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 .
Drawings
The drawings are objected to because of the following minor informalities: the drawings are objected to because of the use of inverted commas in reference numbers 102’ and 110’ in Fig. 1 (see 37 C.F.R. 1.84(p)(1) and (u)(2)). It is recommended to use reference numbers 102A and 110A, for example.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: the specification is objected to because of the use of inverted commas in reference numbers 102’ and 110’ in paragraphs [0046] and [0049] (see 37 C.F.R. 1.84(p)(1) and (u)(2)). It is recommended to use reference numbers 102A and 110A, for example.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 13-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Lin (US 2022/0357178 A1).
Regarding claim 1, Lin discloses a vehicle (In paragraph [0074], Lin discloses that a vehicle is provided, which includes any one of the lane edge extraction apparatuses as described or any one of the autonomous driving systems as described), comprising:
one or more memories (In paragraph [0075], Lin discloses that there is provided a computer-readable storage medium storing instructions, where the instructions, when executed by a processor, cause the processor to perform any one of the lane edge extraction methods as described); and
one or more processors communicatively coupled to the one or more memories (In paragraph [0075], Lin discloses that there is provided a computer-readable storage medium storing instructions, where the instructions, when executed by a processor, cause the processor to perform any one of the lane edge extraction methods as described), the one or more processors, either alone or in combination, configured to:
obtain, at a first timestamp, one or more first components of a high-definition (HD) map represented by a first set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame);
obtain, at a second timestamp subsequent to the first timestamp, one or more second components of the HD map represented by a second set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame); and
determine one or more current components of the HD map at the second timestamp based at least in part on an association between the one or more first components of the HD map and the one or more second components of the HD map (In paragraph [0037], Lin discloses that observation edge points can be extracted from the current frame, and are used to be continued to the tracking edge points of the immediately preceding frame, to supplement information of the lane edges brought about by the vehicle 601 traveling to a new position, and where there may be differences in a coincident area between some observation edge points in the current frame and some tracking edge points in the immediately preceding frame, and in this case, these tracking edge points can be corrected by using the observation edge points, thereby eliminating accumulated errors; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame).
Regarding claim 13, Lin further discloses wherein the one or more processors, either alone or in combination, are further configured to:
obtain, at a third timestamp subsequent to the second timestamp, one or more third components of the HD map represented by a third set of polylines (In paragraph [0060], Lin discloses that the tracking edge points of the current frame will be input into the processing process of an immediately following frame, and the process can be repeated); and
determine the one or more current components of the HD map at the third timestamp based at least in part on an association between the one or more first components of the HD map, the one or more second components of the HD map, and the one or more third components of the HD map (In paragraph [0037], Lin discloses that observation edge points can be extracted from the current frame, and are used to be continued to the tracking edge points of the immediately preceding frame, to supplement information of the lane edges brought about by the vehicle 601 traveling to a new position, and where there may be differences in a coincident area between some observation edge points in the current frame and some tracking edge points in the immediately preceding frame, and in this case, these tracking edge points can be corrected by using the observation edge points, thereby eliminating accumulated errors; in paragraph [0060], Lin discloses that the tracking edge points of the current frame will be input into the processing process of an immediately following frame, and the process can be repeated).
Regarding claim 14, Lin further discloses wherein:
the first timestamp corresponds to a first frame of the HD map (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame), and
the second timestamp corresponds to a second frame of the HD map (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame).
Regarding claim 15, Lin further discloses wherein a current frame of the HD map includes the one or more current components of the HD map (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated).
Regarding claim 16, Lin further discloses wherein the one or more current components of the HD map comprise: one or more lane predictions (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence, and continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame).
Regarding claim 17, Lin further discloses wherein the one or more processors, either alone or in combination, are further configured to:
perform one or more driving maneuvers based at least in part on the one or more current components of the HD map (In paragraph [0003], Lin discloses that functions such as lateral control for driver assistance are highly dependent on the quality of lane lines on a road; in paragraphs [0033], Lin discloses that high-quality lane edge information will be generated based on defective marking images obtained by the image obtaining apparatus 602 in the examples of this application, for calling by a processing device such as an onboard computer required for computer vision-assisted/autonomous driving functions).
