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 .
Application Status
Claims 1-20 are pending and have been examined in this application.
This communication is the first action on the merits.
Three information disclosure statement (IDS) have been filed on 23 August 2024, 14 May 2025, and 13 April 2026; and reviewed by the Examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claims 2 and 9 are objected to because of the following informalities: “the target road segments” appears to be a typographical error and should be “the target road segment . Appropriate correction is required.
Claims 3 and 10 are objected to because of the following informalities: claims 3 and 10 should be amended to recite “in response to the target vehicle reaching . Appropriate correction is required.
Claims 3, 5 and 10 are objected to because of the following informalities: claims 3, 5 and 10 should be amended to recite “the . Appropriate correction is required.
Claim 6 is objected to because of the following informalities: “the targe vehicle” appears to be a typographical error and should be “the target vehicle” to match the previously recited language. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: claim 10 should be amended to recite “and control . Appropriate correction is required.
Claim 11 is objected to because of the following informalities: claim 11 should be amended to recite “searching . Appropriate correction is required.
Claim 11 is objected to because of the following informalities: claim 11 should be amended to recite “the target road segment” for consistency in claim language. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: claim 15 should be amended to recite “the one road segment ... the convolution layer” for consistency in claim language. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: claim 17 should be amended to recite “the one road segment ...” for consistency in claim language. Appropriate correction is required.
Applicant is advised that should claim 19 be found allowable, claim 8 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8 and 11 are indefinite because the sentence “a target road segment that the target vehicle to travel on” appears incomplete.
Claim 1 is indefinite because of the recited limitation “calling the target perception model to perform ..., and controlling ...”. It is unclear, to the Examiner, which part of the limitation/claim exactly, “controlling” is associated with. For example, 1) if “controlling ...” is merely associated with one of the steps comprised by the method, then why doesn’t it match the format which should be “obtaining ...; obtaining ...; calling ...[[,]] ; and controlling ...” each as their own separate limitation and 2) if “controlling ...” is associated with the “calling the target perception model to ...” limitation then why is the conjugation of the verb incorrect when it should be “calling the target perception model to perform ..., and control ...”.
Claim 8 is indefinite because of the recited limitation “call the target perception model to perform ..., and controlling ...”. It is unclear, to the Examiner, which part of the limitation/claim exactly, “controlling” is associated with. For example, 1) if “controlling ...” is merely associated with one of the steps the computer device is caused to do, then why doesn’t it match the format and use incorrect grammar when it should be “obtain ...; obtain ...; call ...[[,]] ; and control ...” each as their own separate limitation and 2) if “controlling ...” is associated with the “call the target perception model to ...” limitation then why is the conjugation of the verb incorrect when it should be “call the target perception model to perform ..., and control ...”.
Claim 11 is indefinite because of the recited limitation “delivering the target perception model ... to call .... to perform ... and control ...”. It is unclear, to the Examiner, which part of the limitation/claim exactly, “control” is associated with. For example, 1) if “control ...” is merely associated with one of the steps comprised by the method, then why doesn’t it match the format and use incorrect grammar when it should be “obtaining ...; searching ...; delivering ... ; and controlling ...” each as their own separate limitation or 2) if “control ...” is associated with the “call the target perception model to ...” limitation.
Claim 13 recites “the target geographic area” in line 2. There is insufficient antecedent basis for such limitation in the claim.
Claim 16 recites “the road image sets”. There is insufficient antecedent basis for such limitation in the claim.
Claim 17 recites “the perception models of the one road segment under the different environmental conditions”. There is insufficient antecedent basis for such limitation (bolded) in the claim.
Claims 2-7, 9, 10, 12, 14, 15 and 18-20 are rejected as being dependent upon a rejected claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
(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, 3, 6-8, 10-12, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jared (US20210191407A1).
