Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/2/2026 has been entered.
Response to Amendment
The Amendment filed 1/28/2026 has been entered and considered. Claims 1, 14, and 15 have been amended. New claims 17-19 have been added.
Response to Arguments
Applicant’s arguments with respect to amended claim(s) 1, 14, and 15 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 Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 7-8, 10, and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hammarstrom et al. (Previously cited) in view of Diaz-Cabrera et al. (NPL, “Robust real-time traffic light detection and distance estimation using a single camera”, published 2015, pdf attached) further in view of Tian et al. (US Patent Pub. No. 2016/0252905 A1, published 2016).
Regarding claim 1, Hammarstrom teaches a method for processing a video stream of images captured by at least one color camera on board a motor vehicle (Para. 16, “…includes an imaging means 11 for acquiring images of a region surrounding the motor vehicle. Preferably the imaging means 11 includes one or more optical imaging devices 12, in particular at least one camera”; Para. 8, “Therefore, the imaging means preferably includes a color camera or another color sensitive camera.”), said images being used by a computer on board said vehicle to detect a priority vehicle located in the environment of the vehicle (Para. 6, “in case an emergency vehicle is detected, the driver is informed and/or alerted correspondingly through a driver assisting means”), the at least one camera being oriented toward the rear of the vehicle (Para. 16, “the imaging means 11 is a rear view camera 12 adapted to acquire images of a region behind the motor vehicle”), said method comprising: acquiring an image sequence (Para. 16, “…includes an imaging means 11 for acquiring images of a region surrounding the motor vehicle”); for each image of the image sequence: performing color extraction processing, making it possible to detect colored luminous zones of the image that are likely to be flashing lights (Para. 8, “Preferably the processing means is adapted to determine the color of the warning light (blue, orange, red, etc.)… For this application, the imaging means is expediently adapted to extract color information from the acquired images.”); tracking each luminous zone (Para. 22, “In particular, the processing means 14 tracks the warning lights 22 over subsequent image frames and determines the blinking frequency or blinking frequencies of the warning lights 22”), according to which each luminous zone is associated with a prediction luminous zone of the same color (Para. 8, “This allows the processing means to verify that the determined color is equal to at least one pre-defined color corresponding to the usual warning light colors of emergency vehicles like police cars, ambulances or fire engines.”); performing colorimetric classification, using a previously trained classifier, of each luminous zone (Para. 8, “The color information may thus be used to identify or classify the type of emergency vehicle”; Para. 10, “The classifier may advantageously be a trained classifier.”); performing frequency analysis of each luminous zone, making it possible to determine a flashing nature of said zone (Para. 22, “the processing means 14 tracks the warning lights 22 over subsequent image frames and determines the blinking frequency or blinking frequencies of the warning lights 22”); and verifying each image of the image sequence, making it possible to declare a luminous zone as being a flashing light (Para. 9, “Preferably the processing means is adapted to verify that the warning light belongs to another vehicle in order to rule out other (in particular static) periodically blinking warning lights for example on gates, specific traffic lights, for example at railway crossings, and the like. For the same reason, the processing means preferably is adapted to verify that the warning light belongs to a moving vehicle. Furthermore, the processing means preferably is adapted to verify that the warning light has a pre-defined position with respect to another vehicle, for example on the roof of an emergency vehicle, or periodically blinking front lights of an emergency vehicle.”).
Hammarstrom does not explicitly disclose performing thresholding-based colorimetric segmentation, computing an overall confidence index for each image of the image sequence, considering a confidence index of classification from the colorimetric classification step, or performing frequency analysis of each segmented luminous zone. However, they do disclose performing some form of colorimetric extraction, utilizing multiple verification steps to enable a declaration of a luminous zone as being a flashing light, and performing a frequency analysis on each luminous zone.
