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 Amendment
The amendments to the drawings and specification have overcome the objections to the drawings and specification due to minor informality. In light of the amendments, the objections to the drawings and specification have been withdrawn.
Response to Arguments
Applicant's arguments, see the section titled Rejections under 35 U.S.C. 102 and 35 U.S.C. 103” starting on page 11 of the reply filed 07/21/2026, have been fully considered but they are not persuasive.
In the last paragraph beginning on page 12 of the reply filed 07/21/2026, Applicant argues that “Yuan does not disclose or suggest determining a low-dimensional representation of a polyline, much less a polyline that represents a component of an HD map”; however, the Examiner disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). It is the Examiner’s opinion that the combination of Lin and Yuan discloses determining low-dimensional representations of the polylines and determining an association between the components based on the low-dimensional representations of the polylines.
Furthermore, Applicant argues that “determining a low-dimensional representation of the visual appearance of a target in an image is not the same as determining a low-dimensional representation of a polyline” In the last paragraph beginning on page 12; however, the Examiner opines that the two concepts are not mutually exclusive. The Examiner understands the lane edge and observation edge points extracted from frame data as disclosed by Lin (see paragraphs [0033-0037] for example) to be an example of polylines which represent the visual appearance of a target. Using a low-dimensional representation of this data is taught by Yuan (see paragraphs [0052] and [0076-0077]) and is motivated in 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, where doing so may advantageously increase computational efficiency, for example, by decreasing the computational complexity of analyzing the relevant data while maintaining its utility.
In the first paragraph beginning on page 13 of the reply filed 07/21/2026, Applicant similarly argues that “determining a low-dimensional representation of the visual appearance of a visual target, as in Yuan, is not the same as determining a low-dimensional representation of tracking edge points, as in Lin.” However, Applicant is reminded that the references which are combined do not need to disclose identical subject matter in order to achieve a technical effect via combination, nor does one reference need to disclose the subject matter of the claim in its entirety individually, where a combination is considered. It is the Examiner’s opinion that the combination of Lin and Yuan discloses the content of claim 1. Additionally, 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.
Therefore, the grounds of rejection are maintained. See the rejections below.
Examiner further notes that, although not relied upon, additional pertinent prior art has been listed below in the Conclusion section that may further support Examiner’s position that the use of low-dimensional representations is well understood in the art of vehicle-related image recognition and feature detection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
See claim 20 which includes:
“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;
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; and
means for determining a low-dimensional representation of each polyline from the first set of polylines representing the one or more first components of the HD map;
means for determining 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
means for determining an 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; and
means for determining one or more current components of the HD map at the second timestamp based at least in part on the association between the one or more first components of the HD map and the one or more second components of the HD map.”
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 1, 5-6, and 13-20 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 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).
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).
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).
Lin does not explicitly disclose determining a low-dimensional representation of each polyline from the first set of polylines representing the one or more first components of the HD map;
determining 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
determining 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 determining 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);
determining 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
determining 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 method 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 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).
Lin does not explicitly disclose means for determining a low-dimensional representation of each polyline from the first set of polylines representing the one or more first components of the HD map;
means for determining 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
means for determining 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 means for determining 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);
means for determining 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
means for determining 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.
Allowable Subject Matter
Claims 3-4 and 7-12 are 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.
Gazepi (US 2025/0214617 A1) teaches autonomous vehicle motion control for pull-over maneuvers including map data with low-dimension descriptors.
Drost (US 2024/0062550 A1) teaches a method for providing a neural network for directly validating an environment map in a vehicle by means of sensor data including where inputs from various sources, such as HD map data and sensor data, are represented in a low-dimensional feature space such as a joint latent space.
Xu (US 2021/0201504 A1) teaches a vehicle trajectory prediction model with semantic map including generating a low-dimensional vector representation of the target obstacle's features.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Harrison Heflin whose telephone number is (571)272-5629. The examiner can normally be reached Monday - Friday, 1:00PM - 10:00PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at 571-272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HARRISON HEFLIN/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights