DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-6, 8-10, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Herman et al. (US Publication 2020/0398797 A1) in view of Newman (US Publication 2018/0009418 A1).
Regarding claim 1, Herman teaches a method of operating a camera of an imaging device of a vehicle, comprising: obtaining a first image of a test object via the camera, the camera including an image sensor with an array of pixels (Herman: Para. 8; generate a virtual map of at least one of a light reflectivity or a depth from the time-of-flight sensor from the collected first sensor data and the collected second sensor data); determining a resolution of the first image (Herman: Para. 8; determine a difference between the light reflectivity or the depth of each pixel of the first sensor data from the light reflectivity or the depth of each corresponding pixel of the virtual map, determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity); comparing the resolution to a resolution threshold (Herman: Para. 8; determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity or the depth exceeding an occlusion threshold); determining a contrast ratio of the first image at a processor when the resolution is less than the resolution threshold, wherein the contrast ratio is based on a difference between a first gray scale value for a first pixel of the first image having a maximum intensity and a second gray scale value for a second pixel of the first image having a minimum intensity (Herman: Para. 47, Fig. 4; gradations 410, 510 can be represented in the depth map 405, 505 by different colors or, as shown in FIGS. 4-5, hatching representing a color gradient; the hatching of the gradation 410a, 510a can represent a dark gray, the gradation 410b, 410b can represent a medium gray, and the gradation 410c, 510c can represent a light gray); comparing the contrast ratio to a contrast threshold at the processor (Herman: Para. 53; predict a radiant flux leaving a pixel and a ray tracing algorithm, as described above, to predict a radiant flux received by the pixel, the predicted reflectivity map being the ratio of the flux leaving divided by the flux received for each pixel); performing, via the processor, a cleaning operation to clean the camera when the contrast ratio is less than the contrast threshold (Herman: Para. 10; actuate a fluid sprayer to spray cleaning fluid onto the time-of-flight sensor when the occlusion exceeds the occlusion threshold); obtaining a second image of the test object (Herman: Para. 62, 74; in the block 665, the computer determines whether to continue the process 600; process 600 begins in a block 605, in which a computer actuates a time-of-flight sensor to collect image data around a vehicle).
Herman doesn’t explicitly teach sending an alert signal to a driver of the vehicle based on the second image.
However Newman, in the same field of endeavor, teaches sending an alert signal to a driver of the vehicle based on the second image (Newman: Para. 137; if the sensor(s) have been previously cleaned a number of times and the cleaning still does not cure the detected measurement differences, the method may proceed by alerting the user).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Regarding claim 2, Herman teaches the method of claim 1, further comprising segmenting the first image into at least a first region and a second region (Herman: Para. 56-57; generate a reference map with values of 0 for pixels identified to be masked which can be multiplied to the data so the computer does not develop the reflectivity or depth for the masked pixels), further comprising segmenting the first image into at least a first region and a second region, determining a first contrast ratio for the first region and a second contrast ratio for the second region (Herman: Para. 56-57; compare each pixel of the reflectivity map 400, 500 to a corresponding pixel of the predicted reflectivity map. If the pixel of the reflectivity map 400, 500 has a reflectivity below a reflectivity threshold and the corresponding pixel of the predicted reflectivity map is above the reflectivity threshold, the computer can determine that the pixel is occluded), and performing the cleaning operation when at least one of the first contrast ratio and the second contrast ratio is less than the contrast threshold (Herman: Para. 59-60; divide the number of identified pixels by the total number of pixels in the reflectivity map 400, 500 to determine a ratio of identified pixels to total pixels; based on the ratio, the computer can determine the amount and type of obstruction; computer can actuate a cleaning component to clean the time-of-flight sensor upon identifying the type and amount of obstruction causing the occlusion).
Regarding claim 3, Herman teaches the method of claim 1, further comprising determining the resolution of the first image using a modulation transfer function (Herman: Para. 45; the computer can use a conventional modulated signal correlation function that determines the depth based on a phase between an emitted light pulse and a received light pulse).
Regarding claim 5, Herman doesn’t explicitly teach further comprising performing the cleaning operation at a subsequent time when the contrast ratio of the second image is less than the contrast threshold.
