DETAILED ACTION
This Office Action is drafted in response to Amendments/Request for Reconsideration filed 10/24/2025. Claims 1-20 are pending. Claims 1-20 are rejected as cited below. This action is made FINAL.
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 .
Response to Specification Objection
Examiner withdraws the title objection in view of Applicant’s amendments.
Response to Arguments
Applicant's arguments filed 10/24/2025 have been fully considered but they are not persuasive.
Applicant states, on page 8 of Response, “Therefore, Claims 1, 8, and 15 patentably define over Lim and Wang, separately and in combination, because the references fail to disclose or suggest determining a track spawning range of an AV, the track spawning range being a radial distance at which a perception system of the AV can confidently detect, track, and classify target objects located within an environment of the AV.” Examiner respectfully disagrees. Wang teaches determining of a track spawning range in at least ¶ [0050], as cited below. A “track spawning range”, per Applicant’s specification, is merely the radial distance at which an autonomous vehicle can detect, track, and/or classify objects in the environment. Wang does just this by “Based on the determined density and/or the extinction coefficient, the VRM characterization module 133-2 can further determine the visibility range for various components (e.g., lidar(s) 122, radar 126, camera(s) 129, etc.) of the sensing system of the AV.” (Wang ¶ [0050]). Additionally, the track spawning range is taught by Wang as being a radial distance (¶ [0031] “The radial distance can be determined from the lidar data…”). Lastly, the Wang perception system detects, tracks (¶ [0029] “At least ¶ [0029] “The perception system 132 can be configured to detect and track objects in the driving environment 110 and to recognize the detected objects.”), and classifies (¶ [0039] “Perception system 132 can also include an object classification module 188 that classifies clusters of points as associated with objects of a particular type, such as cars, trucks, buses motorcycles, bicyclists, pedestrians, stationary objects, and so on.”) objects in the environment. See updated mappings below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lim (US Pub. 2023/0182723 A1; hereafter Lim) in view of Wang et al. (US Pub. 2022/0390612 A1; hereafter Wang).
Lim and Wang were cited in the previous Office Action.
Regarding claim 1, Lim teaches:
A system comprising:
at least one memory (a memory 1300); and
at least one processor (processor 1100) coupled to the at least one memory, the at least one processor configured to:
collect sensor data for an environment around an autonomous vehicle (AV) (At least ¶ [0049] “The sensor device 200 may be a group of sensors for detecting driving information of the vehicle" and ¶ [0050] “The radar sensor 201 may radiate a laser beam and may detect an obstacle located around the vehicle…”);
determine, based on the collected sensor data, that fog exists in the environment around the AV (At least ¶ [0078] “the input device 20 may input a fog image captured by a camera 202 of FIG. 1, which is required in a process of identifying a fog situation of a road where the vehicle is traveling, to the controller 40.”), and
determine, based on the collected sensor data, a fog proxy level (At least ¶ [0079] “The learning device 30 may classify various types of fog images input from the input device 20 into a plurality of levels based on deep learning.” and ¶ [0084] “a learning device 30 of FIG. 2 may divide a fog level in a fog image into three grades (i.e., grade 1, grade 2, and grade 3)).
Lim does not teach:
determine, based on the fog proxy level, a track spawning range of the AV; and
adjust, based on the track spawning range of the AV, a speed of the AV, the track spawning range being a radial distance at which a perception system of the AV can confidently detect, track, and classify target objects located within the environment.
