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 .
Response to Arguments
Regarding the 35 USC § 103 rejection of claims 1-8 as being unpatentable over Dolgov et al. (US Pat. No. 11,061,404 B1) [hereinafter referred to as Dolgov], in view of Basich et al. (US Pat. No. 11,307,585 B2) [hereinafter referred to as Basich], the Applicant has elected to amend claims 1, 7, and 8. Therefore, the Examiner’s rejection in the previous Office Action based on 35 USC § 103 is rendered moot. However, due to said amendments, new reference Kume et al. (US Pat. Pub. No. 2024/0288883 A1) [hereinafter referred to as Kume] has been necessitated. Therefore, a new rejection based on 35 USC § 103 has been made and is discussed in detail below.
Regarding claim 1, the Applicant argues, see pg. 5, that neither Dolgov nor Basich, either individually or in a combination, disclose or teach a vehicle controller that changes the level of autonomous driving control ... to require a degree of the driver's involvement in driving, when other vehicles traveling around the vehicle indicate congestion. As stated in the previous action, Dolgov discloses including real-time traffic information (which would necessarily include if congestion occurs) and in col 13 ln 8-9 when minimum headway buffer (a time difference between a first vehicle immediately in front of the host vehicle and the third vehicle which is immediately in from of the first vehicle) makes it so the AV would require driver to take control, but does not specifically disclose the limitation when other vehicles traveling around the vehicle indicate congestion. However, new reference Basich teaches in col 20 ln 29-34 of probabilities that are calculated based upon operational environment information including event-related traffic conditions (emphasis added), which an event-related traffic condition is construed as comprising traffic congestion. Further, in col 3 ln 6-16, the system can detect obstacles (i.e., other vehicles) and communicate sensor information based on data collected from cameras to gain situational awareness.
Therefore, this argument is not persuasive.
Applicant further argues that the limitation determine, based on images received from the camera whether other vehicles around the vehicle indicate congestion is not taught by Dolgov nor Basich. However, not only do both Dolgov and Basich utilize image data to assess and analyze their surroundings, but [0057] of Kume teaches surround camera system includes a front camera, a rear camera, a left lateral camera, and a right lateral camera. The surround camera system captures an image of a road surface around the remote driving vehicle. Furthermore, in [0070] autonomous driving control execution unit recognizes a travel environment required for executing the autonomous driving including traffic congestion information acquired from the on-board communication device. The autonomous driving control execution unit successively determines the current autonomous driving level based on the recognized travel environment. When the determined autonomous driving level is autonomous driving level 3, the autonomous driving control execution unit sequentially determines whether the driver is prepared to take over driving operation in response to a request from the system.
Therefore, this argument is moot.
In regards to independent claims 7 and 8, Applicant argues, while differing in scope, these claims recite similar features to claim 1 and their rejections should likewise be withdrawn.
However, this argument is unpersuasive for the same reasons as given above.
Applicant argues the dependent claims are patentable by virtue of their dependency.
This argument is unpersuasive as each independent claim has been fully rejected for the reasons as given above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Dolgov et al. (US Pat No 11,061,404 Bl), hereinafter referred to as Dolgov, in view of Basich et al. (US Pat. No. 11,307,585 B2), hereinafter referred to as Basich, and Kume et al. (US Pat. Pub. No. 2024/0288883 A1), hereinafter referred to as Kume.
Regarding claim 1, Dolgov discloses:
A vehicle controller that executes autonomous driving control of a vehicle, comprising a processor configured to (See Fig. 1 below, a processor and memory used in an autonomous driving control system in an autonomous vehicle (AV) and column (col) 4 lines (ln) 21-30 memory providing executable instructions to a processor including non-transitory mediums):
control the vehicle to perform autonomous driving that does not require a driver's involvement (col 1 ln 28-42 discloses autonomous vehicles that do not require a driver’s involvement where the vehicle “essentially drives itself”);
receive images from a camera mounted on the vehicle(see Fig. 1 above item 154, detection system, col 6 ln 40-43 the detection system 154 may include lasers, sonar, radar, cameras or any other detection devices which record data which may be processed by computer onboard an AV, and col 8 ln63- col 9 ln8 where the AV may detect and identify other vehicles and correlate with image analysis to compare characteristics);
transmit the images into a classifier to detect other vehicles traveling around the vehicle and lane dividing lines of a lane being travelled by the vehicle (col 8 ln63- col 9 ln8 as discussed above and col 10 ln 30-43 vehicle identifies detected objects utilizing a machine learning classifier, which is construed by the Examiner as images being transferred to a classifier to detect other vehicles and lane lines being travelled by the vehicle);
receive a range signal from a range sensor mounted on the vehicle (col 6 ln 55-59 lasers employed by the AV measure distances between the vehicle and other objects and 64-66 that detection units are located on the front, side, and rear of AV),and
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determine a rear detection range within which an object is detectable behind the vehicle (Fig. 1 above items 138 tailgater threshold data and 140 a headway buffer, col 6 ln 55-59 as disclosed above and (Fig 6 below) where in col 6 ln 64-66 radar detection units located on rear of vehicle, col 7 ln 6-8 the parallax from two or more cameras is used to compute a distance to an object, and col 9 ln 14-31 where AV detects objects in same lane and behind the AV and identifies as a tailgater based on different thresholds including distance (col 9 ln 39-40)),
change the level of autonomous driving control applied to the vehicle to require a degree of the driver's involvement in driving (col 13 ln 8-9 when minimum headway buffer makes it so the AV would require driver to take control),
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the rear detection range has been less than a first distance threshold for more than a first predetermined time (Fig. 1 above items 138 tailgater threshold, col 8 ln 43-55 headway buffer data that pertains to distance between two vehicles and speed (e.g., the time it would take the following car to reach the lead vehicle) which defines the minimum headway buffer, which is construed by the Examiner as being capable of not only measuring for vehicles in front but also behind the AV as seen in col 10 ln 15-29 where the AV determines if there is a tailgater (e.g., a person following too closely in the same lane as AV) based on time and distance as sensed by the sensors).
Although Dolgov discloses including real-time traffic information, it however does not explicitly disclose:
when other vehicles traveling around the vehicle indicate congestion.
However, Basich teaches in col 20 ln 29-34 of probabilities that are calculated based upon operational environment information including event-related traffic congestion. Further, in col 3 ln 6-16, the system can detect obstacles (i.e., other vehicles) and communicate sensor information based on data collected from cameras to gain situational awareness. This is discussed utilizing a tele-operator who controls the actions of the AV but this information could necessarily be sent to an in-person operator who would intervene.
Therefore it would have been obvious to one of ordinary skill in the art of vehicle controls before the effective filing date of the current invention to modify the vehicle control method of Dolgov, by incorporating the congestion recognition teachings of Basich, such that the combination would provide for the predictable result of improved situational awareness and safety and to correspond the necessary autonomy level of the vehicle based on such distinctions to allow the operator to take control of the autonomous vehicle. However, Dolgov, as modified by Basich, although both references utilize image analysis to understand the immediate environment around the AV, they do not explicitly disclose:
determine, based on images received from the camera whether other vehicles around the vehicle indicate congestion.
However, [0057] of Kume teaches surround camera system includes a front camera, a rear camera, a left lateral camera, and a right lateral camera. The surround camera system captures an image of a road surface around the remote driving vehicle. Furthermore, in [0070] autonomous driving control execution unit recognizes a travel environment required for executing the autonomous driving including traffic congestion information acquired from the on-board communication device. The autonomous driving control execution unit successively determines the current autonomous driving level based on the recognized travel environment. When the determined autonomous driving level is autonomous driving level 3, the autonomous driving control execution unit sequentially determines whether the driver is prepared to take over driving operation in response to a request from the system.
Therefore it would have been obvious to one of ordinary skill in the art of vehicle controls before the effective filing date of the current invention to modify the vehicle control method of Dolgov, as already modified by Basich, by incorporating the image and autonomous level changing teachings of Kume, such that the combination would provide for the predictable result of improved determining congestion with the use of imagery and improved safety of the autonomous vehicle and operator.
Claims 7 and 8 recite a method and non-transitory recording medium having substantially the same features of claim 1 above, therefore claims 7 and 8 are rejected for the same reasons as claim 1.
Regarding claim 2, Dolgov, as modified by Basich and Kume, discloses:
The vehicle controller according to claim 1, wherein the processor is further configured to determine whether traffic around the vehicle is congested (col 6 ln 38-43 vehicle system can detect objects external to the AV including other vehicles, obstacles, traffic signals, etc., Fig. 1 detailed map data which is elaborated on in col 7 ln 39-44 where real time traffic information is included which is interpreted by the Examiner as being analogous to determining traffic congestion), and
wherein in the case where traffic around the vehicle is congested (col 7 ln 39-44 as seen above referring to real-time traffic information relative to the AV), the processor changes the level of autonomous driving control applied to the vehicle from a control level at which the driver's involvement is unnecessary to a control level at which the driver's involvement is necessary(col 13 ln 8-9 when minimum headway buffer alerts the driver and is capable of requiring the driver to take control of AV), when the rear detection range has been less than the first distance threshold for more than the first predetermined time (Fig. 1 Tailgater threshold and a minimum headway buffer, col 10 ln 15-29 where the AV determines if there is a tailgater (e.g., a person following too closely in the same lane as AV) based on time and distance as sensed by the sensors, and col 2 ln 60-61 use of distance and time thresholds).
