DETAILED ACTIONS
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 .
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 02/23/2026 and 04/10/2026 were reviewed and the listed references were noted.
Status of Claims
Claims 1-20 are pending.
Response to Amendment
The amendment filed 02/19/2026 has been entered. Claims 1-20 remain pending in the
application. Applicant’s amendment to the Claims have overcome each and every objection, 101 rejections previously set forth in the Non-Final Office Action mailed November 19th, 2025.
Response to Arguments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 9-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kocamaz et al., (US 2023/0099494 A1, published 03/30/2023), hereinafter referred to as Kocamaz, in view of Saggu et al., (US 2023/0394849 A1, filed on 08/16/2023), hereinafter referred to as Saggu, in further view of Zhao et al., (US 2024/203135 A1, provisional application filed 12/15/2022), hereinafter referred to as Zhao (previously cited in an IDS filed by the applicant).
Claim 1
Kocamaz discloses a method for managing location information in automated vehicles (Kocamaz, Fig. 6), the method comprising:
obtaining, by a processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”) of an autonomous vehicle (Kocamaz, [0082], “FIG. 8A is an illustration of an example autonomous vehicle 800, in accordance with some embodiments of the present disclosure”), image data from a camera on the autonomous vehicle (Kocamaz, [0014], “FIG. 8B is an example of camera locations and fields of view for the example autonomous vehicle of FIG. 8A”), the image data includes a digital representation of imagery in a field-of-view of the camera including an operational environment with one or more objects including a vehicle and a roadway having a one or more of lanes (Kocamaz, Fig. 2A, [0008], “FIGS. 2A-2D are visualizations of example annotations applied to sensor data and resulting ground truth masks used for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure”);
identifying, by the processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), in the image data the vehicle (Kocamaz, [0035], “FIG. 2B illustrates an example of an annotated image 200B with annotations applied to sensor data to train a machine learning model to assign objects to lanes, in accordance with some embodiments of the present invention. As depicted here, the vehicle objects 202A, 202B, 202C, and 202D of FIG. 2A, may be annotated with polygons or bounding shapes. “) and the one or more lanes (Kocamaz, [0032], “The lane identifier label(s) 110B may be generated for each of the images (or other data representations) and/or for each one or more of the lanes in the images represented by the sensor data 102 used for training the machine learning model(s) 104. The number of lane identifier label(s) 110B may correspond to the number and/or types of lanes or lane features that the machine learning model(s) 104 is trained to predict, or to the number of lane regions and/or types of features in the respective image. Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like.”);
determining, by a processor, one or more lane index value of the one or more lanes (Kocamaz, [0066], “The visualization 530A corresponds to a combined output segmentation mask (corresponding to output masks 106) indicating pixel confidences for different object class/lane identifier combinations”, [0033], “The object class label(s) 110A and the lane identifier label(s) 110B may be configured into a combination of object class and lane identifier. For example, a combination of object class and lane identifier may be associated with a detected object represented by the sensor data 102. For instance, a detected object may be represented by the combination of a “vehicle” object class label and a “left of ego-lane” lane identifier label.”, [0032], “Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like”, other lane type can be a shoulder lane, [0022], “For example, in some embodiments, the combination of object class and lane identifier corresponding to the most pixels or points within the bounding shape may be used to determine the final assignment of object class and lane identifier.”);
determining, by the processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), that the vehicle is situated in a one or more of lanes of the roadway (Kocamaz,[0033], “The object class label(s) 110A and the lane identifier label(s) 110B may be configured into a combination of object class and lane identifier. For example, a combination of object class and lane identifier may be associated with a detected object represented by the sensor data 102. For instance, a detected object may be represented by the combination of a “vehicle” object class label and a “left of ego-lane” lane identifier label.”, [0032], “Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like”, other lane type can be a shoulder lane, [0022], “For example, in some embodiments, the combination of object class and lane identifier corresponding to the most pixels or points within the bounding shape may be used to determine the final assignment of object class and lane identifier.”);