However, Herman is deemed to disclose an equivalent teaching. Herman teaches a continuation step 665 in Figure 6. This is after the cleaning component is actuated to remove the occlusion from the sensor (Herman: Para. 74, Fig. 6). In the continuation step 665 it flows back to the top of the flow chart. The system collects an image, determines the occlusions, checks the occlusions against a threshold, and actuating a cleaning component when the occlusions exceeds the threshold (Herman: Para. 62, Fig. 6).
It would have been obvious to one of ordinary skill before the effective filing date to have trying cleaning the sensor again taught in (Herman: Para. 62, Fig. 6) with a reasonable expectation of success because a system that determines a cleaning action by an occlusion threshold would go back and clean again based the method’s loop as taught by Herman (Herman: Para. 62, 74, Fig. 6).
Regarding claim 6, Herman doesn’t explicitly teach wherein the alert signal is at least one of: (i) a visual signal; and (ii) an audio signal.
However Newman, in the same field of endeavor, teaches wherein the alert signal is at least one of: (i) a visual signal; and (ii) an audio signal (Newman: Para. 144; the message may be configured to alert a user or other entity of a sensing obstruction associated with one or more sensors, indicate a severity of the obstruction, identify the sensor(s), and/or otherwise convey information about the sensor via a display device associated with the vehicle).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Regarding claim 8, Herman teaches an imaging device for a vehicle, comprising: a camera including an array of pixels (Herman: Para. 46; camera); a cleaning system (Herman: Para. 46; cleaning component); a processor configured to: obtain a first image of a test object via the camera (Herman: Para. 8; generate a virtual map of at least one of a light reflectivity or a depth from the time-of-flight sensor from the collected first sensor data and the collected second sensor data); determine a resolution of the first image (Herman: Para. 8; determine a difference between the light reflectivity or the depth of each pixel of the first sensor data from the light reflectivity or the depth of each corresponding pixel of the virtual map, determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity); compare the resolution to a resolution threshold (Herman: Para. 8; determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity or the depth exceeding an occlusion threshold); determine a contrast ratio of the first image when the resolution is less than the resolution threshold, wherein the contrast ratio is based on a difference between a first gray scale value for a first pixel of the first image having a maximum intensity and a second gray scale value for a second pixel of the first image having a minimum intensity (Herman: Para. 47, Fig. 4; gradations 410, 510 can be represented in the depth map 405, 505 by different colors or, as shown in FIGS. 4-5, hatching representing a color gradient; the hatching of the gradation 410a, 510a can represent a dark gray, the gradation 410b, 410b can represent a medium gray, and the gradation 410c, 510c can represent a light gray); compare the contrast ratio to a contrast threshold (Herman: Para. 53; predict a radiant flux leaving a pixel and a ray tracing algorithm, as described above, to predict a radiant flux received by the pixel, the predicted reflectivity map being the ratio of the flux leaving divided by the flux received for each pixel); activate the cleaning system to clean the camera when the contrast ratio is less than the contrast threshold (Herman: Para. 10; actuate a fluid sprayer to spray cleaning fluid onto the time-of-flight sensor when the occlusion exceeds the occlusion threshold); obtain a second image of the test object (Herman: Para. 62, 74; in the block 665, the computer determines whether to continue the process 600; process 600 begins in a block 605, in which a computer actuates a time-of-flight sensor to collect image data around a vehicle).
Herman doesn’t explicitly teach send an alert signal to a driver of the vehicle based on the second image.
