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 Interpretation
The term “substantially-straight” in the context of the claim and lane geometry is explained within the Applicant’s disclosure at least in paragraph [0076] as a lane having either no curvature or a curvature that is less than some threshold, which provides a standard by which one of ordinary skill could ascertain the boundary of this term, and thereby avoids a rejection of indefiniteness for containing a relative term.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5, 78, 13-18, and 20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 6-8, 11, and 14-18 of U.S. Patent No. 12,399,496. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims would be anticipated by the reference patent.
A table showing the claims of the instant application and the corresponding claim of Patent No. 12,399,496 side-by-side for comparison is shown below. All matching elements of the claim limitations appear in bold while non-matching elements of the claim limitations are not bolded.
Instant Application
Patent No. 12,399,496
1. A computer-implemented method comprising:
identifying a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry;
inferring that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
detecting at least one lane boundary of the lane in which the vehicle was driving at the given time;
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; and
based on the first and second lateral distances, determining a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
1. A computer-implemented method comprising:
identifying, based on steering-angle information for a vehicle having an associated sensor system for capturing sensor data, at least two times within a given period of operation of the vehicle when the vehicle was driving in a lane having substantially-straight lane geometry;
for each identified time when the vehicle was driving in a lane having substantially-straight lane geometry, determining a respective measure of a lateral offset between the vehicle's associated sensor system and a lateral reference point of the vehicle based on an inference that, because the lane had substantially straight lane geometry at the identified time, the vehicle was laterally centered within the lane at the identified time;
based on the respective measure of the lateral offset that is determined for each of the at least two identified times, determining the lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle, wherein determining the lateral
offset comprises aggregating the respective measures of the lateral offsets that are determined for the at least two identified times; and
deriving a trajectory for the vehicle based on a combination of both (i) sensor data captured by the vehicle's associated sensor system and (ii) the determined lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle.
6. The computer-implemented method of claim 5, wherein determining the respective measure of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for a given time within the given period of operation comprises:
detecting at least one lane boundary of a given lane in which the vehicle was driving at the given time;
determining a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system;
determining a second lateral distance between the at least one detected lane boundary and a lateral centerline of the given lane; and
based on the first lateral distance and the second lateral distance, determining the respective measure of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
As illustrated in the table above, all matching elements of the claim limitations appear in bold while non-matching elements of the claim limitations are not bolded.
Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions are directed to computer implemented methods determining lateral offsets of the vehicle’s sensor system, the difference being that the instant claim is constructed of limitations of the patent’s independent claim 1 and claim 6, where the patent’s claim 1 also recites identifying at least two times, rather than the instant’s one given time, but which nonetheless includes identifying a given time, the patent recites the incorporation of steering information, and then derives a trajectory based on this information. As such, each and every limitation of independent claim 1 of the instant claims can be found and fully anticipated within the claim limitations of Patent No. 12,399,496.
Addressing the remaining claims:
Instant Application
Patent No. 12,399,496
2. The computer-implemented method of claim 1, wherein identifying the given time when the vehicle was driving in a lane having substantially-straight lane geometry comprises:
determining a location of the vehicle's associated sensor system within a map at the given time; and
determining that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry.
3. The computer-implemented method of claim 1, further comprising identifying additional times within the given period of operation of the vehicle when the vehicle was driving in a lane having substantially-straight lane geometry
by:
localizing the vehicle's associated sensor system within a map encoded with lane geometry information, wherein the localizing produces a set of location points for the vehicle's associated sensor system within the map that each corresponds to a respective time during the given period of operation; and
for each of at least two location points in the set of location points:
obtaining lane geometry information for a road segment surrounding the location point;
based on the obtained lane geometry information, determining that the road segment surrounding the location point has less than a threshold extent of curvature; and
in response to the determining that the road segment surrounding the location point has less than the threshold extent of curvature, identifying the respective time corresponding to the location point as one additional time when the vehicle was driving in a lane having substantially-straight lane geometry.