Diaz-Cabrera teaches performing thresholding-based colorimetric segmentation, making it possible to detect colored luminous zones of the image (Pg. 5, Col. 1, “Color segmentation is an extremely important step on our system to discriminate among traffic light color states… Five clusters were created… corresponding to red, amber, green, black and white colors… Therefore, each pixel from an image would be contained in one of these clusters.”; Pg. 5, Col. 2, “Therefore several ranges based on RGB and RGB normalized components were established.”; Pg. 6, Col. 1, “Four new isolated monochromatic images were built at the end of this process corresponding to red, amber, green and black pixels alongside uncategorized and false positive pixels. In the following steps the resulting images will be managed.”, Also see Appendix A, Algorithms 2 and 3, where thresholds are explicitly disclosed as being used to detect colors).
Diaz-Cabrera does not explicitly disclose computing an overall confidence index for each image of the image sequence, considering a flashing state and a confidence index of classification from the colorimetric classification step, the flashing state and the confidence index of classification making it possible to declare a segmented luminous zone as being a flashing light based on the overall confidence index for each image of the image sequence.
Tian teaches computing an overall confidence index for each image of the image sequence, considering a flashing state and a confidence index of classification from the colorimetric classification step (Para. 62, “For example, the light source 414 in FIGS. 4A-C are configured in a generally horizontal manner. Based on the spatial configuration of the light source 414 and/or the comparison between the flash pattern of the light source 414 with one or more classifiers stored in memory 130, the computing device 110 may determine that the object 412 is a police vehicle (PV)”, Tian does not explicitly name a “confidence index” as being used. However, they disclose a trained classifier that takes multiple input signals, such as the color of the light, the light pattern, a flashing determination, and the positional relationship of the lights, and uses them to output a classification decision. Any trained classifier inherently produces an internal confidence index when it evaluates an input. The end classification result of whether it is an emergency vehicle or not, for example, is a simple thresholding of the underlying confidence value), the flashing state and the confidence index of classification making it possible to declare a segmented luminous zone as being a flashing light based on the overall confidence index for each image of the image sequence (Para. 62, “Based on the spatial configuration of the light source 414 and/or the comparison between the flash pattern of the light source 414 with one or more classifiers stored in memory 130, the computing device 110 may determine that the object 412 is a police vehicle (PV). Upon determining that the flashing light source corresponds to a PV, the autonomous vehicle may appropriately respond by slowing down and/or pulling over to the side of the road.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hammarstrom to incorporate the teachings of Diaz-Cabrera and Tian to include performing thresholding-based colorimetric segmentation, to compute an overall confidence index for each image of the image sequence, considering a confidence index of classification from the colorimetric classification step, and to perform frequency analysis of each segmented luminous zone. Hammarstrom discloses a system for performing color extraction from images captured by a moving vehicle and the use of a classifier to detect luminous zones, however they do not explicitly disclose how they perform this color extraction. Diaz-Cabrera teaches a robust method for performing thresholding-based colorimetric segmentation in order to identify and extract colors from traffic lights. Diaz-Cabrera does not specify the use of a confidence index to perform classification of the lights, however they do perform a probability determination on the 3D position of the luminous zone. Tian discloses determining whether a detected light source is flashing and whether it belongs to a class of emergency vehicle, and based off of that determining whether to act, which inherently involves computing an aggregate confidence value from both the flashing determination and the classification output. To the extent that Tian does not explicitly disclose labeling this aggregate value as a confidence index, one of ordinary skill in the art would have recognized that implementing the combined determination as a numerical confidence index is standard practice in classifier design and produces a more flexible detection system than a binary determination (See previously cited Thomas, which discloses confidence levels as well-known in the art).
Regarding claim 2, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as wherein, in the segmentation step, predefined segmentation thresholds are used so as to segment the luminous zones according to four categories: the color red, the color orange, the color blue, and the color violet (Para. 8, “Preferably the processing means is adapted to determine the color of the warning light (blue, orange, red, etc.). This allows the processing means to verify that the determined color is equal to at least one pre-defined color corresponding to the usual.”; Diaz-Cabrera; Pg. 12, Algorithms 2 and 3 showcase the usage of segmentation thresholds according to color).