However Newman, in the same field of endeavor, teaches send an alert signal to a driver of the vehicle based on the second image (Newman: Para. 137; if the sensor(s) have been previously cleaned a number of times and the cleaning still does not cure the detected measurement differences, the method may proceed by alerting the user).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Regarding claim 9, Herman teaches the imaging device of claim 8, wherein the processor is further configured to segment the first image into at least a first region and a second region (Herman: Para. 56-57; generate a reference map with values of 0 for pixels identified to be masked which can be multiplied to the data so the computer does not develop the reflectivity or depth for the masked pixels), determine a first contrast ratio for the first region and a second contrast ratio for the second region (Herman: Para. 56-57; compare each pixel of the reflectivity map 400, 500 to a corresponding pixel of the predicted reflectivity map. If the pixel of the reflectivity map 400, 500 has a reflectivity below a reflectivity threshold and the corresponding pixel of the predicted reflectivity map is above the reflectivity threshold, the computer can determine that the pixel is occluded), and activate the cleaning system when at least one of the first contrast ratio and the second contrast ratio is less than the contrast threshold (Herman: Para. 59-60; divide the number of identified pixels by the total number of pixels in the reflectivity map 400, 500 to determine a ratio of identified pixels to total pixels; based on the ratio, the computer can determine the amount and type of obstruction; computer can actuate a cleaning component to clean the time-of-flight sensor upon identifying the type and amount of obstruction causing the occlusion).
Regarding claim 10, Herman teaches the imaging device of claim 8, wherein the processor is further configured to determine the resolution of the first image using a modulation transfer function (Herman: Para. 45; the computer can use a conventional modulated signal correlation function that determines the depth based on a phase between an emitted light pulse and a received light pulse).
Regarding claim 12, Herman doesn’t explicitly teach wherein the processor is further configured to activate the cleaning system at a subsequent time when the contrast ratio of the second image is less than the contrast threshold.
However, Herman is deemed to disclose an equivalent teaching. Herman teaches a continuation step 665 in Figure 6. This is after the cleaning component is actuated to remove the occlusion from the sensor (Herman: Para. 74, Fig. 6). In the continuation step 665 it flows back to the top of the flow chart. The system collects an image, determines the occlusions, checks the occlusions against a threshold, and actuating a cleaning component when the occlusions exceeds the threshold (Herman: Para. 62, Fig. 6).
It would have been obvious to one of ordinary skill before the effective filing date to have trying cleaning the sensor again taught in (Herman: Para. 62, Fig. 6) with a reasonable expectation of success because a system that determines a cleaning action by an occlusion threshold would go back and clean again based the method’s loop as taught by Herman (Herman: Para. 62, 74, Fig. 6).
Regarding claim 13, Herman doesn’t explicitly teach further comprising an interface for providing the alert signal to the driver as at least one of: (i) a visual signal; and (ii) an audio signal.
However Newman, in the same field of endeavor, teaches further comprising an interface for providing the alert signal to the driver as at least one of: (i) a visual signal; and (ii) an audio signal (Newman: Para. 144; the message may be configured to alert a user or other entity of a sensing obstruction associated with one or more sensors, indicate a severity of the obstruction, identify the sensor(s), and/or otherwise convey information about the sensor via a display device associated with the vehicle).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Regarding claim 14, Herman teaches the imaging device of claim 8, wherein the camera is located at a side of the vehicle (Herman: Para. 1; sensors can be placed on or in various parts of the vehicle, e.g., a vehicle roof, a vehicle hood, a rear vehicle door).
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Herman et al. (US Publication 2020/0398797 A1) in view of Newman (US Publication 2018/0009418 A1) and in further view of Ninh et al. (US Publication 2024/0083358 A1).
Regarding claim 4, Herman and Newman don’t explicitly teach wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera.
However Ninh, in the same field of endeavor, teaches wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera (Ninh: Para. 62; secondary camera faces the side mirror and is able to capture the target calibration pattern via the side mirror; target calibration pattern is placed at the driver's seat).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) and the calibration pattern (Ninh: Para. 62) with a reasonable expectation of success because proving a target calibration pattern in the camera’s field of view allows for automatic movement and calibration (Ninh: Para. 5-6, 84).
Regarding claim 11, Herman and Newman don’t explicitly teach wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera.
However Ninh, in the same field of endeavor, teaches wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera (Ninh: Para. 62; secondary camera faces the side mirror and is able to capture the target calibration pattern via the side mirror; target calibration pattern is placed at the driver's seat).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) and the calibration pattern (Ninh: Para. 62) with a reasonable expectation of success because proving a target calibration pattern in the camera’s field of view allows for automatic movement and calibration (Ninh: Para. 5-6, 84).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Herman et al. (US Publication 2020/0398797 A1) in view of Newman (US Publication 2018/0009418 A1) and in further view of Altman (US Publication 2021/0116907 A1).