3. The computer-implemented method of claim 2, wherein determining that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry comprises:
using one or both of lane geometry data or trajectory data encoded within the map to determine that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry.
3. The computer-implemented method of claim 1, further comprising identifying additional times within the given period of operation of the vehicle when the vehicle was driving in a lane having substantially-straight lane geometry
by:
localizing the vehicle's associated sensor system within a map encoded with lane geometry information, wherein the localizing produces a set of location points for the vehicle's associated sensor system within the map that each corresponds to a respective time during the given period of operation; and
for each of at least two location points in the set of location points:
obtaining lane geometry information for a road segment surrounding the location point;
based on the obtained lane geometry information, determining that the road segment surrounding the location point has less than a threshold extent of curvature; and
in response to the determining that the road segment surrounding the location point has less than the threshold extent of curvature, identifying the respective time corresponding to the location point as one additional time when the vehicle was driving in a lane having substantially-straight lane geometry.
5. The computer-implemented method of claim 1, wherein detecting the at least one lane boundary of the lane in which the vehicle was driving at the given time comprises:
based on an analysis of sensor data captured by the vehicle's associated sensor system at or near the given time, detecting at least one object that is indicative of a lane boundary.
7. The computer-implemented method of claim 6, wherein detecting the at least one lane boundary of the given lane in which the vehicle was driving at the given time comprises:
based on an analysis of sensor data captured by the vehicle's associated sensor system at or near the given time, detecting at least one object that is indicative of a lane boundary.
7. The computer-implemented method of claim 1, further comprising:
determining plurality of additional times when the vehicle was driving in a lane having substantially-straight lane geometry;
for each respective time of the plurality of additional times:
inferring that, because the lane had substantially-straight lane geometry at the respective time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the respective time;
detecting at least one lane boundary of the lane in which the vehicle was driving at the respective time;
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the respective time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the respective time; and
based on the first and second lateral distances, determining a respective measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the respective time; and
determining an estimate of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle based on the given and respective measures of the lateral offset determined for the given and additional times.
1. A computer-implemented method comprising:
identifying, based on steering-angle information for a vehicle having an associated sensor system for capturing sensor data, at least two times within a given period of operation of the vehicle when the vehicle was driving in a lane having substantially-straight lane geometry;
for each identified time when the vehicle was driving in a lane having substantially-straight lane geometry, determining a respective measure of a lateral offset between the vehicle's associated sensor system and a lateral reference point of the vehicle based on an inference that, because the lane had substantially straight lane geometry at the identified time, the vehicle was laterally centered within the lane at the identified time;
based on the respective measure of the lateral offset that is determined for each of the at least two identified times, determining the lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle, wherein determining the lateral
offset comprises aggregating the respective measures of the lateral offsets that are determined for the at least two identified times; and
deriving a trajectory for the vehicle based on a combination of both (i) sensor data captured by the vehicle's associated sensor system and (ii) the determined lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle.
6. The computer-implemented method of claim 5, wherein determining the respective measure of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for a given time within the given period of operation comprises:
detecting at least one lane boundary of a given lane in which the vehicle was driving at the given time;
determining a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system;
determining a second lateral distance between the at least one detected lane boundary and a lateral centerline of the given lane; and
based on the first lateral distance and the second lateral distance, determining the respective measure of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
8. The computer-implemented method of claim 7, wherein determining the estimate of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle based on the given and respective measures of the lateral offset determined for the given and additional times comprises:
aggregating the given and respective measures of the lateral offset determined for the given and additional times.
1. A computer-implemented method comprising:
identifying, based on steering-angle information for a vehicle having an associated sensor system for capturing sensor data, at least two times within a given period of operation of the vehicle when the vehicle was driving in a lane having substantially-straight lane geometry;
for each identified time when the vehicle was driving in a lane having substantially-straight lane geometry, determining a respective measure of a lateral offset between the vehicle's associated sensor system and a lateral reference point of the vehicle based on an inference that, because the lane had substantially straight lane geometry at the identified time, the vehicle was laterally centered within the lane at the identified time;
based on the respective measure of the lateral offset that is determined for each of the at least two identified times, determining the lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle, wherein determining the lateral
offset comprises aggregating the respective measures of the lateral offsets that are determined for the at least two identified times; and
deriving a trajectory for the vehicle based on a combination of both (i) sensor data captured by the vehicle's associated sensor system and (ii) the determined lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle.
13. The computer-implemented method of claim 1, further comprising:
determining a longitudinal offset between the vehicle's associated sensor system and a longitudinal reference point of the vehicle based on sensor data captured by the vehicle's associated sensor system and information regarding physical dimensions of the vehicle.
11. The computer-implemented method of claim 1, further comprising:
determining a longitudinal offset between the vehicle's associated sensor system and a longitudinal reference point of the vehicle based on sensor data captured by the vehicle's associated sensor system during the given period of operation and information regarding the vehicle's physical dimensions.
14. The computer-implemented method of claim 1, further comprising:
determining elevation information for a vertical reference point related to the vehicle based on one or more of (i) map data, (ii) sensor data captured by the vehicle's associated sensor system, or (iii) information regarding physical dimensions of the vehicle.
8. The computer-implemented method of claim 1, further comprising:
determining elevation information for a vertical reference point related to the vehicle during the given period of operation based on one or more of (i) map data, (ii) sensor data captured by the vehicle's associated sensor system during the given period of operation, or (iii) information regarding the vehicle's physical dimensions.
Claims 15 and 16 mirror claim 1 and therefore are found to be double patenting for the same reasons as claim 1. Dependent claims 17, 18, and 20 mirror claims 2, 3, and 5 and therefore are similarly found to be double patenting.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 will be treated as a representative claim and reads:
A computer-implemented method comprising:
identifying a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry;
inferring that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
detecting at least one lane boundary of the lane in which the vehicle was driving at the given time;
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; and
based on the first and second lateral distances, determining a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. See MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method comprising:
identifying a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry [mental process/step];
inferring that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time [mental process/step];
detecting at least one lane boundary of the lane in which the vehicle was driving at the given time;
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time [mental process/step and mathematical concept]; and
based on the first and second lateral distances, determining a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time [mental process/step and mathematical concept].
The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind and mathematical concepts. For example, “identifying…” in the context of the claim encompasses a person forming a simple judgment of a time. Similarly, “inferring…” encompasses that same person forming another simple judgment of an assumption that the vehicle is centrally located in the lane based on the previous judgement. “determining…” and “based on…” within the context of the claim encompass both potential mental processes and mathematical concepts. “determining…” includes simple observation and evaluation by the very same human of distances which may include mathematically calculating the distances based on coordinates of captured data. “based on…” encompasses further evaluation by the person to find another value from the other values, which includes mathematical calculation to produce the evaluation result. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. See MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A computer-implemented method comprising:
identifying a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry;
inferring that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
detecting at least one lane boundary of the lane in which the vehicle was driving at the given time [pre-solution activity (data gathering)];
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; and
based on the first and second lateral distances, determining a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “detecting…”, the Examiner submits this limitation is recited at a high level of generality, as a general means of gathering vehicle and road condition data for use in the evaluating step, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of detecting is an insignificant extra-solution activity. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claims 2-14 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. In particular, the dependent claims recite additional abstract concepts or additional data gathered, which do not amount to integration of the abstract concepts into practical application. Therefore, dependent claims 2-14 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
In particular, in regards to dependent claim 12, the claim recites deriving a trajectory based on the collected data, which amounts to planning, another abstract concept, but falls short of practical application, such as operating the vehicle based on the trajectory.
Independent claims 15 and 16 and dependent claims 17-20 recite similar features to independent claim 1 and dependent claims 2-14 and therefore are found not patent eligible under the same rationale as above.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1-5, 7, and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 20070069874), in view of Hashimoto et al. (JP 2018034719) and Natroshvili et al. (US 20160284087).