Hammarstrom as modified does not show the color violet specifically. However, (etc.) indicates other colors are contemplated.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hammarstrom in view of Diaz-Cabrera and Tian to include different colors such as violet. Hammarstrom as modified in view of Diaz-Cabrera and Tian already discloses the determination of multiple colors, providing multiple non-limiting examples. One of ordinary skill in the art would have recognized the importance of determining any relevant colors for emergency vehicles, including blue and violet, based on the practices of local emergency responders, in order to correctly distinguish the lights of emergency vehicles. Geographical information is also mentioned as being used due to emergency light colors differing from country to country (Para. 8), leaving violet as a color to be considered in view of different geographical locations.
Regarding claim 3, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as wherein, after the segmentation step, the method furthermore comprises a post-segmentation filtering step, making it possible to filter the results from the segmentation step, this post-segmentation step being carried out according to predetermined criteria regarding position and/or size and/or color and/or intensity (Diaz-Cabrera; Pg. 8, Col. 1, “A traffic light was validated only if its tracking age was over a fixed threshold, and it was removed if it was not detected for a fixed number of frames… As most suspended traffic lights were found in pairs, and the distance between each other could be reasonably fixed within a range, if two blobs were detected at the same height and distance, lower thresholds were used for the validation”).
Regarding claim 7, Hammarstrom as modified above teaches all of the elements of claim 3, as stated above, as well as wherein the post- segmentation step comprises, for a segmented luminous zone, a sub-step of performing oriented chromatic thresholding-based filtering (Diaz-Cabrera; Pg. 5, Col. 2, “All these six data (R; G; B; RN; GN; BN) were combined to one another through the basic arithmetic operation of the components of each pixel on the image… For instance, for the subtraction between the red and green colors R - G, the mean and the standard deviation for red, amber, green and false positive (white) cluster were calculated”; Pg. 7, Col. 1, “True color was a function developed for this system. Each pixel within bounding boxes was verified… To validate a pixel, the component values have to satisfy all the mean values (m) for the five Gaussian curves with minimum standard deviation. They were previously established for the color segmentation, in Section 4.2. Such rule is more restrictive because we are interested in a few candidates on the images at this point. To confirm a bounding box, a minimum number of pixels had to be validated following this formula:
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, where n was the number of pixels which satisfy the conditions, A was the bounding box area and X was the threshold.”).
Regarding claim 8, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as wherein comprising a second segmentation step, at the end of the tracking step, for each segmented luminous zone for which no association was found (Para. 7, “Preferably the processing means is adapted to determine the blinking frequency of the warning light, which can be accomplished in particular by tracking a detected light source over several image frames. This allows the processing means to verify that the determined blinking frequency is equal to at least one pre-defined blinking frequency corresponding to the usual warning light blinking frequencies of emergency vehicles like police cars, ambulances or fire engines.”, the detected light source is continually tracked across multiple image frames to verify the pre-defined association, necessarily performing a plurality of segmentation steps when modified in view of Diaz-Cabrera to complete the verification process; Diaz-Cabrera; Pg. 8, Col. 1, “As the traffic light cycle (green – amber – red) was known and the lenses can be found approximately in the same columns of the image and at the same distance, it was possible to track the traffic lights even when a light was turning itself off and the following one was turning itself on: in this case, the previous bounding box was removed and the new one inherited its age")
Regarding claim 10, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as wherein, in the frequency analysis step, a flashing frequency of each segmented luminous zone is compared with a first frequency threshold and with a second frequency threshold greater than the first frequency threshold, both thresholds being predetermined (Para. 7, “This allows the processing means to verify that the determined blinking frequency is equal to at least one pre-defined blinking frequency corresponding to the usual warning light blinking frequencies of emergency vehicles like police cars, ambulances or fire engines.”, in order to determine a “pre-defined blinking frequency corresponding to the usual warning light blinking frequencies” there is necessarily an upper and lower bound to determine a correspondence.), a segmented luminous zone being filtered if: its flashing frequency is less than the first frequency threshold, such that the segmented luminous zone is considered to be not flashing or weakly flashing, and therefore to not be a flashing light; its flashing frequency is greater than the second frequency threshold, such that the luminous zone is also considered to not be a flashing light (Para. 22, “The processing means 14 then proves whether the determined blinking frequency and/or warning light color correspond(s) to usual warning light blinking frequencies or colors of emergency vehicles. Such pre-defined information may for example be stored in the memory 25.”, Including a second pre-defined blinking frequency simply defines an upper and lower bound, which is necessary for determining a corresponding flashing light frequency. Performing thresholding based on these bounds is an obvious next step to one of ordinary skill in the art.). Hammarstrom does not say that detecting a frequency is detecting a range of frequencies. However, in a real implementation, detecting the frequency would not detect the exact frequency but would detect a more or less narrow range. This requires an upper and lower bound. It would have been obvious to set an appropriate range to detect flashing lights at real world (i.e. imperfect) frequencies.
Regarding claim 11, Hammarstrom as modified above teaches all of the elements of claim 10, as stated above, as well as wherein the first frequency threshold is equal to 1 Hz and the second frequency threshold is equal to 5 Hz (Para. 22, “The processing means 14 then proves whether the determined blinking frequency and/or warning light color correspond(s) to usual warning light blinking frequencies or colors of emergency vehicles.”, Further limiting the frequency thresholds to a specific number would have been an obvious design choice to one of ordinary skill in the art, given that “usual warning light blinking frequencies of emergency vehicles” is already known and predefined, with different ranges possibly being needed depending on geographical location.).
Regarding claim 12, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as further comprising performing directional analysis of each segmented luminous zone (Para. 22, “In particular, the processing means 14 tracks the warning lights 22 over subsequent image frames”), making it possible to determine a displacement of said segmented luminous zone (Para. 23, “Preferably the processing means 14 is adapted to verify that the warning light 22 is belonging to a moving vehicle”).
Regarding claim 13, Hammarstrom as modified above teaches all of the elements of claim 12, as stated above, as well as wherein a segmented luminous zone is filtered if the displacement direction obtained in the directional analysis step makes it possible to conclude as to: immobilization of the segmented luminous zone, with respect to the vehicle (Para. 9, “Preferably the processing means is adapted to verify that the warning light belongs to another vehicle in order to rule out other (in particular static) periodically blinking warning lights for example on gates”; Para. 23, “Preferably the processing means 14 is adapted to verify that the warning light 22 is belonging to a moving vehicle”); moving away of the segmented luminous zone, with respect to the vehicle (Para. 24, “The processing means 14 may decide that action is required only if the distance to the warning lights is smaller than a pre-defined threshold and/or if the emergency vehicle 21 is approaching the vehicle 20, i.e. if the distance to the warning lights 22 is reducing.”).
Regarding claim 14, the recited non-transitory computer program (Para. 19, “The processing means 14 expediently has access to a memory means 25”, a memory means is taken to be a non-transitory storage medium) product performs variably the same function as the method of claim 1. It is rejected under the same analysis.
Regarding claim 15, the recited vehicle performs variably the same function as the method of claim 1. It is rejected under the same analysis.
Regarding claim 16, the recited elements perform variably the same function as that of claim 3. It is rejected under the same analysis.
Regarding claim 17, Hammarstrom as modified above teaches all of the elements of claim 1, as stated above, as well as further comprising assigning a weight value to each segmented luminous zone, the weight value being based on at least one of a size of the segmented luminous zone (Diaz-Cabrera; Pg. 7, Col. 1, “To confirm a bounding box, a minimum number of pixels had to be validated following this formula:
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, where n was the number of pixels which satisfy the conditions, A was the bounding box area and X was the threshold.”), a luminous intensity of the segmented luminous zone (Para. 21, “the image processing means detects the blinking warning lights 22 in the images taken by the rear view camera 12 by suited image processing, in particular by detecting periodical changes in the light intensity in well-defined regions of the acquired images.”; Diaz-Cabrera; Pg. 4, Col. 1, “4.1. Adaptive image acquisition with exposure control: …Exposure time was managed carefully: a tradeoff between night images, which allow to detect far away or dark lamps, and dark images, which allow to detect close or very bright lamps.”, luminous intensity is considered), a clarity of the color of the segmented luminous zone (Diaz-Cabrera; Pg. 7, Col. 1, “True color was a function developed for this system. Each pixel within bounding boxes was verified.”), and a position of the segmented luminous zone to other segmented luminous zones in the image (Diaz-Cabrera; Pg. 8, Col. 1, “A traffic light was validated only if its tracking age was over a fixed threshold, and it was removed if it was not detected for a fixed number of frames… As most suspended traffic lights were found in pairs, and the distance between each other could be reasonably fixed within a range, if two blobs were detected at the same height and distance, lower thresholds were used for the validation”; Tian; Para. 38, “After one or more potential light sources corresponding to EVs are identified in an image, the spatial configuration of the individual light sources, size of the light sources, etc., may be used to determine the type of EV.”), wherein the weight values of the segmented luminous zones are considered in computing the overall confidence index for each image (See analysis of claim 1 above. One of ordinary skill in the art would have understood that implementing a trained classifier, such as the one disclosed by Tian, would inherently be assigning a weight to input features for each luminous zone in order to compute a confidence index for final classification).
Regarding claim 18, the recited elements perform variably the same function as that of claim 17. It is rejected under the same analysis.
Regarding claim 19, the recited elements perform variably the same function as that of claim 17. It is rejected under the same analysis.
Claim(s) 4-6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hammarstrom as modified above in view of Diaz-Cabrera and Tian, further in view of Bogacki et al. (NPL, “Selected Methods for Increasing the Accuracy of Vehicle Lights Detection”, published 8/26/2019, previously cited) and Workman et al. (NPL, “Horizon lines in the Wild”, published 8/16/2016, previously cited).
Regarding claim 4, Hammarstrom as modified in view of Diaz-Cabrera and Tian teaches all of the elements of claim 3, as stated above. They do not explicitly disclose a dimensional filtering sub-step in which luminous zones located in parts of the image that are far from a horizon line and from a vanishing point and have a size less than a predetermined dimensional threshold are filtered. However, they do perform thresholding based on the distance of the luminous zone (Para. 24; Diaz-Cabrera; Pg. 7, Col. 1).
Bogacki teaches wherein the post-segmentation step comprises a dimensional filtering sub-step in which luminous zones located in parts of the image that have a size less than a predetermined dimensional threshold are filtered (Pg. 228, Col. 1, “Light spots detection: The most common methods for light spots detection include a fixed thresholding [3]– [5] or adaptive thresholding [6]… Typical general rules for pairing (of light spots) are as follows: spots have to be horizontally close to each other and the vertical and horizontal positions should be considered; spots should be of similar size; widths of particular bounding boxes enclosing two spots should be greater than their heights; area (No. of pixels) of both spots should be similar; a symmetry condition has to be fulfilled”).
Bogacki does not explicitly reference a horizon line or a vanishing point being used as reference locations in an image. However, they do recognize the importance of considering the horizontal and vertical positions of the luminous zones.
Workman teaches a horizon line and a vanishing point (Pg. 1, “Knowledge of the horizon line enables a wide variety of applications, including: image metrology [8], geometrically biased pedestrian and vehicle detection [14]…”; Pg. 2, “However, we show that by using our CNN as context for their method, replacing the one they proposed, significantly improves performance for vanishing point based horizon line estimation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hammarstrom in view of Diaz-Cabrera and Tian to incorporate the teachings of Bogacki and Workman to include the post-segmentation step comprising a dimensional filtering sub-step in which luminous zones located in parts of the image that are far from a horizon line and from a vanishing point and have a size less than a predetermined dimensional threshold are filtered. Hammarstrom teaches that the position of the luminous zones is an important factor when attempting to classify it as a flashing light of an emergency vehicle. Bogacki further teaches filtering luminous zones based on spatial attributes and size, including the use of thresholding to eliminate detections unlikely to correspond to meaningful objects. Workman teaches the usage of a horizon line and vanishing point to assist with computer vision tasks such as vehicle detection. One of ordinary skill in the art would understand the horizon line and vanishing point to be standard reference points in image analysis, commonly used to assess the relative position of objects within a scene, as disclosed by Workman. Incorporating a post-segmentation filtering step that removes small luminous zones distant from such reference points would improve classification accuracy by discarding irrelevant data. This would present a predictable improvement using known techniques in the art, improving the classification of the luminous zones.