Regarding claim 7, Herman and Newman don’t explicitly teach further comprising sending the alert signal when at least one of: (i) the resolution of the second image is less than the resolution threshold; and (ii) the contrast ratio of the second image is less than the contrast threshold.
However Altman, in the same field of endeavor, teaches further comprising sending the alert signal when at least one of: (i) the resolution of the second image is less than the resolution threshold; and (ii) the contrast ratio of the second image is less than the contrast threshold (Altman: Para. 75; resolution or details-in-the-image drop below threshold values, then the remote AI may alert the human tele-operator).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) and the resolution threshold (Altman: Para. 75) with a reasonable expectation of success because monitoring the sensor imaging and alerting the operator when the sensor’s resolution drops below threshold values so that the operator can take over vehicle control (Altman: Para. 75).
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Herman et al. (US Publication 2020/0398797 A1) in view of Ninh et al. (US Publication 2024/0083358 A1) and in further view of Newman (US Publication 2018/0009418 A1).
Regarding claim 15, Herman teaches a vehicle, comprising: (Herman: Para. 1; vehicle) …… ; an imaging system including: a camera including an image sensor having an array of pixels (Herman: Para. 46; camera); a cleaning system for cleaning a glass surface of the camera (Herman: Para. 46; cleaning component); a processor configured to: obtain a first image of the test object via the camera (Herman: Para. 8; generate a virtual map of at least one of a light reflectivity or a depth from the time-of-flight sensor from the collected first sensor data and the collected second sensor data); determine a resolution of the first image (Herman: Para. 8; determine a difference between the light reflectivity or the depth of each pixel of the first sensor data from the light reflectivity or the depth of each corresponding pixel of the virtual map, determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity); compare the resolution to a resolution threshold (Herman: Para. 8; determine an occlusion of the time-of-flight sensor as a number of pixels having respective differences of the light reflectivity or the depth exceeding an occlusion threshold); determine a contrast ratio of the first image when the resolution is less than the resolution threshold, wherein the contrast ratio is based on a difference between a first gray scale value for a first pixel of the first image having a maximum intensity and a second gray scale value for a second pixel of the first image having a minimum intensity (Herman: Para. 47, Fig. 4; gradations 410, 510 can be represented in the depth map 405, 505 by different colors or, as shown in FIGS. 4-5, hatching representing a color gradient; the hatching of the gradation 410a, 510a can represent a dark gray, the gradation 410b, 410b can represent a medium gray, and the gradation 410c, 510c can represent a light gray); compare the contrast ratio to a contrast threshold (Herman: Para. 53; predict a radiant flux leaving a pixel and a ray tracing algorithm, as described above, to predict a radiant flux received by the pixel, the predicted reflectivity map being the ratio of the flux leaving divided by the flux received for each pixel); activate the cleaning system to clean the camera when the contrast ratio is less than the contrast threshold (Herman: Para. 10; actuate a fluid sprayer to spray cleaning fluid onto the time-of-flight sensor when the occlusion exceeds the occlusion threshold); obtain a second image of the test object (Herman: Para. 62, 74; in the block 665, the computer determines whether to continue the process 600; process 600 begins in a block 605, in which a computer actuates a time-of-flight sensor to collect image data around a vehicle).
Herman doesn’t explicitly teach a test object located on the vehicle.
However Ninh, in the same field of endeavor, teaches a test object located on the vehicle (Ninh: Para. 62; target calibration pattern is placed at the driver's seat).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the calibration pattern (Ninh: Para. 62) with a reasonable expectation of success because proving a target calibration pattern in the camera’s field of view allows for automatic movement and calibration (Ninh: Para. 5-6, 84).
Herman and Ninh don’t explicitly teach send an alert signal to a driver of the vehicle based on the second image.