for each lane, applying, by the processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), to the image data a lane label associated with the particular lane (Kocamaz, [0032], “The lane identifier label(s) 110B may be generated for each of the images (or other data representations) and/or for each one or more of the lanes in the images represented by the sensor data 102 used for training the machine learning model(s) 104. The number of lane identifier label(s) 110B may correspond to the number and/or types of lanes or lane features that the machine learning model(s) 104 is trained to predict, or to the number of lane regions and/or types of features in the respective image. Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like.”, [0034], “For example, lanes 204, 206, 208, and 210 are depicted in image 200A and may correspond to a location relative to reference point, such as an ego-vehicle associated with the sensor data 102. From the perspective of an ego-vehicle, the lanes 204, 206, 208, and 210 may correspond to an ego-lane, a left of ego-lane, a right of ego-lane, and other lane. For instance, if an ego-vehicle is positioned in lane 204, lane 204 may be associated with the ego-lane, lane 206 may correspond to a left of ego-lane, lane 208 may correspond to a right of ego-lane, and lane 210 may correspond to an “other” lane label”); and
updating, by the processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), the image data by applying a vehicle label indicating the (Kocamaz, Fig. 2C, [0036], “The segmentation mask 200C may depict the assignment of pixels to a single lane classification, or may depict the assignment of pixels to a plurality of lane classifications. For example, pixels in pixel area 222A may be assigned to the left of an ego-lane, the pixels in pixel area 222B may be assigned to the ego-lane, and the pixels of pixel area 222C may be assigned to an other lane. In such an example, a first GT mask 116 may be generated for a vehicle class/left of ego-lane combination such that the pixels corresponding to object 222A may be included in the first GT mask 116. A second GT mask 116 may be generated for a vehicle to ego-lane combination such that the pixels corresponding to object 22B may be included in the second GT mask 116. This process may be repeated for each object class/lane combination to generate the GT masks 116 for each instance of the sensor data 102. The lane assignments may be of similar visual representation for a same classification. The different lane assignments may be represented in FIG. 2C by colors, solid lines, dashed lines, etc., to represent different classifications.”, the other lane type which has vehicle 222C can be the shoulder lane).
Kocamaz discloses that one of the lanes that is labeled and assigned for the vehicle is an “other” lane type which could be a shoulder lane as shown in Figure 2C. Kocamaz does not explicitly disclose determining, by the processor that the vehicle is situated in a shoulder lane of the plurality of lanes of the roadway and updating, by the processor the image data by applying a vehicle label indicating the shoulder lane for the vehicle and in response to updating the image data by applying the vehicle label indicating the shoulder lane, controlling operations of the autonomous vehicle.
However, Saggu teaches determining, by the processor that the vehicle is situated in a shoulder lane of the plurality of lanes of the roadway and updating, by the processor the image data by applying a vehicle label indicating the shoulder lane for the vehicle (Saggu, [0039], the data collection device 110 can execute a third machine learning model (not shown) to generate an indication of a location of the vehicle with respect to the at least one lane based on the at least one lane, each image from the first plurality of images, and the offset value. The third machine learning model can be optionally coupled to the first machine learning model 114, the localization model 115, the data selector 116 and/or the second machine learning model 117 and can be configured to generate an indication of location (e.g., a Cartesian coordinate) of the vehicle with respect to the at least one lane based on at least one of the at least one lane, respective image from the first set of images, and/or respective offset value” indication is the vehicle label indicating the lane, [0038], “the set of scenarios can include a scenario(s) at which a vehicle is parked, moving on to or moving out from a shoulder of a road”, the third machine learning model can be coupled to second machine learning model that outputs the scenario where the vehicle is determined to be situated in the shoulder lane).
Kocamaz and Saggu are both considered to be analogous to the claimed invention because they are in the same field of vehicle and lane detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Kocamaz to incorporate the teachings of Saggu of determining, by the processor that the vehicle is situated in a shoulder lane of the plurality of lanes of the roadway and updating, by the processor the image data by applying a vehicle label indicating the shoulder lane for the vehicle. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been for improving a safety of machine learning models used, for example, in self-driving vehicles (Saggu, [0002]).
The combination of Kocamaz in view of Saggu does not explicitly disclose the one or more labels including a shoulder lane, the one or more lane index values including a lane index value corresponding to the shoulder lane, the lane label including the lane index value corresponding to the shoulder lane, and in response to updating the image data by applying the vehicle label indicating the shoulder lane, controlling operations of the autonomous vehicle.
However, Zhao teaches the one or more labels including a shoulder lane (Zhao, [0005], “rendering the plurality of polygons onto the image; and determining identifiers of lane segments of the lane, where the lane segments are imaginary and extend lengthwise along the lane, and where at least one lane segment of the lane is adjacent to at least one other lane segment of the lane; determining one or more characteristics of a lane segment on which the vehicle is operating based on an identifier of the lane segment”, [0049], “if the semantic rendering module determines that an identifier of the vehicle is associated with an emergency lane or shoulder or breakdown lane, the semantic rendering module can send the identifier of the vehicle to the driving operation module”), the one or more lane index values including a lane index value (Zhao, [0007], “for each of the plurality of lanes, a plurality of pixels within the boundaries of the lane are associated with the identifiers of the lane segments of the lane. In some embodiments, a pixel value of a pixel from the plurality of pixels stores a second identifier of one lane segment that comprises the pixel”) corresponding to the shoulder lane (Zhao, [0049], “if the semantic rendering module determines that an identifier of the vehicle is associated with an emergency lane or shoulder or breakdown lane, the semantic rendering module can send the identifier of the vehicle to the driving operation module”), the lane label including the lane index value corresponding to the shoulder lane (Zhao, [0007], “ the set of values that describe the locations of the boundaries of the lane includes three-dimensional (3D) world coordinates for each of a plurality of point along the boundaries of the lane. In some embodiments, a plurality of pixels within the boundaries of the lane are associated with the 3D world coordinates of the lane.”, [0009], “for each of the plurality of lanes, the identifiers of the lane segments of the lane is determined by: obtaining a location of the vehicle; and determining, from the map database stored in the computer, the identifiers of the lane segments of the lane located in front of or around the location of the vehicle. In some embodiments, the method further comprises: assigning, for each of the plurality of lanes, the identifiers of the lane segments of the lane to the lane segments on the lane in the image by determining in image locations of the lane segments relative to the location of the vehicle. In some embodiments, the method further comprises: assigning at least one identifier of a first lane segment to at least one vehicle in response to determining that the at least one vehicle is operating or located on the first lane segment.”, [0049], “if the semantic rendering module determines that an identifier of the vehicle is associated with an emergency lane or shoulder or breakdown lane, the semantic rendering module can send the identifier of the vehicle to the driving operation module”), and in response to updating the image data by applying the vehicle label indicating the shoulder lane, controlling operations of the autonomous vehicle (Zhao, [0049], “For example, if the semantic rendering module determines that an identifier of the vehicle is associated with an emergency lane or shoulder or breakdown lane, the semantic rendering module can send the identifier of the vehicle to the driving operation module. The driving operation module determines that the autonomous vehicle is operating on a first lane immediately adjacent to the emergency lane, then the driving operation module can send an instruction to a motor associated with a steering system of the autonomous vehicle that causes the autonomous vehicle to move into a second lane adjacent to the first lane and away from the emergency lane.”).
Kocamaz, Saggu, and Zhao are all considered to be analogous to the claimed invention because they are in the same field of vehicle and lane detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Kocamaz to incorporate the teachings of Zhao of the one or more labels including a shoulder lane, the one or more lane index values including a lane index value corresponding to the shoulder lane, the lane label including the lane index value corresponding to the shoulder lane, and in response to updating the image data by applying the vehicle label indicating the shoulder lane, controlling operations of the autonomous vehicle. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been for improving the safety of the autonomous vehicle.
Claim 2
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6), further comprising executing, by the processor (Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), one or more driving operations based upon the vehicle label and each lane label (Kocamaz, [0080], “The method 700, at block B712, includes performing, using the one or more processing units, one or more operation for an autonomous machine based at least in part on the assignment of the first combination of object class and lane identifier to the object. For example, based on the object to lane assignments 408, the control component(s) 410 may perform or more operations associated with an autonomous machine.”, [0061], “the post-processor 402 may use the output mask(s) 106 to generate a representation of the road surface, which may be used, in turn, to navigate the roadway—e.g., by control component(s) 410 of the vehicle”).
