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 .
Claim Status
Claims 1-4 and 6-8 are pending for examination in the application filed 06/26/2026. Claim 2 has been amended, claim 5 has been cancelled, and claims 6-8 are new.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been received in parent application JP2023-125743; filing date 08/01/2023.
Response to Arguments and Amendments
Applicant's arguments filed 06/26/2026 regarding the combination of Suggu and Yasui have been fully considered but they are not persuasive. Applicant argues on page 4 of the Remarks that Yasui fails to teach a single combined target image input to a model. Stated on page 4 of the Non-Final Rection filed 04/01/2026, Suggu teaches:
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Applicant does not present arguments as to why Suggu fails to teach the amended limitation of “and a model configured to receive the single target image and position of the target region and configured to output a recognition result for a lane line of a road and at least one of a traffic light and a signboard”. Suggu teaches a single target image, as shown in Figure 6. Please see the updated 35 U.S.C. Rejections based on the amendments below.
Applicant further argues that the combination of Suggu and Yasui does not teach the amended limitation of claim 1: “wherein the controller determines the position of the first region and the position of the second region based on a position of the lane line recognized by the model”, previously presented in now cancelled claim 5. Pages 6-7 of the Non-Final Rejection filed 04/01/2026 describe:
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Applicant specifically argues on page 5 that “None of these disclosures teach or suggest a closed-loop control in which the position of the first region and the second region are dynamically determined based on the position of the lane line recognized by the model itself”. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). For further clarification, as described in the Suggu citations above, the target region of Suggu is comprised of a first region and second region, which correspond to the plurality of vehicles. Suggu further describes that after the image data is collected, an object detection model is utilized to identify objects such as lane markings, which are then used to determine the drivable area of the road. In Suggu, this drivable area of the road is where the images of the vehicles are extracted from. Thus, Suggu teaches the limitation of “wherein the controller determines the position of the first region and the position of the second region based on a position of the lane line recognized by the model” in claim 1.
Applicant further argues on page 5 of the Remarks that there is a lack of motivation to combine Yasui with Suggu. Applicant specifically argues that the technical fields, processing methodologies, and objectives and Suggu and Yasui are substantially different. As stated in the Non-Final Rejection filed 04/01/2026, Yasui is in the same field of endeavor of vehicle image analysis. MPEP 904.01(c) describes that determination of what arts are analogous to a particular claimed invention depends upon the necessary essential function or utility of the subject matter covered by the claims, and not upon what it is called by the applicant. See MPEP § 2141.01(a) for a discussion of analogous and nonanalogous art in the context of establishing a prima facie case of obviousness under 35 U.S.C. 103. Furthermore, in order to establish a prima facie case of obviousness, Examiner must set forth (a) the relevant teachings of the prior art relied upon, (b) the differences between the prior art in the claim and the applied references, (c) the proposed modification of the applied references necessary to arrive at the claimed subject matter, and (d) an explanation as to why the claimed invention would have been obvious to one of ordinary skill in the art at the relevant time. See MPEP 2142. Here, Examiner has mapped the Suggu reference to the claim, explained the deficiencies of the Suggu reference, proposed a modification of the Suggu reference with the Yasui reference, and provided a motivation for the combination. Therefore, a prima facie case of obviousness has been made. Please see the updated 35 U.S.C 103 rejections in view of the amendments.
Applicant’s arguments with respect to the newly added limitation of “such that the target region has a convex shape or an L-shape formed by combining the first region and the second region” of 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, as facilitated by the newly added amendments.
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 1-3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Suggu (US20240001849A1) in view of Yasui (US20020031242A1) and Sajjadi (US20200293796A1).
Regarding claim 1, Suggu teaches a device for recognizing an object, comprising: an image sensor configured to acquire an image of a travel direction of a vehicle ([0009] In one aspect, a method is implemented at a computer system including one or more processors and memory to augment training data used for vehicle driving modelling. The method includes obtaining a first image of a road. [0129] In some embodiments, the first image 602 is captured by a camera 266 facing forward to a driving direction of an ego vehicle 102. [0105] In this way, video and image data can be processed by the CNN for video and image recognition or object detection);
a controller (vehicle control system) configured to clip a target region from an image acquired by the image sensor and configured to set the target region as a target image, the target region including a first region and a second region ([0110] The computer system obtains an image of an object 618. In some embodiments, the object includes a vehicle 102, and an image of the vehicle 618A is extracted (622) from a drivable area 606 of a road in a first road image 620A. In some embodiments, the object includes a plurality of vehicles 102 located at different depths of a second road image 620B. An image of the plurality of vehicles 618B is extracted (622) from a drivable area 606 of a road in the second road image 620B. In some embodiments, the object includes one or more traffic safety objects (e.g., a barrel and a delineator). Two images of traffic safety objects 618C and 618D are extracted (622) from a drivable area 606 of a road in the third road image 620C. In some embodiments, each image of an object 618 corresponds to one or more rectangular bounding boxes in the corresponding road image 620 (e.g., in image 620A, 620B, or 620C). The road image 620 is cropped according to the one or more rectangular bounding boxes to generate the image of the object 618. Further, in some embodiments, a background portion of the image of the object 618 is made transparent, while a foreground portion of the image of the object 618 remains opaque, containing visual information concerning the object);
and a model configured to receive the single target image and position of the target region and configured to output a recognition result for a lane line of a road ([0141] In some embodiments, the computer system generates the second image 604 by extracting (1418) the image of the object 618 from the first image 602 at a first location within the first image 602, selecting (1420) a second location in the drivable area 606, and overlaying (1422) the image of the object 618 at the second location of the drivable area 606. [0120] In some embodiments, each solid edge marking 612, broken lane marking 614, or shoulder barrier structure 616 is recognized, and associated with a respective edge line 802, lane line 804, and shoulder line 806, respectively. The drivable area 606 of the road is bound by two edge lines 802 in the first and second images 602 and 604. Each of the edge line 802, the lane line 804, and the shoulder line 806 is associated with a set of pixels of the first image 602 that is marked with the respective line 802, 804, or 806. The pixel locations of the edge lines 802, lane lines 804, and shoulder lines 806 form the first ground truth associated with the first image 602) and at least one of a traffic light and a signboard ([0055] In some embodiments, deep learning techniques are applied by the vehicles 102, the servers 104, or both, to process the vehicle data 112. For example, in some embodiments, after image data is collected by the cameras of one of the vehicles 102, the image data is processed using an object detection model to identify objects (e.g., road features including, but not limited to, vehicles, lane lines, shoulder lines, road dividers, traffic lights, traffic signs, road signs, cones, pedestrians, bicycles, and drivers of the vehicles) in the vehicle driving environment 100);
wherein the controller determines the position of the first region and the position of the second region based on position of the lane line recognized by the model ([0055] In some embodiments, deep learning techniques are applied by the vehicles 102, the servers 104, or both, to process the vehicle data 112. For example, in some embodiments, after image data is collected by the cameras of one of the vehicles 102, the image data is processed using an object detection model to identify objects (e.g., road features including, but not limited to, vehicles, lane lines, shoulder lines, road dividers, traffic lights, traffic signs, road signs, cones, pedestrians, bicycles, and drivers of the vehicles) in the vehicle driving environment 100. [0124] In some embodiments, the drivable area detection model is applied to recognize the drivable area 606 of the road, a road area 608, and a shoulder area 610. Further, in some embodiments, the drivable area detection model is applied to identify one or more of solid edge markings 612, broken lane markings 614, and shoulder barrier structures 616 and apply them to define the drivable area 606, road area 608, and shoulder area 610 of the road. [0120] In some embodiments, each solid edge marking 612, broken lane marking 614, or shoulder barrier structure 616 is recognized, and associated with a respective edge line 802, lane line 804, and shoulder line 806, respectively. The drivable area 606 of the road is bound by two edge lines 802 in the first and second images 602 and 604. Each of the edge line 802, the lane line 804, and the shoulder line 806 is associated with a set of pixels of the first image 602 that is marked with the respective line 802, 804, or 806. The pixel locations of the edge lines 802, lane lines 804, and shoulder lines 806 form the first ground truth associated with the first image 602. [0141] In some embodiments, the computer system generates the second image 604 by extracting (1418) the image of the object 618 from the first image 602 at a first location within the first image 602, selecting (1420) a second location in the drivable area 606, and overlaying (1422) the image of the object 618 at the second location of the drivable area 606. The image of the object 618 is retained at the first location, while it is duplicated to the second location. Specifically, a first set of pixels corresponding to a bottom surface of the object are aligned on a z-axis with a second set of pixels corresponding to the second location of the drivable area 606 of the road, such that the first set of pixels of the object is placed immediately adjacent to or overlap the second set of pixels of the drivable area 606. In some embodiments, the first and second locations are identified based on depths measured with reference to a camera location. Alternatively, in some embodiments, the first image 602 is divided to a plurality of rows and columns, and the first and second locations are identified based on a vertical (row) position 1002, a horizontal (column) position 1102, or both on the first image 602).
Suggu does not teach the target region including a first region below a center of the image and a second region above the center of the image, the second region being adjacent to the first region and having an area smaller than that of the first region.
Yasui, in the same field of endeavor of vehicle image analysis, teaches the target region including a first region below a center of the image and a second region above the center of the image, the second region being adjacent to the first region and having an area smaller than that of the first region (see Fig. 5. [0099] More specifically, the perspective image Vi (FIG. 5) is divided into two parts, a top region St and a bottom region Sb, using a single horizontal line Ls. Note that the horizontal line Ls is aligned to the A-th vertical pixel counted from the bottom of the image. The area of the top region St is therefore Ph.times.(PV-A) pixels, and the area of the bottom region Sb is Ph.times.A pixels. The vertical position of this A-th pixel is preferably set to match the horizontal position of the vanishing point of perspective image Vi when the perspective image Vi is captured with the automobile AM on a level road. [0064] As shown in FIG. 1, the local positioning apparatus LP comprises a digital imaging apparatus 100, spatial frequency separator 200, lane area detector 300, lane contour detector 40a, lane detector 500, and electronic control unit (ECU) 700, Note that the ECU 700 is a device commonly used and known in the automobile industry, and is used to detect the vehicle condition as represented by the speed of travel and steering condition, generate a vehicle condition signal Sc, which includes a velocity signal Sv and steering signal Ss, and controls the various electrical devices of the vehicle).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Yasui for the target region to include a first region below a center of the image and a second region above the center of the image, the second region being adjacent to the first region and having an area smaller than that of the first region because "To reduce the amount of data that must be filtered for this extraction, the filtering operation is limited to a specifically limited part of the perspective image Vi. It is therefore possible to more quickly detect the perspective image edge Vh data, and generate the high spatial frequency signal SH" [Yasui 0098].
Suggu does not teach such that the target region has a convex shape or an L-shape formed by combining the first region and the second region.
Sajjadi, in the same field of endeavor of vehicle image analysis, teaches such that the target region has a convex shape or an L-shape formed by combining the first region and the second region ([0049] FIG. 3B illustrates encoding an intersection coverage map by reducing a size of an intersection coverage map based on a first bounding box 322 overlapping with a second bounding box 324 representing another intersection, in accordance with some embodiments of the present disclosure. Image 320 is annotated with the first bounding box 322 corresponding to a first intersection and the second bounding box 324 corresponding to a second intersection in an instance of sensor data 102 (e.g., the image 320). In an initial intersection coverage map 326, the pixels or points within the initial first bounding box 322 and the second bounding box 324 may be encoded with intersection location information. Based on the first bounding box 322 and the second bounding box 324 overlapping, the initial intersection coverage map 326 may reflect the same, such that some values corresponding to the overlapping region may represent both intersections and thus may result in training the machine learning model(s) 104 to inaccurately predict the bounding box locations).
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Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Sajjadi for the target region to have a convex or L shape formed by combining the first and second regions because "As described herein, reducing the size of one or more intersection coverage maps 126 may help the machine learning model(s) 104 learn to clearly delineate multiple bounding boxes for intersections thereby enabling the machine learning model(s) 104 to accurately and efficiently detect multiple intersections in a single instance of the sensor data 102" [0049].