Regarding claim 1, Jared discloses a vehicle control method, performed by a computer device (see at least abstract and claim 1), comprising: obtaining traveling information of a target vehicle, the traveling information indicating a target road segment that the target vehicle to travel on and a target environmental condition when the target vehicle is on the target road segment (see at least [0064], [0068] and claims 1,3&5-7); obtaining a target perception model of the target road segment under the target environmental condition from a perception model library (see at least [0027], [0031], [0040], [0062], [0064], [0068] and claims 5-7), the perception model library storing perception models of a plurality of road segments under different environmental conditions (see at least [0027], [0046], [0050], [0052], [0058], [0064], [0065] and claims 3&5-7); and calling the target perception model to perform a perception task on the target road segment to obtain a perception result, and controlling, based on the perception result, the target vehicle to travel on the target road segment (see at least [0036], [0066], [0069] and claim 1).
Regarding claim 3, Jared discloses wherein: the target road segment is one of a plurality of target road segments on a current navigation route of the target vehicle (see at least Figures 1B-4, [0034], [0037] and [0070]), obtaining the target perception model includes: obtaining target perception models of the plurality of target road segments under corresponding target environmental conditions from the perception model library in response to obtaining of the plurality of target road segments, each of the target perception models matching a corresponding one of the plurality of target road segments and a corresponding one of the target environmental conditions when the target vehicle is on the corresponding one of the target road segments (see at least [0027], [0031], [0040], [0062], [0064], [0065] [0068] and claims 1,3&5-7), calling the target perception model to perform the perception task and controlling the target vehicle to travel include: calling, during traveling of the target vehicle, in response to the target vehicle reaches one target road segment of the plurality of target road segments, one target perception model of the plurality of target perception models that match the one target road segment to perform the perception task, and controlling, based on the perception result, the target vehicle to travel on the one target road segment (see at least [0036], [0066], [0069] and claims 1,3&5-7).
Regarding claim 6, Jared discloses wherein: the target road segment is a current target road segment where the target vehicle is currently located on (see at least Figures 1B-4, [0031], [0043] and [0064]), obtaining the target perception model includes: obtaining the target perception model of the target road segment under the target environmental condition from the perception model library, the target perception model matching the current target road segment and the target environmental condition when the targe vehicle is on the current target road segment (see at least [0027], [0031], [0040], [0043], [0062], [0064], [0065] [0068] and claims 1,3&5-7), calling the target perception model to perform the perception task and controlling the target vehicle to travel include: calling the target perception model to perform the perception task on the current target road segment to obtain the perception result, and controlling, based on the perception result, the target vehicle to travel on the current target road segment (see at least [0031], [0036], [0043], [0044], [0066], [0069] and claims 1,3&5-7).
Regarding claim 7, Jared discloses a non-transitory computer-readable storage medium storing at least one computer program that, when executed by at least one processor, causes the at least one processor to perform the method according to claim 1 (see at least claim 19 in Jared, and rejection of claim 1 above).
Regarding claim 8, Jared discloses a computer device comprising: at least one processor; and at least one memory storing at least one computer program that, when executed by the at least one processor, causes the computer device to (see at least claim 17). The rest of claim 8 is commensurate in scope with claim 1. See above for rejection of claim 1.
Regarding claim 10, claim 10 is commensurate in scope with claim 3. See above for rejection of claim 3.
Regarding claim 11, Jared discloses a vehicle control method, performed by a computer device (see at least abstract and claim 1), comprising: obtaining traveling information of a target vehicle, the traveling information indicating a target road segment that the target vehicle to travel on and a target environmental condition when the target vehicle is on the target road segment (see at least [0064], [0068] and claims 1,3&5-7); search for a target perception model of the target road segment under the target environmental condition from a perception model library (see at least [0027], [0031], [0040], [0062], [0064], [0068] and claims 5-7), the perception model library storing perception models of a plurality of road segments under different environmental conditions (see at least [0027], [0046], [0050], [0052], [0058], [0064], [0065] and claims 3&5-7); and delivering the target perception model to the target vehicle, to enable the target vehicle to call the target perception model to perform a perception task on the target segment to obtain a perception result and control, based on the perception result, the target vehicle to travel on the target road segment (see at least [0036], [0066], [0069] and claim 1).