Claim 3
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6), wherein the lane index value of the lane label represents the lane of a number of lanes from a leftmost or rightmost lane to the lane in which the autonomous vehicle was positioned (Kocamaz, [0066], “In this example, the visualization 530A includes four classifications of object class/lane identifier for the pixels of the corresponding input image: pixel group 506A corresponds to an assignment of vehicles to the left of ego-lane, pixel group 506B corresponds to an assignment of vehicles to the ego-lane, pixel group 506C corresponds to an assignment of vehicles to the right of ego-lane, and pixel group 506D corresponds to an assignment of vehicles to other lanes”, Fig. 2C).
Claim 4
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6), further comprising applying, by the processor ([Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”), in the vehicle label for the vehicle a flag indicating the object is in the shoulder lane (Saggu, [0039], “the data collection device 110 can execute a third machine learning model (not shown) to generate an indication of a location of the vehicle with respect to the at least one lane based on the at least one lane, each image from the first plurality of images, and the offset value.”, [0038], “the set of scenarios can include a scenario(s) at which a vehicle is parked, moving on to or moving out from a shoulder of a road”). The proposed combination as well as the motivation for combining the Kocamaz and Saggu references presented in the rejection of Claim 1, apply to Claim 4 and are incorporated herein by reference. Thus, the method recited in Claim 4 is met by Kocamaz and Saggu.
Claim 5
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6), further comprising, for each driving lane of the one or more lanes, applying, by the processor, to the image data a lane label associated with the particular lane and indicating a lane index value (Kocamaz, [0066], “The visualization 530A corresponds to a combined output segmentation mask (corresponding to output masks 106) indicating pixel confidences for different object class/lane identifier combinations”, [0033], “The object class label(s) 110A and the lane identifier label(s) 110B may be configured into a combination of object class and lane identifier. For example, a combination of object class and lane identifier may be associated with a detected object represented by the sensor data 102. For instance, a detected object may be represented by the combination of a “vehicle” object class label and a “left of ego-lane” lane identifier label.”, [0032], “Depending on the embodiment, the lane identifier label(s) 110B may correspond to classifications or tags corresponding to the lane identifiers, such as but not limited to ego-lane, right of ego-lane, left of ego-lane, other lane types, and/or the like”, other lane type can be a shoulder lane, [0022], “For example, in some embodiments, the combination of object class and lane identifier corresponding to the most pixels or points within the bounding shape may be used to determine the final assignment of object class and lane identifier.”).
Claim 6
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 5 (Kocamaz, Fig. 6), wherein the lane index value indicates the vehicle is on the shoulder lane (Zhao, [0007], “ the set of values that describe the locations of the boundaries of the lane includes three-dimensional (3D) world coordinates for each of a plurality of point along the boundaries of the lane. In some embodiments, a plurality of pixels within the boundaries of the lane are associated with the 3D world coordinates of the lane.”, [0009], “for each of the plurality of lanes, the identifiers of the lane segments of the lane is determined by: obtaining a location of the vehicle; and determining, from the map database stored in the computer, the identifiers of the lane segments of the lane located in front of or around the location of the vehicle. In some embodiments, the method further comprises: assigning, for each of the plurality of lanes, the identifiers of the lane segments of the lane to the lane segments on the lane in the image by determining in image locations of the lane segments relative to the location of the vehicle. In some embodiments, the method further comprises: assigning at least one identifier of a first lane segment to at least one vehicle in response to determining that the at least one vehicle is operating or located on the first lane segment.”, [0049], “if the semantic rendering module determines that an identifier of the vehicle is associated with an emergency lane or shoulder or breakdown lane, the semantic rendering module can send the identifier of the vehicle to the driving operation module”). The proposed combination as well as the motivation for combining the Kocamaz, Saggu, and Zhao references presented in the rejection of Claim 1, apply to Claim 6 and are incorporated herein by reference. Thus, the method recited in Claim 6 is met by Kocamaz, Saggu, and Zhao.
Claim 9
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6), further comprising applying, by the processor, a shoulder classifier on the image data to determine the vehicle is in the shoulder (Saggu, [0039], “the data collection device 110 can execute a third machine learning model (not shown) to generate an indication of a location of the vehicle with respect to the at least one lane based on the at least one lane, each image from the first plurality of images, and the offset value.”, [0038], “the set of scenarios can include a scenario(s) at which a vehicle is parked, moving on to or moving out from a shoulder of a road”). The proposed combination as well as the motivation for combining the Kocamaz, Saggu, and Zhao references presented in the rejection of Claim 1, apply to Claim 9 and are incorporated herein by reference. Thus, the method recited in Claim 9 is met by Kocamaz, Saggu, and Zhao.
Claim 10
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 9 (Kocamaz, Fig. 6), further comprising applying, by the processor, in the vehicle label for the vehicle a flag indicating the object is in the shoulder lane (Saggu, [0039], “the data collection device 110 can execute a third machine learning model (not shown) to generate an indication of a location of the vehicle with respect to the at least one lane based on the at least one lane, each image from the first plurality of images, and the offset value.”, [0038], “the set of scenarios can include a scenario(s) at which a vehicle is parked, moving on to or moving out from a shoulder of a road”, indication is the label to flag that the vehicle is in the shoulder lane). The proposed combination as well as the motivation for combining the Kocamaz, Saggu, and Zhao references presented in the rejection of Claim 1, apply to Claim 10 and are incorporated herein by reference. Thus, the method recited in Claim 10 is met by Kocamaz, Saggu, and Zhao.
Claims 11-16 and 19-20 are rejected for similar reasons as those described in claim 1-6 and 9-10. The additional elements in Claims 11-16 and 19-20 (the combination of Kocamaz in view of Saggu in view of Zhao) discloses includes: a system for managing location information in automated vehicles (Kocamaz, Fig. 1), the system comprising a datastore (Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”) of an automated vehicle (Kocamaz, Fig. 8A) comprising non-transitory machine-readable storage (Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”) configured to store image data from a camera of the automated vehicle, the image data includes a digital representation of imagery in a field-of-view of the camera including an operational environment with one or more objects and a roadway having one or more driving lanes (Kocamaz, Fig. 2A, [0008], “FIGS. 2A-2D are visualizations of example annotations applied to sensor data and resulting ground truth masks used for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure”). The proposed combination as well as the motivation for combining the Kocamaz, Saggu, and Zhao references presented in the rejection of Claim 1, apply to Claims 11-16 and 19-20 and are incorporated herein by reference. Thus, the method recited in Claim 11-16 and 19-20 is met by Kocamaz, Saggu, and Zhao.
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kocamaz in view of Saggu in view of Zhao in further view of Shima et al., (US 2012/0288154 A1, published 11/15/2012), hereinafter referred to as Shima)
Claim 7
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6).
The combination of Kocamaz in view of Saggu in view of Zhao does not explicitly disclose wherein the processor determines that the shoulder having the vehicle is a left shoulder.
However, Shima teaches wherein the processor determines that the shoulder having the vehicle is a left shoulder ([0043], “The device is so installed that the left imaging portion 105 and the right imaging portion 106 can image a range located ahead of the vehicle 103 and can image the vehicular road 101 and the subject (three-dimensional object) ahead of the vehicle”, [0091]. “The contents of the received decision result include a decision as to whether there are the road shoulders 113 on the right and left of the vehicular road 101 ahead of the vehicle occurring in a case where the off-road regions 114 are lower than the surface of the vehicular road 101, as well as the relative position between the road shoulders 113 and the vehicle 103 in a case where there are the road shoulders 113 occurring in a case where the off-road regions 114 are lower than the vehicular road surface.”, [0095], “The warning need decision portion 112 makes a decision as to whether it is better to give a warning to the driver about road departure of the vehicle taking account of the relative position with the road shoulders 113 occurring in a case where the off-road regions 114 are lower than the vehicular road surface, the speed of the subject vehicle, and the direction of motion of the subject vehicle (steering angle), based on the result of the decision received from the vehicle departure decision portion 116, and sends instructions to the HEMI (human-machine interface) to give a warning to the driver)”, Fig. 13 shows both left and right shoulder)
Kocamaz, Saggu, Zhao and Shima are all considered to be analogous to the claimed invention because they are in the same field of vehicle and lane detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Shima to incorporate the teachings of Saggu wherein the processor determines that the shoulder having the vehicle is a left shoulder. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to realize safe vehicle motion (Shima, [0002]).