Regarding claim 5, Hammarstrom as modified above in view of Diaz-Cabrera and Tian teaches all of the elements of claim 3, as stated above, and in view of Bogacki and Workman as further modified above also teaches wherein the post-segmentation step comprises a sub-step of filtering luminous zones having a size greater than a predetermined dimensional threshold (Bogacki, Pg. 228, Col. 1, “Light spots detection: The most common methods for light spots detection include a fixed thresholding [3]– [5] or adaptive thresholding [6]… spots should be of similar size; widths of particular bounding boxes enclosing two spots should be greater than their heights; area (No. of pixels) of both spots should be similar; a symmetry condition has to be fulfilled.”) and a luminous intensity less than a predetermined luminous intensity threshold (Bogacki, Pg. 228, Col. 1, “Brightness features – they include the average and the variance of intensity of all pixels within the blob. Typically, the close light objects are brighter than far away objects or reflected objects. Also, headlights are generally brighter than taillights”, One of ordinary skill in the art would recognize the importance of both size and luminous intensity in view of Bogacki, leaving it obvious to implement a predetermined threshold to account for these attributes).
Regarding claim 6, Hammarstrom as modified in view of Diaz-Cabrera and Tian teaches all of the elements of claim 3, as stated above, and in view of Bogacki and Workman as modified above also teaches wherein the post-segmentation step comprises a positional filtering sub-step in which luminous zones positioned below a horizon line defined on the image of the image sequence are filtered (Bogacki, Pg. 228, Col. 1, “Light spots detection: The most common methods for light spots detection include a fixed thresholding [3]– [5] or adaptive thresholding [6]… Typical general rules for pairing (of light spots) are as follows: spots have to be horizontally close to each other and the vertical and horizontal positions should be considered.” See analysis of claim 4 above, where it would have been obvious to account for a “horizon line” to more accurately classify the luminous zones in view of their relative position in the image).
Regarding claim 9, Hammarstrom as modified in view of Diaz-Cabrera and Tian teaches all of the elements of claim 8, as stated above, and in view of Bogacki and Workman as modified above also teaches wherein the second segmentation step comprises: a first sub-step in which the segmentation thresholds are widened and the segmentation and tracking steps are repeated for each image of the image sequence with these new widened segmentation thresholds (Bogacki, Pg. 228, Col. 1, “Light spots detection: The most common methods for light spots detection include… adaptive thresholding [6].”, it is well-known to use adaptive thresholding, such as widening a threshold, to further analyze the luminous zones in an image, leaving it obvious to one of ordinary skill in the art to incorporate such thresholds to more accurately and completely identify the relevant zones), the segmentation thresholds being those corresponding to the color of the segmented luminous zone, and if, at the end of this first sub-step, no association has been found, a second sub-step in which the segmentation thresholds are modified so as to correspond to those of the color white (Bogacki, Pg. 228, Col. 2, “Color features – they may refer to the dominant hue, average saturation and average value of each blob. This is motivated by the observation that each type of light usually has its specific dominant color – headlights are usually white, taillights are reddish and street lights might be yellow”, one of ordinary skill in the art would understand that headlights are usually white, thus in the context of detecting emergency flashing lights which are usually not, it would have been obvious to account for the color white when analyzing the luminous zones).
Conclusion
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/DAVID ALEXANDER WAMBST/ Examiner, Art Unit 2663
/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698