed: to determine by using a computer or calculator, wherein determine is defined: to conclude or ascertain, as after reasoning, observation, etc., wherein ascertain is defined: to find out definitely; learn with certainty or assurance; determine, wherein certainty is defined: the state of being certain, wherein certain is defined: free from doubt or reservation; confident; sure. (Dictionary.com).
112 sum: the aggregate of two or more numbers, magnitudes, quantities, or particulars as determined by or as if by the mathematical process of addition, wherein determine is defined: to conclude or ascertain, as after reasoning, observation, etc., wherein ascertain is defined: to find out definitely; learn with certainty or assurance; determine, wherein certainty is defined: the state of being certain, wherein certain is defined: free from doubt or reservation; confident; sure. (Dictionary.com)..
113 “that” is a pronoun for “image”
114 state: the condition of matter with respect to structure, form, constitution, phase, or the like, wherein form is defined: configuration (Dictionary.com)
115 (italics) represent claim limitations already taught
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119 mask: mask: computing a bit pattern which, by convolution with a second pattern in a logical operation, can be used to isolate a specific subset of the second pattern for examination, wherein bit is defined: a single digit of binary notation, represented either by 0 or by 1, wherein represent is defined: to describe as having a specified character or quality; make out to be, wherein describe is defined: to pronounce or label (Dictionary.com)
120 state: the condition of matter with respect to structure, form, constitution, phase, or the like, wherein form is defined: configuration (Dictionary.com)
121 for: intended to belong to, or be used in connection with. (Dictionary.com)
122 base: to have a basis; be based (usually followed by on orupon ) (Dictionary.com)
123 base: base: to have a basis; be based (usually followed by on orupon ) (Dictionary.com)
124 -ing (of “filtering”): a suffix of nouns formed from verbs (filter), expressing the action of the verb (filter) or its result, product, material, etc. (the art of building; a new building; cotton wadding ). (Dictionary.com)
125 etc.: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted). (Dictionary.com)
126 automatic: having the capability of starting, operating, moving, etc., independently
127 controller: Also called control unit, processor. Computers. the key component of a device, as a terminal, printer, or external storage unit, that contains the circuitry necessary to interpret and execute instructions fed into the device, wherein processor is defined: Computers. a computer, wherein computer is defined: a programmable electronic device designed to accept data, perform prescribed mathematical and logical operations at high speed, and display the results of these operations, wherein device is defined: an invention or contrivance, especially a mechanical or electrical one, wherein invention is defined: U.S. Patent Law. a new, useful process, machine, improvement, etc., that did not exist previously and that is recognized as the product of some unique intuition or genius, as distinguished from ordinary mechanical skill or craftsmanship. (Dictionary.com)
128 state: the condition of a person or thing, as with respect to circumstances or attributes, wherein condition is defined: a particular mode of [being] of a person or thing; existing state; situation with respect to circumstances, wherein mode is defined: Grammar. mood, wherein mood is defined: Grammar.
A) a set of categories for which the verb [being] is inflected in many languages, and that is typically used to indicate the syntactic relation of the clause in which the verb occurs to other clauses in the sentence, or the attitude of the speaker toward what they are saying, such as certainty or uncertainty, wish or command, emphasis or hesitancy.
B) a set of syntactic devices in some languages that is similar to this set in function or meaning, involving the use of auxiliary words, such as can, may, might.
C) any of the categories of these sets.
129 (Italics) represent limitations already taught above
130 (Italics) represent limitations (“state”) already taught above
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134 states: a “be”-word
135 reflection: the act of reflecting, as in casting back a light or heat, mirroring, or giving back or showing an image; the state of being reflected in this way, wherein act is defined: the process of doing, wherein doing is defined: to act or conduct oneself, wherein act is defined: to perform as an actor. (Dictionary.com)
136 reflection: the act of reflecting, as in casting back a light or heat, mirroring, or giving back or showing an image; the state of being reflected in this way, wherein act is defined: the process of doing, wherein doing is defined: to act or conduct oneself, wherein act is defined: to perform as an actor. (Dictionary.com)
137 This phrase-- using the one or more segmentation mask[[s]] confidences—is non-limiting under the broadest reasonable interpretation.
138 -ing (of “oncoming”): a suffix of nouns formed from verbs (come), expressing the action of the verb (come) or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active, wherein acting is defined: to perform as an actor. (Dictionary.com)
139 compute: to determine by using a computer or calculator. (Dictionary.com)
140 shape: the outward form of an object defined by outline, wherein form is defined: the shape or configuration of something as distinct from its colour, texture, etc (Dictionary.com)
141 based: to have a basis; be based (usually followed by on orupon ). (Dictionary.com)
142 on: in connection, association, or cooperation with; as a part or element of (Dictionary.com)
143 (italics) represent claim limitations already taught
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149 -ing (of “oncoming”): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active. (Dictionary.com)
150 automatic: having the capability of starting, operating, moving, etc., independently
151 control: a device for regulating and guiding a machine, as a motor or airplane., wherein device is defined: an invention or contrivance, especially a mechanical or electrical one, wherein invention is defined: U.S. Patent Law. a new, useful process, machine, improvement, etc., that did not exist previously and that is recognized as the product of some unique intuition or genius, as distinguished from ordinary mechanical skill or craftsmanship.(Dictionary.com)
152 on: by the agency or means of (Dictionary.com)
153 label: a word or phrase indicating that what follows belongs in a particular category or classification, wherein classification is defined: one of the groups or classes into which things may be or have been classified. classify. (Dictionary.com)
154 represent: to be the equivalent of; correspond to. (Dictionary.com)
155 probability: statistics a measure or estimate of the degree of confidence one may have in the occurrence of an event, measured on a scale from zero (impossibility) to one (certainty). (Dictionary.com)
156 model: a simplified representation or description of a system or complex entity, esp one designed to facilitate calculations and predictions (Dictionary.com)
157 -ing (of “oncoming”): a suffix of nouns formed from verbs (come), expressing the action of the verb (come) or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active, wherein acting is defined: to perform as an actor. (Dictionary.com)
158 -ing (of “oncoming”): a suffix of nouns formed from verbs (come), expressing the action of the verb (come) or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein action is defined: the process or state of acting or of being active, wherein acting is defined: to perform as an actor. (Dictionary.com)
159 “output” is a verb (not a noun)
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162 CLAIM DIFFERENCE INTERPRETATION: ~output then inactive state stationary~
163 body: the section of a vehicle, usually in the shape of a box, cylindrical container, or platform, in or on which passengers or the load is carried. (Dictionary.com)
164 assesrt: to state with assurance, confidence, or force; state strongly or positively; affirm; aver. (Dictionary.com)
165 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com)
166 correspond: to be similar or analogous; be equivalent in function, position, amount, etc. (usually followed byto ). (Dictionary.com)
167 assignments: an act of assigning; appointment (Dictionary.com)
168 same: agreeing in kind, amount, etc.; corresponding, wherein agreeing is defined: to be consistent; harmonize (usually followed bywith ). (Dictionary.com)
169 type: a number of things or persons sharing a particular characteristic, or set of characteristics, that causes them to be regarded as a group, more or less precisely defined or designated; class; category, wherein designated is defined: to nominate or select for a duty, office, purpose, etc.; appoint; assign. (Dictionary.com)
170 of: (used to indicate apposition or identity), wherein apposition is defined: Grammar. a syntactic relation between expressions (“consistent assignments of the one or more class labels”), usually consecutive, that have the same function and the same relation to other elements (“the one or more”) in the sentence, the second expression identifying or supplementing the first. In Washington, our first president, the phrase our first president is in apposition with Washington. (Dictionary.com)
171 truth: the true or actual state of a matter, wherein true is defined: reliable, unfailing, or sure, wherein reliable is defined: that may be relied on or trusted; dependable in achievement, accuracy, honesty, etc., wherein relied is defined: to depend confidently; put trust in (usually followed by on or upon). (Dictionary.com)
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