Claim 8
The combination of Kocamaz in view of Saggu in view of Zhao discloses the method according to claim 1 (Kocamaz, Fig. 6).
The combination of Kocamaz in view of Saggu in view of Zhao does not explicitly disclose wherein the processor determines that the shoulder having the vehicle is a right shoulder.
However, Shima teaches wherein the processor determines that the shoulder having the vehicle is a right shoulder ([0043], “The device is so installed that the left imaging portion 105 and the right imaging portion 106 can image a range located ahead of the vehicle 103 and can image the vehicular road 101 and the subject (three-dimensional object) ahead of the vehicle”, [0091]. “The contents of the received decision result include a decision as to whether there are the road shoulders 113 on the right and left of the vehicular road 101 ahead of the vehicle occurring in a case where the off-road regions 114 are lower than the surface of the vehicular road 101, as well as the relative position between the road shoulders 113 and the vehicle 103 in a case where there are the road shoulders 113 occurring in a case where the off-road regions 114 are lower than the vehicular road surface.”, [0095], “The warning need decision portion 112 makes a decision as to whether it is better to give a warning to the driver about road departure of the vehicle taking account of the relative position with the road shoulders 113 occurring in a case where the off-road regions 114 are lower than the vehicular road surface, the speed of the subject vehicle, and the direction of motion of the subject vehicle (steering angle), based on the result of the decision received from the vehicle departure decision portion 116, and sends instructions to the HEMI (human-machine interface) to give a warning to the driver)”, Fig. 13 shows both left and right shoulder)
Kocamaz, Saggu, Zhao, and Shima are all considered to be analogous to the claimed invention because they are in the same field of vehicle and lane detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Shima to incorporate the teachings of Saggu wherein the processor determines that the shoulder having the vehicle is a right shoulder. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to realize safe vehicle motion (Shima, [0002]).
Claims 17-18 are rejected for similar reasons as those described in claims 7-8. The additional elements in Claims 17-18 (the combination of Kocamaz in view of Saggu in view of Zhao in further view of Shima) discloses includes: a system for managing location information in automated vehicles (Kocamaz, Fig. 1), the system comprising a datastore (Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”) of an automated vehicle (Kocamaz, Fig. 8A) comprising non-transitory machine-readable storage (Kocamaz, [0025], “various functions may be carried out by a processor executing instructions stored in memory”) configured to store image data from a camera of the automated vehicle, the image data includes a digital representation of imagery in a field-of-view of the camera including an operational environment with one or more objects and a roadway having one or more driving lanes (Kocamaz, Fig. 2A, [0008], “FIGS. 2A-2D are visualizations of example annotations applied to sensor data and resulting ground truth masks used for training a neural network to assign objects to lanes, in accordance with some embodiments of the present disclosure”). The proposed combination as well as the motivation for combining the Kocamaz, Saggu, Zhao, and Shima references presented in the rejection of Claim 7-8, apply to Claims 17-18 and are incorporated herein by reference. Thus, the system recited in Claim 17-18 is met by Kocamaz, Saggu, Zhao, and Shima.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Foster et al., (US 2022/0348227 A1) – teaches the ability to identify that there is at least one vehicle in the shoulder lane (Foster, [0121]).
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 DENISE G ALFONSO whose telephone number is (571)272-1360. The examiner can normally be reached Monday - Friday 7:30 - 5:30.
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, Amandeep Saini can be reached at (571)272-3382. 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.
/DENISE G ALFONSO/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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