However Newman, in the same field of endeavor, teaches send an alert signal to a driver of the vehicle based on the second image (Newman: Para. 137; if the sensor(s) have been previously cleaned a number of times and the cleaning still does not cure the detected measurement differences, the method may proceed by alerting the user).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Regarding claim 16, Herman teaches the vehicle of claim 15, wherein the processor is further configured to segment the first image into at least a first region and a second region (Herman: Para. 56-57; generate a reference map with values of 0 for pixels identified to be masked which can be multiplied to the data so the computer does not develop the reflectivity or depth for the masked pixels), determine a first contrast ratio for the first region and a second contrast ratio for the second region (Herman: Para. 56-57; compare each pixel of the reflectivity map 400, 500 to a corresponding pixel of the predicted reflectivity map. If the pixel of the reflectivity map 400, 500 has a reflectivity below a reflectivity threshold and the corresponding pixel of the predicted reflectivity map is above the reflectivity threshold, the computer can determine that the pixel is occluded), and activate the cleaning system when at least one of the first contrast ratio and the second contrast ratio is less than the contrast threshold (Herman: Para. 59-60; divide the number of identified pixels by the total number of pixels in the reflectivity map 400, 500 to determine a ratio of identified pixels to total pixels; based on the ratio, the computer can determine the amount and type of obstruction; computer can actuate a cleaning component to clean the time-of-flight sensor upon identifying the type and amount of obstruction causing the occlusion).
Regarding claim 17, Herman teaches the vehicle of claim 15, wherein the processor is further configured to determine the resolution of the first image using a modulation transfer function (Herman: Para. 45; the computer can use a conventional modulated signal correlation function that determines the depth based on a phase between an emitted light pulse and a received light pulse).
Regarding claim 18, Herman doesn’t explicitly teach wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera.
However Ninh, in the same field of endeavor, teaches wherein the test object is at least one of: (i) a door handle of the vehicle; (ii) a tail light of the vehicle; (iii) a test pattern printed on the vehicle within a field of view of the camera; and (iv) a set of LEDs on the vehicle and within the field of view of the camera (Ninh: Para. 62; secondary camera faces the side mirror and is able to capture the target calibration pattern via the side mirror; target calibration pattern is placed at the driver's seat).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the calibration pattern (Ninh: Para. 62) with a reasonable expectation of success because proving a target calibration pattern in the camera’s field of view allows for automatic movement and calibration (Ninh: Para. 5-6, 84).
Regarding claim 19, Herman doesn’t explicitly teach wherein the processor is further configured to activate the cleaning system at a subsequent time when the contrast ratio of the second image is less than the contrast threshold.
However, Herman is deemed to disclose an equivalent teaching. Herman: Para. 62, 74, Fig. 6; Herman teaches a continuation step 665 in Figure 6. This is after the cleaning component is actuated to remove the occlusion from the sensor (Herman: Para. 74, Fig. 6). In the continuation step 665 it flows back to the top of the flow chart. The system collects an image, determines the occlusions, checks the occlusions against a threshold, and actuating a cleaning component when the occlusions exceeds the threshold (Herman: Para. 62, Fig. 6).
It would have been obvious to one of ordinary skill before the effective filing date to have trying cleaning the sensor again taught in (Herman: Para. 62, Fig. 6) with a reasonable expectation of success because a system that determines a cleaning action by an occlusion threshold would go back and clean again based the method’s loop as taught by Herman (Herman: Para. 62, 74, Fig. 6).
Regarding claim 20, Herman and Ninh don’t explicitly teach further comprising an interface for providing the alert signal to a driver as at least one of: (i) a visual signal; and (ii) an audio signal.
However Newman, in the same field of endeavor, teaches further comprising an interface for providing the alert signal to a driver as at least one of: (i) a visual signal; and (ii) an audio signal (Newman: Para. 144; the message may be configured to alert a user or other entity of a sensing obstruction associated with one or more sensors, indicate a severity of the obstruction, identify the sensor(s), and/or otherwise convey information about the sensor via a display device associated with the vehicle).
It would have been obvious to one having ordinary skill in the art to modify the system of cleaning a vehicle’s image sensor (Herman: Para. 8) with the displayed message of failed sensor cleaning (Newman: Para. 137) with a reasonable expectation of success because if cleaning the image sensor does not work then a message to the user helps the user know the location and severity of the obstruction for the sensor (Newman: Para. 137, 144).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571) 272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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