However, Wang, within the same field of endeavor, teaches:
determine, based on the fog proxy level, a track spawning range of the AV (At least ¶ [0050] “Based on the identified degree of reduction, the VRM characterization module 133-2 can determine the density of the VRM and/or the extinction coefficient of light propagation in the VRM. Based on the determined density and/or the extinction coefficient, the VRM characterization module 133-2 can further determine the visibility range for various components (e.g., lidar(s) 122, radar 126, camera(s) 129, etc.) of the sensing system of the AV.” and ¶ [0048] “the VRM characterization module 133-2 can determine the type of VRM (e.g., fog, rain, snow, dust, etc.) and the density of the VRM…”), the track spawning range being a radial distance at which a perception system of the AV can confidently detect, track, and classify target objects located within the environment (At least ¶ [0029] “The perception system 132 can be configured to detect and track objects in the driving environment 110 and to recognize the detected objects.” and ¶ [0031] “The radial distance can be determined from the lidar data whereas the angles can be independently known from a synchronizer data, a clock data, e.g., based on the known lidar scanning frequency within the horizontal plane.” and ¶ [0039] “Perception system 132 can also include an object classification module 188 that classifies clusters of points as associated with objects of a particular type, such as cars, trucks, buses motorcycles, bicyclists, pedestrians, stationary objects, and so on.” Additionally, see FIG. 3A which shows the LiDAR sensor emitting rays in a radial pattern.); and
adjust, based on the track spawning range of the AV, a speed of the AV (At least ¶ [0061] “For example, the perception system of the AV can detect presence of fog in the driving environment and can further determine that camera visibility is 50 meters and that lidar visibility is 75 meters. The perception system can communicate this information to the control system (e.g., the AVCS 140). The control system can use this information for charting a driving path of the AV, including modifying (compared with good visibility conditions) the AV's speed regime, the way the AV stops, brakes, changes lanes, backs up, and the like. The control system can subsequently output instructions to powertrain, brakes, and steering 150, vehicle electronics 160, signaling 170, etc., to ensure that the AV follows the determined driving path.”).
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 Lim with Wang. This modification would have been obvious as both Lim and Wang contain subject matter within the same field of endeavor (determining AV atmospheric visibility) and Lim ¶ [0009] notes that “When it is determined that the current driving environment is a fog situation without regard to a visible distance of a driver according to the fog situation, such an existing technology decreases driving satisfaction of the driver because of reducing a speed of the vehicle at all times.”. Introducing Wang to Lim helps increase driving satisfaction of a driver. One of ordinary skill in the art may be motivated to utilize the speed adjustment feature of Wang in order to lessen the harshness of the Lim system, thus increasing driver satisfaction. Wang helps the AV maintain an appropriate speed based on AV detection distance. This may also increase the safety of pedestrians proximate to the AV in that stopping distances may be reduced in low visibility areas, thus lessening the chance of a collision.
Regarding claim 2, the combination of Lim and Wang teaches the system of claim 1. Lim further teaches wherein the sensor data is collected from at least one of a light detection and ranging (LIDAR) sensor, a radio detection and ranging (RADAR) sensor, a time-of- flight (TOF) sensor, and a camera sensor (At least ¶ [0050] “radar sensor 201 may radiate a laser beam and may detect an obstacle located around the vehicle…”).
Regarding claim 3, the combination of Lim and Wang teaches the system of claim 1. Wang further teaches wherein the sensor data comprises fused sensor data (At least ¶ [0029] “The sensing data obtained by the sensing system 120 can be processed by a data processing system 130 of AV 100. For example, the data processing system 130 can include a perception system 132. The perception system 132 can be configured to detect and track objects in the driving environment 110 and to recognize the detected objects … In some implementations, the perception system 132 can use the lidar data in combination with the data captured by the camera(s) 129.”).
Regarding claim 4, the combination of Lim and Wang teaches the system of claim 1. Lim further teaches wherein the fog proxy level comprises a value ranging from 0 to 20 (At least ¶ [0084] “a learning device 30 of FIG. 2 may divide a fog level in a fog image into three grades (i.e., grade 1, grade 2, and grade 3), may divide an illumination level in the fog image into three grades (i.e., grade 1, grade 2, and grade 3), and may divide a fog situation into ninth grades by means of a combination of the fog level and the illumination level, as classification parameters based on deep learning.” Lim discloses the claimed invention except for the fog proxy level being limited to a value ranging from 0-20. It would have been an obvious matter of design choice to quantify the fog proxy levels in a manner suiting the fog intensity. Applicant has not disclosed that the arbitrary range of 0-20 solves any stated problem or is for any particular purpose, and it appears that the invention would perform equally as well with three grade fog level system as described in Lim. Additionally, Applicant specification [0017] states “the determined fog proxy level can be expressed in a numerical value. For example, in one example embodiment, the determined fog proxy level can range from 0 to 20, with 0 indicating no fog in the environment and 20 indicating essentially white- out conditions (i.e., zero vision). While the range of 0 to 20 is provided in this example, it is understood that the fog proxy level can be represented by any suitable numerical range. In some cases, a very low fog proxy level (i.e., less than 0.8) can be considered as clear conditions wherein no speed adjustments are necessary. In some cases, a very high fog proxy level (i.e., more than 6) can be considered too dangerous to drive in, and the AV can be grounded until the fog proxy level falls below a certain threshold value. The fog proxy level can continue to be monitored until it falls below a certain threshold that is considered safe to proceed. In some examples, the fog proxy level can be divided into various levels and given semantic labels that indicate the real-world intensity of the fog (i.e., "no fog," "very light fog," "light fog," "medium fog," "high fog," "very high fog," etc.).” Bold emphasis added by Examiner.).