Regarding claim 2, Suggu, Yasui, and Sajjadi teach the device of claim 1. Suggu further teaches wherein the controller includes a table storing an identifier of the image sensor, the position of the first region, and the position of the second region in association with each other, and determines the position of the first region and the position of the second region based on the identifier of the image sensor and the table ([0141] In some embodiments, the computer system generates the second image 604 by extracting (1418) the image of the object 618 from the first image 602 at a first location within the first image 602, selecting (1420) a second location in the drivable area 606, and overlaying (1422) the image of the object 618 at the second location of the drivable area 606. The image of the object 618 is retained at the first location, while it is duplicated to the second location. Specifically, a first set of pixels corresponding to a bottom surface of the object are aligned on a z-axis with a second set of pixels corresponding to the second location of the drivable area 606 of the road, such that the first set of pixels of the object is placed immediately adjacent to or overlap the second set of pixels of the drivable area 606. In some embodiments, the first and second locations are identified based on depths measured with reference to a camera location. Alternatively, in some embodiments, the first image 602 is divided to a plurality of rows and columns, and the first and second locations are identified based on a vertical (row) position 1002, a horizontal (column) position 1102, or both on the first image 602).
Regarding claim 3, Suggu, Yasui, and Sajjadi teach the device of claim 1. Suggu further teaches further comprising a yaw rate sensor configured to detect a yaw rate of the vehicle, wherein the controller determines the position of the first region and the position of the second region based on the yaw rate of the vehicle ([0048] For each vehicle 102, the plurality of sensors includes one or more of…(8) an inertial navigation system (INS) including accelerometers and gyroscopes… The cameras are configured to capture a plurality of images in the vehicle driving environment 100, and the plurality of images are applied to map the vehicle driving environment 100 to a 3D vehicle space and identify a location of the vehicle 102 within the environment 100. The cameras also operate with one or more other sensors (e.g., GPS, LiDAR, RADAR, and/or INS) to localize the vehicle 102 in the 3D vehicle space…Data collected by these sensors is used to determine vehicle locations determined from the plurality of images or to facilitate determining vehicle locations between two images. [0073] sensor data 254 captured or measured by the plurality of sensors 260; [0074] mapping and location data 256, which is determined from the sensor data 254 to map the vehicle driving environment 100 and locations of the vehicle 102 in the environment 100).
Regarding claim 7, Suggu, Yasui, and Sajjadi teach the device of claim 1. Suggu does not explicitly teach wherein a width of the second region in a horizontal direction of the image is smaller than a width of the first region in the horizontal direction of the image.
Sajjadi, in the same field of endeavor of vehicle image analysis, teaches wherein a width of the second region in a horizontal direction of the image is smaller than a width of the first region in the horizontal direction of the image.
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Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Sajjadi for a width of the second region to be smaller than a width of the first region in the horizontal direction of the image for "encoding an intersection coverage map by reducing a size of an intersection coverage map based on a first bounding box 322 overlapping with a second bounding box 324 representing another intersection…As described herein, reducing the size of one or more intersection coverage maps 126 may help the machine learning model(s) 104 learn to clearly delineate multiple bounding boxes for intersections thereby enabling the machine learning model(s) 104 to accurately and efficiently detect multiple intersections in a single instance of the sensor data 102" [0049].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Suggu in view of Yasui, Sajjadi, and Akimoto (US20230134579A1).
Regarding claim 4, Suggu, Yasui, and Sajjadi teach the device of claim 1. Akimoto, in the same field of endeavor of vehicle image analysis, teaches further comprising a sensor configured to detect a direction of a direction indicator light indicating a travel direction of the vehicle, wherein the controller determines the position of the first region and the position of the second region based on the direction indicated by the direction indicator light ([0093] The direction indicating operation detection unit 100 detects an operation of turning on a direction indicator such as a turn (signal) switch or a turn (signal) lever, that is performed when the vehicle turns right or left or changes lanes with a temporary change in traveling direction and a direction of the direction indication and notifies the image cutting position changing unit 52 of the operation and the direction. Here, the direction indicating operation detection unit 100 functions as a direction detection unit that executes a direction detection step for detecting a change in traveling direction of the moving apparatus by detecting the operation direction of the direction indicator provided in the moving apparatus. [0046] In the first embodiment, the position to be cut includes a second region R2 corresponding to an angle of view for imaging the side behind the vehicle itself at a wide angle at the time of backward traveling, the image of which has been formed by the optical system 10. Also, the position includes a first region R1 corresponding to the angle of view of high resolution for observing the vehicle 300 on the side behind the vehicle itself at the time of ordinary traveling. [0096] In a case where the right or left direction indicator is turned on, for example, the cutting position is changed to the predetermined cutting position (fourth region R4).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Akimoto to determine the position of the regions based on the direction indicated by the direction indicator light "such that the part corresponding to a blind angle which is difficult to be checked with a side mirror on the right or left side behind the vehicle itself can be imaged" [Akimoto 0096].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Suggu in view of Yasui, Sajjadi, and Nishikawa (US20220185200A1).