In regards to claim 1, Huang teaches a computer-implemented method comprising: (Fig 3, 4, 6.)
identifying a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry; ([0031] road curvature is compared with a threshold road curvature, when below the threshold curvature, the road is determined to be straight at a particular time, which occurs at steps 202 and 204b.)
detecting at least one lane boundary of the lane in which the vehicle was driving at the given time; ([0029] in step 102, lane markings are recognized in vision image data for the current time.)
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; ([0027], [0033] a distance to the lane marking from the sensor position is determined as a lateral deviation or displacement and [0034] a half lane width is determined which is a lateral distance between a lane boundary and the lane centerline at each time.) and
Huang does not teach:
inferring that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
based on the first and second lateral distances, determining a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
However, Hashimoto teaches making an assumption that the host vehicle returns to the travelling lane while the vehicle travels straight through lane sections, including particularly straight lane sections, when the vehicle does not deviate from the lane (Pages 11, 22). This makes an assumption that when the vehicle is not leaving the lane, it travels centrally while the lane boundaries are straight, thereby inferring that because the lane boundaries are straight, the vehicle travels alight with the lateral centerline of the lane.
Further, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, by incorporating the teachings of Hashimoto and Natroshvili, such that an assumption is made that because the vehicle travels through a straight lane without deviation, the returns to the center of the lane, and an extrinsic matrix of the camera of the vehicle is determined particularly taking as input the environmental features around the vehicle includes at least the lateral distance between the sensor and lane boundary and the distance between the lane boundary and the centerline of the lane, which then produces the position of the camera.
The motivation to make such an assumption is that, as acknowledged by Hashimoto, this allows for improved stability and driving comfort of the vehicle’s occupants (Page 11). The motivation to determine a camera’s extrinsic matrix is that, as acknowledged by Natroshvili, this allows for improved calibration which improves precision ([0005], [0024], [0026]).
In regards to claim 2, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 1, wherein identifying the given time when the vehicle was driving in a lane having substantially-straight lane geometry comprises:
determining a location of the vehicle's associated sensor system within a map at the given time; ([0038] vehicle is localized within map, including localizing the sensor system.) and
determining that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry. ([0031], [0038] vehicle is localized within map, including localizing the sensor system within segment of lane that has straight lane geometry below a curvature threshold.)
In regards to claim 3, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 2, wherein determining that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry comprises:
using one or both of lane geometry data or trajectory data encoded within the map to determine that the location of the vehicle's associated sensor system within the map at the given time is a location within a segment of a lane that has substantially-straight lane geometry. ([0031], [0038] curvature of lane markings may be derived from image data and map data, and the vehicle, including its sensor system, is localized within particular lane segments, including using lane geometry data to determine the curvature of a lane segment of the vehicle is below a threshold and thereby considered to be straight.)
In regards to claim 4, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 1, wherein identifying the given time when the vehicle was driving in a lane having substantially-straight lane geometry comprises:
identifying the given time when the vehicle was driving in a lane having substantially-straight lane geometry based on sensor data captured by the vehicle's associated sensor system. ([0031] road curvature is derived based on image data from vehicle sensors and map data, which is then checked against threshold to determine if road should be considered straight at current time.)
In regards to claim 5, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 1, wherein detecting the at least one lane boundary of the lane in which the vehicle was driving at the given time comprises:
based on an analysis of sensor data captured by the vehicle's associated sensor system at or near the given time, detecting at least one object that is indicative of a lane boundary. ([0031] road curvature is derived based on image data from vehicle sensors viewing lane markings, which is then checked against threshold to determine if road should be considered straight at current time. Lane markings are objects indicative of lane boundaries.)