Regarding claim 12, Jared discloses further comprising: obtaining a segmentation granularity matching a road type of a road within a target geographic area (see at least Figures 1B-3, [0031], [0034] and [0037]); and segmenting the road based on the segmentation granularity to obtain road segments of the road (see at least Figures 1B-3, [0031], [0034] and [0037]), an overlapping area existing between any two adjacent ones of the road segments (see at least [0031], [0034] and [0037]).
Regarding claim 19, claim 19 is commensurate in scope with claims 1 and 8. See above for rejection of claims 1 and 8.
Regarding claim 20, Jared discloses a non-transitory computer-readable storage medium storing at least one computer program that, when executed by at least one processor, causes the at least one processor to perform the method according to claim 11 (see at least claim 19 of Jared; and rejection of claim 11 above).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Jared (US20210191407A1).
Regarding claim 2, Jared discloses wherein obtaining the traveling information includes: obtaining a road condition during traveling of the target vehicle based on a current navigation route (see at least [0070] and [0077]); determining, based on the road condition, a target time at which the target vehicle is on the target road segments (see at least [0065], [0068] and [0070]); and determining, based on the target time, the target environmental condition when the target vehicle is on the target road segment (see at least [0027], [0031], [0040], [0062], [0064], [0065], [0068] and claims 5-7). Jared also discloses determining a traveling speed of the target vehicle (see at least [0075]). However, Jared does not explicitly disclose determining the target time further based on the traveling speed. One of ordinary skill in the art is aware that in order to determine a time at which a vehicle is on a road segment, a traveling speed of the vehicle is needed since the time to reach the segment depends on a distance to the segment and the traveling speed of the vehicle going to the segment; therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to look at Jared and realize that the determining, based on the road condition, a target time at which the target vehicle is on the target road segments is further based on the traveling speed of the vehicle in order to be able to actually determine an accurate target time.
Regarding claim 4, Jared discloses wherein obtaining the target perception models of the plurality of target road segments under the corresponding target environmental conditions from the perception model library includes: obtaining a road condition during traveling of the target vehicle based on the current navigation route (see at least [0070] and [0077]); determining, based on the road condition, target times at which the target vehicle is on the plurality of target road segments, respectively (see at least [0065], [0068] and [0070]); and obtaining, based on the target times, the target perception models from the perception model library in sequence (see at least [0027], [0031], [0040], [0062], [0064], [0065], [0068] and claims 5-7). Jared also discloses determining a traveling speed of the target vehicle during the traveling (see at least [0075]). However, Jared does not explicitly disclose determining the target times further based on the traveling speed. One of ordinary skill in the art is aware that in order to determine a time at which a vehicle is on a road segment, a traveling speed of the vehicle is needed since the time to reach the segment depends on a distance to the segment and the traveling speed of the vehicle going to the segment; therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to look at Jared and realize that the determining, based on the road condition, target times at which the target vehicle is on the plurality of target road segments is further based on the traveling speed of the vehicle in order to be able to actually determine accurate target times.
Regarding claim 9, claim 9 is commensurate in scope with claim 2. See above for rejection of claim 2.
Claims 5, 13, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jared (US20210191407A1) in view of Omer (US20150178572A1).
Regarding claim 5, Jared discloses wherein calling the one target perception model to perform the perception task includes: obtaining, based on a sensor deployed at the target vehicle, a second environmental condition of a current target road segment where the target vehicle is currently located; determining, based on the second environmental condition, a final environmental condition of the current target road segment; and calling the one target perception model of the current target road segment under the final environmental condition from the plurality of target perception models (see at least [0027], [0031], [0036], [0040], [0062], [0064], [0065], [0066], [0068], [0069], [0077] and claims 1,3&5-7).