Regarding claim 6, Suggu, Yasui, and Sajjadi teach the device of claim 3. Suggu does not explicitly teach wherein the controller determines an amount of movement of the first region and an amount of movement of the second region based on a magnitude of the yaw rate of the vehicle, and the amount of movement of the second region is greater than the amount of movement of the first region.
Nishikawa, in the same field of endeavor of vehicle image analysis, teaches wherein the controller determines an amount of movement of the first region and an amount of movement of the second region based on a magnitude of the yaw rate of the vehicle, and the amount of movement of the second region is greater than the amount of movement of the first region ([0069] FIG. 12 is a plan view which demonstrates the subject vehicle 80 while cornering. When the near-field region A1 and the far-field region A2 are, as indicated by broken lines, defined in front of the subject vehicle 80 while cornering…When the yaw rate of the subject vehicle 80 is higher than the given yaw rate, the boundary determiner 41 shifts the near-field region A1 and the far-field region A2 in a direction in which the yaw rate acts on the subject vehicle 80, in other words, the subject vehicle 80 is yawing and defines regions, as indicated by solid lines).
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Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Nishikawa to determine an amount of movement of the first region and an amount of movement of the second region based on a magnitude of the yaw rate of the vehicle, and the amount of movement of the second region is greater than the amount of movement of the first region because "while cornering…the boundary determiner 41 defines and shifts the near-field region A1 and the far-field region A2 in the direction in which the subject vehicle 80 is yawing with an increase in yaw rate acting on the subject vehicle 80" [0069].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Suggu in view of Yasui, Sajjadi, and Smith (US20230281819A1).
Regarding claim 8, Suggu, Yasui, and Sajjadi teach the device of claim 1. Suggu does not explicitly teach wherein the controller divides the image into a matrix of blocks each having an identifier, and determines the position of the first region and the position of the second region based on the identifiers of the blocks.
Smith, in the same field of endeavor of image analysis, teaches wherein the controller divides the image into a matrix of blocks each having an identifier, and determines the position of the first region and the position of the second region based on the identifiers of the blocks ([0072] FIG. 4 is a schematic illustration of the grid overlay image 304. The grid overlay image 304 may be partitioned such that each grid unit (the squares in the example illustrated in FIG. 4) is identifiable with a unique name. For example, the grid units across the top row may be named A1, A2, A3, A4, A5, A6, A7, A8, A9, and A10, and the grid units across the second row may be named B1, B2, B3, B4, B5, B6, B7, B8, B9, and B10, and so forth. The plurality of square grids within the grid overlay image 304 may be preferrable to simplify the grid overlay and simply the naming of each grid unit. [0087] For example, the neural network first analyzes grid unit A1 to determine the following: (a) an x-coordinate for grid unit A1; (b) a y-coordinate for grid unit A1; (c) a width of grid unit A1; (d) a height of grid unit A1; (e) a determination of whether grid unit A1 includes an object of interest. The neural network is trained to identify and classify certain objects of interest and may be trained to ignore other objects within the image. [Abstract] The method includes generating a bounding box around the object of interest, identifying one or more grid units of the plurality of grid units that comprise a portion of the bounding box, and identifying which of the one or more grid units comprises a center point of the bounding box).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the device of Suggu with the teachings of Smith to divide the image into a matrix of blocks and determine the position of the first region and the position of the second region based on the identifiers of the blocks "to simplify the grid overlay and simply the naming of each grid unit" [0072] and "The grid overlay image 304 is then processed with a machine learning algorithm (such as the neural network 114) to identify, box, and classify the objects within the input image 302" [0064].
Conclusion
THIS ACTION IS MADE FINAL. 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 Jacqueline R Zak whose telephone number is (571)272-4077. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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