Regarding claim 3, Dolgov, as modified by Basich and Kume, discloses:
The vehicle controller according to claim 1, wherein the processor is further configured to make, when the rear detection range has been less than a second distance threshold greater than the first distance threshold for more than a second predetermined time (col 10 ln 15-29 where the AV determines if there is a tailgater (e.g., a person following too closely in the same lane as AV) based on time and distance as sensed by the sensors, col 4 ln 1-8 AV observes another vehicle for a period of time and eliminates false positives for vehicles changing lanes or increasing the distance relative to the AV, interpreted by the Examiner as a second distance threshold not being exceeded that is larger than the first distance threshold, and col 9 ln 28-31 AV may determine tailgater including selecting between two or more different types (construed as meaning different scalars and also meaning time, distance, etc.) thresholds),
the amount of adjustment to the position of the vehicle in a lane being traveled by the vehicle in a direction traversing the lane less than when the rear detection range is not less than the second distance threshold (col 10 ln 44 – col 11 ln 4 discloses how an AV reacts to the tailgater, in which the Examiner is interpreting the actions of having certain movement and parameter adjustments based on thresholds being exceeded (i.e., see Fig. 6 below when the car is identified as a tailgater and has not exceeded the second distance threshold causing the AV to change lanes to allow the tailgater to pass, but if exceeded the headway buffers on either side of vehicle are adjusted (D2 decreases and D1 increases) to allow vehicle to aggressively accelerate to increase the gap to be beyond second threshold so to allow for a lane change)).
Regarding claim 4, Dolgov, as modified by Basich and Kume, discloses:
The vehicle controller according to claim 1, wherein the processor sets the first distance threshold for the case where a road being traveled by the vehicle has a road shoulder less than the first distance threshold for the case where the road being traveled by the vehicle does not have a road shoulder (col 10 ln 44 – col 11 ln 4 as seen in claim 4, paired with the situational analysis performed in col 6 ln 38-43 AV can detect obstacles in the roadways which is utilized in the processing steps, col 7 ln 67 – col 8 ln shoulder data is represented within map information that is utilized by the system to orient itself relative to various features of the roadway, and col 8 ln 50-55 where headway buffer can be varied based on external factors such as driving conditions and is capable of carrying a different set of thresholds such as whether a road has a shoulder or not, is interpreted by the Examiner as a capability of the system to identify whether there is a shoulder, understand that this is a different situation that affects the movement of the vehicle, and is capable of adjusting thresholds to accommodate for said situation).
Regarding claim 5, Dolgov, as modified by Basich and Kume, discloses:
The vehicle controller according to claim 1, wherein the processor is further configured to detect an object in an area around the vehicle, based on the sensor signal (see claim 1), and
wherein the processor determines whether a road shoulder of a road being traveled by the vehicle is passable by another vehicle in a most recent predetermined period, based on the result of detection of the object (see claim 4 regarding detecting a shoulder and claim 1 where distances between objects and map information is incorporated in situational analysis), and
sets the first distance threshold for the case where the road shoulder is passable by another vehicle less than the first distance threshold for the case where the road shoulder is impassable to another vehicle (see claim 4 rationale regarding situational awareness, map information and distance information being implemented by processor, and claim 3 where the capability of the AV to adjust distance thresholds based on said situation, also please see references cited but not used section in Conclusion Section(e.g., Jardine et al. (US 20220135039 A1))).
Regarding claim 6, Dolgov, as modified by Basich and Kume, discloses:
The vehicle controller according to claim 1, wherein the processor sets the first distance threshold smaller as a road being traveled by the vehicle has a narrower lane (see claim 4 rationale regarding situational awareness, map information being implemented by processor, and claim 3 where the capability of the AV to adjust distance thresholds based on said situation, which is construed by the Examiner as a capability to adjust the distance threshold based on a lane becoming narrower).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see:
Hayward (US Pat. Pub. No. 2025/0218302 A1) method of using travel event alerts and telematics associated with a vehicle.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEITH ALLEN VON VOLKENBURG whose telephone number is (703)756-5886. The examiner can normally be reached Monday-Friday 8:30 am-5:00 pm.
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, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Keith A von Volkenburg/Examiner, Art Unit 3665
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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