Jared does not explicitly disclose obtaining, based on an environment classification model deployed at the target vehicle, a first environmental condition of the current target road segment; determining, based on the first environmental condition and the second environmental condition, the final environmental condition of the current target road segment. However, Omer teaches obtaining, based on an environment classification model deployed at the target vehicle, a first environmental condition of the current target road segment; determining, based on the first environmental condition and the second environmental condition, the final environmental condition of the current target road segment (see at least abstract, Figure 3C, Figure 6, [0037], [0054]-[0062], [0069] and [0070]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared to incorporate the teachings of Omer which teaches obtaining, based on an environment classification model deployed at the target vehicle, a first environmental condition of the current target road segment; determining, based on the first environmental condition and the second environmental condition, the final environmental condition of the current target road segment since they are directed to environmental conditions used in vehicle navigation and incorporation of the teachings of Omer would increase accuracy of the determination and thereby increase reliability of the overall system.
Regarding claim 13, Jared does not explicitly disclose obtaining road images collected by a plurality of vehicles within the target geographic area and position information of the plurality of vehicles when the road images are collected; associating, based on the position information, the road images with different road segments within the target geographic area to obtain sample data sets of the road segments within the target geographic area; and associating, for one road segment of the road segments, the sample data set of the one road segment with different environmental conditions to obtain road image sets of the one road segment under the different environmental conditions, each of the road image sets including one of the road images of the one road segment under one of the different environmental conditions. However, Omer teaches obtaining road images collected by a plurality of vehicles within the target geographic area and position information of the plurality of vehicles when the road images are collected; associating, based on the position information, the road images with different road segments within the target geographic area to obtain sample data sets of the road segments within the target geographic area; and associating, for one road segment of the road segments, the sample data set of the one road segment with different environmental conditions to obtain road image sets of the one road segment under the different environmental conditions, each of the road image sets including one of the road images of the one road segment under one of the different environmental conditions (see at least abstract, Figure 1, Figure 3C, Figure 4, [0009], [0013]-[0015], [0030], [0034], [0037], [0054]-[0062], [0069] and [0070]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared to incorporate the teachings of Omer which teaches obtaining road images collected by a plurality of vehicles within the target geographic area and position information of the plurality of vehicles when the road images are collected; associating, based on the position information, the road images with different road segments within the target geographic area to obtain sample data sets of the road segments within the target geographic area; and associating, for one road segment of the road segments, the sample data set of the one road segment with different environmental conditions to obtain road image sets of the one road segment under the different environmental conditions, each of the road image sets including one of the road images of the one road segment under one of the different environmental conditions since they are directed to environmental conditions used in vehicle navigation and incorporation of the teachings of Omer would increase accuracy of the determinations and thereby increase reliability of the overall system.
Regarding claim 14, Jared discloses obtaining data collected by the target vehicle during traveling; updating, based on the data, data sets of the road segments within the target geographic area under the different environmental conditions, to obtain updated data sets; and training, based on the updated data sets, perception models of the road segments under the different environmental conditions (see at least [0027], [0046], [0050], [0052], [0058], [0064], [0065], [0074] and claims 3&5-7).
Jared does not explicitly disclose for the data to be a plurality of road images and the data sets to be road image sets. However, Omer teaches the data to be a plurality of road images and the data sets to be road image sets (see at least abstract, Figure 1, Figure 3C, Figure 4, [0009], [0013]-[0015], [0030], [0034] and [0037]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared to incorporate the teachings of Omer which teaches the data to be a plurality of road images and the data sets to be road image sets since they are directed to environmental conditions used in vehicle navigation and incorporation of the teachings of Omer would increase accuracy of the determinations and thereby increase reliability of the overall system.
Regarding claim 16, Jared discloses wherein for a target perception task, perception models of one road segment under the different environmental conditions are trained based on the data sets of the one road segment under the different environmental conditions and a model training method matching the target perception task, the perception models being configured to perform the target perception task (see at least [0027], [0040], [0046], [0050], [0052], [0058], [0062], [0064], [0065], [0068] and claims 3&5-7).
Jared does not explicitly disclose for the data sets to be road image sets. However, Omer teaches the data sets to be road image sets (see at least abstract, Figure 1, Figure 3C, Figure 4, [0009], [0013]-[0015], [0030], [0034] and [0037]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared to incorporate the teachings of Omer which teaches the data sets to be road image sets since they are directed to environmental conditions used in vehicle navigation and incorporation of the teachings of Omer would increase accuracy of the determinations and thereby increase reliability of the overall system.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Jared (US20210191407A1) in view of Omer (US20150178572A1) in further view of Ren (NPL, “Faster R-CNN ...”).
Regarding claim 15, Jared as modified by Omer fails to disclose wherein: associating the sample data set of the one road segment with the different environmental conditions includes: classifying the sample data set of the one road segment based on an environment classification model to obtain environmental conditions corresponding to road images in the sample data set; and using road images with a same environmental condition as a road image set of the road segment under an environmental condition; the environment classification model includes a convolution layer, a first pooling layer, a plurality of residual blocks connected in sequence, a second pooling layer, and a fully connected layer; and for one road image in the sample data set: the first convolution layer is configured to perform feature extraction on the one road image to obtain a first feature map; the first pooling layer is configured to downsample the first feature map to obtain a second feature map; the plurality of residual blocks connected in sequence are configured to perform feature extraction on the second feature map to obtain a third feature map; the second pooling layer is configured to downsample the third feature map to obtain a fourth feature map; and the fully connected layer is configured to output, based on the fourth feature map, an environmental condition corresponding to the one road image. However, Ren teaches wherein: associating the sample data set of the one road segment with the different environmental conditions includes: classifying the sample data set of the one road segment based on an environment classification model to obtain environmental conditions corresponding to road images in the sample data set; and using road images with a same environmental condition as a road image set of the road segment under an environmental condition; the environment classification model includes a convolution layer, a first pooling layer, a plurality of residual blocks connected in sequence, a second pooling layer, and a fully connected layer; and for one road image in the sample data set: the first convolution layer is configured to perform feature extraction on the one road image to obtain a first feature map; the first pooling layer is configured to downsample the first feature map to obtain a second feature map; the plurality of residual blocks connected in sequence are configured to perform feature extraction on the second feature map to obtain a third feature map; the second pooling layer is configured to downsample the third feature map to obtain a fourth feature map; and the fully connected layer is configured to output, based on the fourth feature map, an environmental condition corresponding to the one road image (see at least pages 1-11). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared as modified by Omer to incorporate the teachings of Ren which teaches wherein: associating the sample data set of the one road segment with the different environmental conditions includes: classifying the sample data set of the one road segment based on an environment classification model to obtain environmental conditions corresponding to road images in the sample data set; and using road images with a same environmental condition as a road image set of the road segment under an environmental condition; the environment classification model includes a convolution layer, a first pooling layer, a plurality of residual blocks connected in sequence, a second pooling layer, and a fully connected layer; and for one road image in the sample data set: the first convolution layer is configured to perform feature extraction on the one road image to obtain a first feature map; the first pooling layer is configured to downsample the first feature map to obtain a second feature map; the plurality of residual blocks connected in sequence are configured to perform feature extraction on the second feature map to obtain a third feature map; the second pooling layer is configured to downsample the third feature map to obtain a fourth feature map; and the fully connected layer is configured to output, based on the fourth feature map, an environmental condition corresponding to the one road image since they are directed to object detection and incorporation of the teachings of Ren would increase accuracy of the associations and thereby increase reliability of the overall system.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Jared (US20210191407A1) in view of Liu (US20220076444A1).
Regarding claim 17, Jared discloses wherein for one road segment of the road segments, the perception models of the one road segment under the different environmental conditions are trained (see at least [0027], [0040], [0046], [0050], [0052], [0058], [0062], [0064], [0065], [0068] and claims 3&5-7).
Jared fails to disclose trained by: generating, for a road image set of the one road segment, anchors on road images in the road image set; and annotating, for any one of the road images, the anchor on the road image based on an annotation box on the road image, to obtain an annotation category, annotation offset, and annotation confidence of the anchor, the annotation box being configured for annotating a target in the road image, the annotation category being configured for indicating a category of the target, the annotation offset being configured for indicating an offset between the anchor and a similar annotation box, and the annotation confidence being configured for indicating a degree of confidence that the anchor includes the target; inputting the road images in the road image set into a deep learning model to obtain an output feature map, each position of the output feature map including a plurality of anchors; and training a perception model of the road segment under an environmental condition based on prediction categories, prediction offsets, and prediction confidences of the anchors on the output feature map, annotation categories, annotation offsets, and annotation confidences of the anchors. However, Liu teaches trained by: generating, for a road image set of the one road segment, anchors on road images in the road image set; and annotating, for any one of the road images, the anchor on the road image based on an annotation box on the road image, to obtain an annotation category, annotation offset, and annotation confidence of the anchor, the annotation box being configured for annotating a target in the road image, the annotation category being configured for indicating a category of the target, the annotation offset being configured for indicating an offset between the anchor and a similar annotation box, and the annotation confidence being configured for indicating a degree of confidence that the anchor includes the target; inputting the road images in the road image set into a deep learning model to obtain an output feature map, each position of the output feature map including a plurality of anchors; and training a perception model of the road segment under an environmental condition based on prediction categories, prediction offsets, and prediction confidences of the anchors on the output feature map, annotation categories, annotation offsets, and annotation confidences of the anchors (see at least abstract [0053], [0073], [0095] and specifically [0125]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared to incorporate the teachings of Liu which teaches trained by: generating, for a road image set of the one road segment, anchors on road images in the road image set; and annotating, for any one of the road images, the anchor on the road image based on an annotation box on the road image, to obtain an annotation category, annotation offset, and annotation confidence of the anchor, the annotation box being configured for annotating a target in the road image, the annotation category being configured for indicating a category of the target, the annotation offset being configured for indicating an offset between the anchor and a similar annotation box, and the annotation confidence being configured for indicating a degree of confidence that the anchor includes the target; inputting the road images in the road image set into a deep learning model to obtain an output feature map, each position of the output feature map including a plurality of anchors; and training a perception model of the road segment under an environmental condition based on prediction categories, prediction offsets, and prediction confidences of the anchors on the output feature map, annotation categories, annotation offsets, and annotation confidences of the anchors since they are directed to object detection and incorporation of the teachings of Liu would increase accuracy of the training and thereby increase reliability of the overall system.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Jared (US20210191407A1) in view of Liu (US20220076444A1) in further view of Shen (CN110175658A – translation attached).
Regarding claim 18, Jared as modified by Liu does not explicitly disclose wherein generating the anchors on the road images includes: clustering annotation boxes on the road images to obtain a plurality of classes, each class including a plurality of data points, and the data points being generated based on height values and width values of the annotation boxes; using a quantity of the classes as a quantity of the anchors, and using height values and width values corresponding to centroids of the classes as sizes of the anchors; and generating the anchors based on the quantity of anchors and the sizes of the anchors. However, Shen teaches wherein generating the anchors on the road images includes: clustering annotation boxes on the road images to obtain a plurality of classes, each class including a plurality of data points, and the data points being generated based on height values and width values of the annotation boxes; using a quantity of the classes as a quantity of the anchors, and using height values and width values corresponding to centroids of the classes as sizes of the anchors; and generating the anchors based on the quantity of anchors and the sizes of the anchors (see at least [0019], [0024], [0026], [0030], [0040], [0063], [0064], [0073]-[0079]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Jared as modified by Liu to incorporate the teachings of Shen which teaches wherein generating the anchors on the road images includes: clustering annotation boxes on the road images to obtain a plurality of classes, each class including a plurality of data points, and the data points being generated based on height values and width values of the annotation boxes; using a quantity of the classes as a quantity of the anchors, and using height values and width values corresponding to centroids of the classes as sizes of the anchors; and generating the anchors based on the quantity of anchors and the sizes of the anchors since they are directed to object detection and incorporation of the teachings of Shen would increase accuracy of the detection and thereby increase reliability of the overall system.
Conclusion
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/SAHAR MOTAZEDI/Primary Examiner, Art Unit 3667
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