In regards to claim 7, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 1, further comprising:
determining plurality of additional times when the vehicle was driving in a lane having substantially-straight lane geometry; (Figs 3, 4, 6, each method culminates in a return step which causes the method to be repeated at a next time step. [0031] road curvature is compared with a threshold road curvature, when below the threshold curvature, the road is determined to be straight at a particular time, which occurs at steps 202 and 204b. As the method repeats, whenever the curvature is checked and determined to be considered straight, additional times are identified. This determines a plurality of additional times when the vehicle was driving in straight lane geometry.)
for each respective time of the plurality of additional times: (Figs 3, 4, 6, each method culminates in a return step which causes the method to be repeated at a next time step.)
detecting at least one lane boundary of the lane in which the vehicle was driving at the respective time; ([0029] in step 102, lane markings are recognized in vision image data for the current time.)
determining (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the respective time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the respective time; ([0027], [0033] a distance to the lane marking from the sensor position is determined as a lateral deviation or displacement and [0034] a half lane width is determined which is a lateral distance between a lane boundary and the lane centerline at each time.) and
Hashimoto teaches making an assumption that the host vehicle returns to the travelling lane while the vehicle travels straight through lane sections, including particularly straight lane sections, when the vehicle does not deviate from the lane (Pages 11, 22). This makes an assumption that when the vehicle is not leaving the lane, it travels centrally while the lane boundaries are straight, thereby inferring that because the lane boundaries are straight, the vehicle travels alight with the lateral centerline of the lane.
Further, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by further incorporating the teachings of Hashimoto and Natroshvili, such that the assumption is made that because the vehicle travels through a straight lane without deviation, the returns to the center of the lane, and an extrinsic matrix of the camera of the vehicle is determined particularly taking as input the environmental features around the vehicle includes at least the lateral distance between the sensor and lane boundary and the distance between the lane boundary and the centerline of the lane, which then produces the position of the camera, for each time.
The motivations to do so are the same as acknowledged by Hashimoto and Natroshvili in regards to claim 1.
In regards to claim 12, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 7.
Huang also teaches a path is projected based on the maneuver of the vehicle which is based on the heading and the observed lane boundaries of the vehicle’s lane ([0021], [0022], [0032]). This projects the path of the vehicle based on the position of the sensor and the captured sensor information
Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by further incorporating the teachings Natroshvili, such that the position of the sensor is determined through determining the extrinsic matrix of the sensor which is then further used to control the vehicle by planning the vehicle’s path.
The motivation to do so is the same as acknowledged by Natroshvili in regards to claim 1.
In regards to claim 13, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). This includes lateral and longitudinal offsets of the camera. The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle and the curves of the front edge of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]). The features of the vehicle include information regarding physical dimensions of the vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by further incorporating the teachings of Natroshvili, such that when the extrinsic matrix of the camera is determined, it determines the position of the camera further including a longitudinal position relative to determined feature points of the vehicle based on sensor information.
The motivation to do so is the same as acknowledged by Natroshvili in regards to claim 1.
In regards to claim 14, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). This includes lateral and longitudinal offsets of the camera and vertical coordinates of points of the vehicle. The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle and the curves of the front edge of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]). The features of the vehicle include information regarding physical dimensions of the vehicle. This determines the vertical coordinates of at least the camera origin point as a reference point for the vehicle coordinate frame ([0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by further incorporating the teachings of Natroshvili, such that when the extrinsic matrix of the camera is determined, it determines the position of the camera further including a vertical reference position of the vehicle using sensor data from the vehicle’s sensor system and physical dimension of the vehicle.
The motivation to do so is the same as acknowledged by Natroshvili in regards to claim 1.
In regards to claim 15, Huang teaches a non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to: ([0019], [0020], [0039] system executes processing and therefore necessarily must have processor or component acting as processor with instructions stored in memory to be executed by processor.)
identify a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry; ([0031] road curvature is compared with a threshold road curvature, when below the threshold curvature, the road is determined to be straight at a particular time.)
detect at least one lane boundary of the lane in which the vehicle was driving at the given time; ([0029] in step 102, lane markings are recognized in vision image data for the current time.)
determine (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; ([0027], [0033] a distance to the lane marking from the sensor position is determined as a lateral deviation or displacement and [0034] a half lane width is determined which is a lateral distance between a lane boundary and the lane centerline at each time.) and
Huang does not teach:
infer that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
based on the first and second lateral distances, determine a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
However, Hashimoto teaches making an assumption that the host vehicle returns to the travelling lane while the vehicle travels straight through lane sections, including particularly straight lane sections, when the vehicle does not deviate from the lane (Pages 11, 22). This makes an assumption that when the vehicle is not leaving the lane, it travels centrally while the lane boundaries are straight, thereby inferring that because the lane boundaries are straight, the vehicle travels alight with the lateral centerline of the lane.
Further, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control instructions of Huang, by incorporating the teachings of Hashimoto and Natroshvili, such that an assumption is made that because the vehicle travels through a straight lane without deviation, the returns to the center of the lane, and an extrinsic matrix of the camera of the vehicle is determined particularly taking as input the environmental features around the vehicle includes at least the lateral distance between the sensor and lane boundary and the distance between the lane boundary and the centerline of the lane, which then produces the position of the camera.
The motivation to make such an assumption is that, as acknowledged by Hashimoto, this allows for improved stability and driving comfort of the vehicle’s occupants (Page 11). The motivation to determine a camera’s extrinsic matrix is that, as acknowledged by Natroshvili, this allows for improved calibration which improves precision ([0005], [0024], [0026]).
In regards to claim 16, Huang teaches a computing platform comprising: (Figs 2, 7, 8.)
at least one processor; ([0019], [0020] system executes processing and therefore necessarily must have processor or component acting as processor.)
at least one non-transitory computer-readable medium; ([0019], [0020], [0039] system executes processing and therefore necessarily must have processor or component acting as processor with instructions stored in memory to be executed by processor.) and
program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to: ([0019], [0020], [0039] system executes processing and therefore necessarily must have processor or component acting as processor with instructions stored in memory to be executed by processor.)
identify a given time when a vehicle having an associated sensor system was driving in a lane having substantially-straight lane geometry; ([0031] road curvature is compared with a threshold road curvature, when below the threshold curvature, the road is determined to be straight at a particular time.)
detect at least one lane boundary of the lane in which the vehicle was driving at the given time; ([0029] in step 102, lane markings are recognized in vision image data for the current time.)
determine (i) a first lateral distance between the at least one detected lane boundary and the vehicle's associated sensor system at the given time and (ii) a second lateral distance between the at least one detected lane boundary and the lateral centerline of the lane at the given time; ([0027], [0033] a distance to the lane marking from the sensor position is determined as a lateral deviation or displacement and [0034] a half lane width is determined which is a lateral distance between a lane boundary and the lane centerline at each time.) and
Haung does not teach:
infer that, because the lane had substantially-straight lane geometry at the given time, the vehicle was laterally positioned in alignment with a lateral centerline of the lane at the given time;
based on the first and second lateral distances, determine a given measure of a lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle for the given time.
However, Hashimoto teaches making an assumption that the host vehicle returns to the travelling lane while the vehicle travels straight through lane sections, including particularly straight lane sections, when the vehicle does not deviate from the lane (Pages 11, 22). This makes an assumption that when the vehicle is not leaving the lane, it travels centrally while the lane boundaries are straight, thereby inferring that because the lane boundaries are straight, the vehicle travels alight with the lateral centerline of the lane.
Further, Natroshvili teaches determining an extrinsic matrix for a camera mounted on a vehicle, where the extrinsic matrix includes the position and orientation of the camera ([0021]). The position is defined based upon any reference point of the vehicle, including particularly a lateral center of the vehicle ([0050]). The extrinsic matrix is determined based on constraint sets found from environmental features and features of the vehicle viewed by the camera mounted on the vehicle ([0023], [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control system of Huang, by incorporating the teachings of Hashimoto and Natroshvili, such that an assumption is made that because the vehicle travels through a straight lane without deviation, the returns to the center of the lane, and an extrinsic matrix of the camera of the vehicle is determined particularly taking as input the environmental features around the vehicle includes at least the lateral distance between the sensor and lane boundary and the distance between the lane boundary and the centerline of the lane, which then produces the position of the camera.