Regarding claim 18, Lin further discloses wherein the one or more driving maneuvers comprises:
driving straight (In paragraph [0003], Lin discloses that functions such as lateral control for driver assistance are highly dependent on the quality of lane lines on a road; in paragraphs [0033], Lin discloses that high-quality lane edge information will be generated based on defective marking images obtained by the image obtaining apparatus 602 in the examples of this application, for calling by a processing device such as an onboard computer required for computer vision-assisted/autonomous driving functions).
Regarding claim 19, Lin discloses a method performed by a vehicle (In paragraph [0074], Lin discloses that a vehicle is provided, which includes any one of the lane edge extraction apparatuses as described or any one of the autonomous driving systems as described), comprising:
obtaining, at a first timestamp, one or more first components of a high-definition (HD) map represented by a first set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame);
obtaining, at a second timestamp subsequent to the first timestamp, one or more second components of the HD map represented by a second set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame); and
determining one or more current components of the HD map at the second timestamp based at least in part on an association between the one or more first components of the HD map and the one or more second components of the HD map (In paragraph [0037], Lin discloses that observation edge points can be extracted from the current frame, and are used to be continued to the tracking edge points of the immediately preceding frame, to supplement information of the lane edges brought about by the vehicle 601 traveling to a new position, and where there may be differences in a coincident area between some observation edge points in the current frame and some tracking edge points in the immediately preceding frame, and in this case, these tracking edge points can be corrected by using the observation edge points, thereby eliminating accumulated errors; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame).
Regarding claim 20, Lin discloses a vehicle (In paragraph [0074], Lin discloses that a vehicle is provided, which includes any one of the lane edge extraction apparatuses as described or any one of the autonomous driving systems as described), comprising:
means for obtaining, at a first timestamp, one or more first components of a high-definition (HD) map represented by a first set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame);
means for obtaining, at a second timestamp subsequent to the first timestamp, one or more second components of the HD map represented by a second set of polylines (In paragraphs [0034-0036], Lin discloses a lane edge extraction method including continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame); and
means for determining one or more current components of the HD map at the second timestamp based at least in part on an association between the one or more first components of the HD map and the one or more second components of the HD map (In paragraph [0037], Lin discloses that observation edge points can be extracted from the current frame, and are used to be continued to the tracking edge points of the immediately preceding frame, to supplement information of the lane edges brought about by the vehicle 601 traveling to a new position, and where there may be differences in a coincident area between some observation edge points in the current frame and some tracking edge points in the immediately preceding frame, and in this case, these tracking edge points can be corrected by using the observation edge points, thereby eliminating accumulated errors; see also paragraph [0033] where Lin discloses that “high-quality” lane edge information will be generated; see also paragraphs [0034] and [0051] where Lin discloses capturing a sequence of edge images at a fixed time interval, and where the current positions of the tracking edge points of the immediately preceding frame may be determined based on a speed and a yaw angle of a vehicle and a time difference between the immediately preceding frame and the current frame).
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:
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 2 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 2022/0357178 A1), in view of Yuan (US 2025/0200765 A1).
Regarding claim 2, Lin does not explicitly disclose wherein the one or more processors, either alone or in combination, are further configured to:
determine a low-dimensional representation of each polyline from the first set of polylines representing the one or more first components of the HD map;
determine a low-dimensional representation of each polyline from the second set of polylines representing the one or more second components of the HD map; and
determine the association between the one or more first components of the HD map and the one or more second components of the HD map based at least in part on the low-dimensional representation of each polyline from the first set of polylines and the low-dimensional representation of each polyline from the second set of polylines.
However, Yuan teaches wherein the one or more processors, either alone or in combination, are further configured to:
determine a low-dimensional representation of each polyline from the first set of polylines representing the one or more first components of the HD map (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target);
determine a low-dimensional representation of each polyline from the second set of polylines representing the one or more second components of the HD map (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target); and
determine the association between the one or more first components of the HD map and the one or more second components of the HD map based at least in part on the low-dimensional representation of each polyline from the first set of polylines and the low-dimensional representation of each polyline from the second set of polylines (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target; in paragraphs [0076-0077], Yuan teaches that the tracking client searches for matching reidentification data items among the received first reidentification data items, and if the underlying feature vectors are discrete-valued or otherwise defined in such manner (e.g., projection on low-dimensional subspace, rounding to integer values) that they shall be considered to match only in the case of complete equality (2), then the reidentification data items too match if all their components are equal).
Yuan is considered to be analogous to the claimed invention in that they both pertain to utilizing low-dimensional representations for feature recognition and object tracking. It would be obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Yuan with the vehicle as disclosed by Lin where the Examiner understands that the use of low-dimensional representations is well understood in the art, and may be implemented without undue experimentation, and with a reasonable expectation of success and predictable results. Doing so may advantageously increase computational efficiency, for example, by decreasing the computational complexity of analyzing the relevant data while maintaining its utility.
Regarding claim 5, Yuan further teaches wherein the one or more processors, either alone or in combination, are further configured to:
apply a machine learning model to each polyline from the first set of polylines representing the one or more first components of the HD map to obtain the low-dimensional representation of each polyline from the first set of polylines (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target; in paragraph [0057], Yuan teaches that the feature vectors are computed by a machine-learning model, such as a convolutional neural network (CNN), which has been trained to mimic correct or human-like reidentification decisions); and
apply the machine learning model to each polyline from the second set of polylines representing the one or more second components of the HD map to obtain the low-dimensional representation of each polyline from the second set of polylines (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target; in paragraph [0057], Yuan teaches that the feature vectors are computed by a machine-learning model, such as a convolutional neural network (CNN), which has been trained to mimic correct or human-like reidentification decisions).
Regarding claim 6, Yuan further teaches wherein the machine learning model comprises a series of one-dimensional convolutional neural networks (In paragraph [0052], Yuan teaches that a feature vector may be described as a low-dimensional representation of the visual appearance of the tracking target; in paragraph [0057], Yuan teaches that the feature vectors are computed by a machine-learning model, such as a convolutional neural network (CNN), which has been trained to mimic correct or human-like reidentification decisions).
Allowable Subject Matter
Claims 3-4 and 7-12 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang (US 12,547,906 B2) teaches a method, device, and program product for training model, including correcting, in the vehicle polar coordinate system, tracking edge points of the immediately preceding frame by using the observation edge points of the current frame, to obtain the temporary tracking edge points; and map the temporary tracking edge points into the vehicle rectangular coordinate system.
Chang (US 2025/0189658 A1) discloses sensor fusion and object tracking system and method thereof including fusing the low-dimensional 2D radar information to compensate for the blind zone of detection.
Ra (US 2025/0093468 A1) teaches an apparatus for controlling vehicle and method thereof including tracking the first reference point in regions divided by grids during frames of a designated section.
Ho (US 2025/0087317 A1) teaches predicting unobserved quantitative measures using machine learning, including where training the machine learning model may include, via one or more processors, an encoder to map one or more portions of the training images to the continuous latent space embedding representing a compressed low dimensional representation of the one or more training images.
Patsekin (US 2024/0338830 A1) teaches systems and methods for multiple-object tracking, where the template feature extraction model can extract embeddings, also referred to as low-dimensional representations (e.g., vectors) from the one or more templates, and where extracted features may be the same as extracted embeddings or represented by embeddings (e.g., vector representations).
Chen (US 2019/0325223 A1) teaches tracking objects with multiple cues where, based on the accumulated affinity score, whether a particular pair of objects refer to the same object or cannot be the same object is determined.
Peleg (US 8,949,235 B2) teaches methods and systems for producing a video synopsis using clustering, for example by building an affinity (similarity) matrix based on some similarity measure between every pair of objects.
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/HARRISON HEFLIN/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
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