Regarding claim 5, the combination of Lim and Wang teaches the system of claim 1. Wang further teaches wherein the fog proxy level is correlated with the track spawning range based on a mathematical model (At least ¶ [0041] “Conversely, by fitting the detected intensity I.sub.R(t) (or I.sub.MAX, I.sub.AV, τ, etc.) to the predictions of the modeling component, the VRM characterization module 133-2 can determine an extinction coefficient β of the VRM. The extinction coefficient β can characterize exponential attenuation of the light signal (of a given frequency) with distance x travelled in the VRM: I(x)=I(0)e.sup.−βx, where I(x) is the initial intensity.” and ¶ [0042] “Using the determined extinction coefficient #3 (or other similar parameter(s)), the processing system of the AV can determine the maximum visibility range for various types of targets, such as vehicles, pedestrians, road signs, street lights, buildings and structures, and so on. For example, the maximum visibility range of the lidar(s) 122 can be determined based on the reduction in the returned signal intensity by e.sup.−2βL (compared with a situation when no VRM is present), where L is the distance from the lidar transceiver to the target and 2L is the total distance of the beam travel. Similarly, the maximum visibility range can be determined for camera(s) 129 by taking into account that the amount of light registered by the camera(s) is reduced by the factor e.sup.−β.sup.1.sup.L (since the optical path of the light detected by the camera has length L of travel in only one direction). Note that the extinction coefficient β.sub.1 for camera detection can be different from the extinction coefficient β for lidar detection (if the two types of devices can use light of different wavelengths).”).
Regarding claim 6, the combination of Lim and Wang teaches the system of claim 1. Lim further teaches wherein the fog proxy level is correlated with the track spawning range using a machine learning model (At least ¶ [0070] “the control device 100 may generate a classification model of classifying various fog situations into a plurality of levels based on deep learning, may determine visible distances of the driver, which correspond to the plurality of levels. may detect a visible distance of the driver, which corresponds to a fog situation of a road on which the vehicle is currently traveling”) trained on real-world fog data (At least ¶ [0079] “The input device 20 may input various types of fog images as train data to the learning device 30.”).
Regarding claim 7, the combination of Lim and Wang teaches the system of claim 1. Wang further teaches wherein the speed of the AV is adjusted to a speed correlated with a stopping distance less than a distance of the track spawning range (At least ¶ [0061] “the perception system of the AV can detect presence of fog in the driving environment and can further determine that camera visibility is 50 meters and that lidar visibility is 75 meters. The perception system can communicate this information to the control system (e.g., the AVCS 140). The control system can use this information for charting a driving path of the AV, including modifying (compared with good visibility conditions) the AV's speed regime, the way the AV stops, brakes, changes lanes, backs up, and the like. The control system can subsequently output instructions to powertrain, brakes, and steering 150, vehicle electronics 160, signaling 170, etc., to ensure that the AV follows the determined driving path.”), wherein the speed is continuously updated in real time such that the stopping distance of the AV is always less than the dynamically determined track spawning range as the fog proxy level changes (At least ¶ [0053] “The return points are data entries that are associated with a reflection of one of the emitted signals from an object of the environment or VRM. The return points can be generated in real time.” The return points (i.e. perception data) are generated in real-time, which are then used as input for the AV control process. Therefore, the speed/stopping distance of the AV is also updated in real-time. See FIG. 6.).
Claims 8-14 describe a method executed by the system in claims 1-7, thus are respectively rejected on the same basis.
Claims 15-20 describe a non-transitory computer-readable medium comprising at least one instruction which causes a processor to execute the steps detailed in claims 1-6, thus are respectively rejected on the same basis. Additionally, Lim teaches a non-transitory computer-readable medium comprising at least one instruction (At least ¶ [0045] “non-transitory computer readable media”).
Conclusion
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 Jonathan E Reinert whose telephone number is (571)272-1260. The examiner can normally be reached Mon - Thurs 7AM - 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, James J Lee can be reached at (571) 270-5965. 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.
/J.E.R./Examiner, Art Unit 3668
/BRIAN P SWEENEY/Primary Examiner, Art Unit 3668
Prosecution Insights