The motivation to make such an assumption is that, as acknowledged by Hashimoto, this allows for improved stability and driving comfort of the vehicle’s occupants (Page 11). The motivation to determine a camera’s extrinsic matrix is that, as acknowledged by Natroshvili, this allows for improved calibration which improves precision ([0005], [0024], [0026]).
In regards to claim 17, Haung, as modified by Hashimoto and Natroshvili, teaches the computing platform of claim 16.
Claim 17 recites a system having substantially the same features of claim 2 above, therefore claim 17 is rejected for the same reasons as claim 2.
In regards to claim 18, Haung, as modified by Hashimoto and Natroshvili, teaches the computing platform of claim 17.
Claim 18 recites a system having substantially the same features of claim 3 above, therefore claim 18 is rejected for the same reasons as claim 3.
In regards to claim 19, Haung, as modified by Hashimoto and Natroshvili, teaches the computing platform of claim 16.
Claim 19 recites a system having substantially the same features of claim 4 above, therefore claim 19 is rejected for the same reasons as claim 4.
In regards to claim 20, Haung, as modified by Hashimoto and Natroshvili, teaches the computing platform of claim 16.
Claim 20 recites a system having substantially the same features of claim 5 above, therefore claim 20 is rejected for the same reasons as claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Hashimoto and Natroshvili, in further view of Schwartz et al. (US 20160010984).
In regards to claim 6, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 1.
Huang, as modified by Hashimoto and Natroshvili, does not teach:
wherein the method is carried out in response to an indication of a potential change in a position of the vehicle's sensor system relative to the vehicle.
However, Schwartz teaches that portable devices within vehicles, such as smart phones, may have vehicle independent motion states, for example caused by the user handling the phone or the phone falling ([0001]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by incorporating the teachings of Schwartz, such that vehicle independent motion of the sensors of Huang are determined, and the methods, as modified, are then performed to at least further calibrate the sensor.
The motivation to do so is that, as acknowledged by Schwartz, this allows for improved detection of driving events ([0002]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Hashimoto and Natroshvili, in further view of Gagnon et al. (US 20180232909).
In regards to claim 8, Huang, as modified by Hashimoto and Natroshvili, teaches the computer-implemented method of claim 7, wherein determining the estimate of the lateral offset between the vehicle's associated sensor system and the lateral center of the vehicle based on the given and respective measures of the lateral offset determined for the given and additional times comprises:
Huang, as modified by Hashimoto and Natroshvili, does not teach:
aggregating the given and respective measures of the lateral offset determined for the given and additional times.
However, Gagnon teaches deriving extrinsic parameters for each individual image of multiple images and forming an average of the derived extrinsic parameters ([0041]) where the extrinsic parameters describe a camera’s rotation and translation ([0005], [0008]). Averaging requires some aggregation first to collect the data to be averaged.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Huang, as already modified by Hashimoto and Natroshvili, by incorporating the teachings of Gagnon, such that corresponding extrinsic parameters for two or more times are averaged, which averages the offsets of the camera and aggregates the data.
The motivation to do so is that, as acknowledged by Gagnon, this allows for accounting for deterioration and thereby improved detection of objects ([0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
O’Donnell (US 20200319646) teaches determining an offset between a sensor and a lateral centerline of the vehicle the sensor is mounted on.
Westmacott et al. (US 20210342600) teaches determining a sensor position on a vehicle.
Tietze et al. (US 20190285729) teaches calibrating a vehicle mounted radar by determining a lateral offset of the radar relative to the vehicle’s center axis.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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