CTFR 18/452,272 CTFR 86353 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the amendments filed on March 16, 2026. Claim 3 is canceled; and claims 1-2 and 4-21 are pending and examined below. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claims 1-2 and 4-21 are reject ed under 35 U.S.C. 103 as being unpatentable over Moak (U. S. 2023/0184953) in view of Huh (U.S. 2020/0192091) . With re gard to claim 1, Moak teaches a system of object detection for a trailer connected to a vehicle ([abstract] determining an angle of articulation between a vehicle and a trailer; [0002] systems and methods to detect trailer articulation angles) , the system comprising: a camera positioned at a rear end of the vehicle ([0043] the side mirrors 510 may provide mounting locations for rear-facing cameras 528) , the camera configured to capture images of the trailer ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , the images including an object other than the trailer ([0031] perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500; [0032] The images and location information may be processed to identify or detect objects around or in the path of the semi-truck 500) ; a sensor configured to capture sensor data about the object ([0003] a sensor (such as, for example, a lidar sensor or sensors) may be mounted on a back of a vehicle (e.g., such as on a rear surface of a cab of a semi-truck) and directed toward a front face of a trailer to capture sensor data) , a display configured to display images from a perspective of the camera ([0004] cause information to be displayed on a display device; [0018] information may be displayed to the operator on a user interface (such as a display device mounted in the cab of the vehicle )) , and a controller on the vehicle (Fig. 4, controller 482; [0030] control signals to control one or more actuators or other controllers associated with systems of the vehicle) , the controller including an input/output interface (Fig. 4, input/output interfaces 448; [0032] The computer system 440 may also cause information associated with the trailer orientation to be displayed to an operator (e.g., via I/O devices 448)) , a memory ([0023] The angle of articulation determined at 304 may be stored, e.g., in a memory device of the truck 102 (e.g., such as in a memory associated with the control system 400)) , and an electronic processor (Fig. 4, CPU(s) 442) configured to: receive the image data from the camera ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , receive the sensor data from the sensor ([0003] a sensor (such as, for example, a lidar sensor or sensors) may be mounted on a back of a vehicle (e.g., such as on a rear surface of a cab of a semi-truck) and directed toward a front face of a trailer to capture sensor data) , determine, from the image data ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , a blind spot ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120) caused by obstruction by the trailer ([0019] Pursuant to some embodiments, alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate)) , analyze the sensor data for radar data associated with the object ([0003] the sensor data is analyzed to identify a current plane occupied by a front face of the trailer; [0031] one or more lidar 414 and radar 416 sensors may be positioned to sense or detect the presence and volume of objects proximate or in the path of the semi-truck 500) , calculate the position of the object relative to the blind spot ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120) , determine that the object is within the blind spot using an object detection algorithm ([0026-[0027] A representative example of an approach to processing can be seen in FIG. 2D in which a trailer 120 is shown in two positions (a first position shown as 120 where the front of the trailer is parallel to the rear of the truck 102 and a second position shown as 120′ where the trailer is articulated)…) , and in response to the determination that the object is within the blind spot, generate an augmented image including a representation of the object for presentation by the display ([0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) . However, Moak does not specifically teach: - a visual representation of the blind spot and the object within the blind spot Huh teaches a method of providing driving information of a vehicle and tracking and recognizing driver gaze of the vehicle [abstract]. Huh teaches an image of a blind spot that is not viewed by a side mirror ([0009]; [0037] when a vehicle performs an operation such as driving or parking, an image of a blind spot depending on a heading direction on a navigation path of the vehicle or a driver gaze direction may be received from a surrounding server or the like and may be displayed by an augmented reality device) . Huh also teaches a visual representation of the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle; [0047] image information on a position hidden by an obstacle of a blind spot on a driving path; [0064] he left lane of the front side corresponds to a blind spot hidden by a vehicle 610 that drives on a front left side, and thus, the controller 150 may acquire a camera image 143 of a front side of the front vehicle 610 through the communication unit 130 and may display the camera image 143 through the augmented reality provision unit 140) and the object within the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle) . Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the blind spot shown through object detection as taught by Moak, with the visual representation of a blind spot as taught by Huh, to have achieved a system and method for providing driving information of a vehicle and an image of a blind spot that is not viewed by a side mirror or the like. With regard to claim 2, the limitations are addressed above and Moak teaches wherein the augmented image includes proximity information of the object relative to the trailer ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) . With regard to claim 4, the limitations are addressed above and Moak teaches wherein the augmented image includes more than one blind spot ([0001] equip trailers with external sensors or cameras to reduce the blind spots; [0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) . With regard to claim 5, the limitations are addressed above and Moak teaches wherein the augmented images include more than one object other than the trailer ([0031] one or more lidar 414 and radar 416 sensors may be positioned to sense or detect the presence and volume of objects proximate or in the path of the semi-truck 500. Pursuant to some embodiments, one or more of the lidars 414 may be a rearward-facing lidar (such as, for example, the lidar 104 of FIG. 1, or the lidar 540 of FIG. 5A) which is used to monitor an orientation of a trailer) . With regard to claim 6, the limitations are addressed above and Moak teaches wherein the sensor is configured to capture sensor data about more than one object ([0032] Other sensors may also be positioned or mounted on various locations of the semi-truck 500 to capture other information such as position data. For example, the sensors may include one or more satellite positioning sensors and/or inertial navigation systems such as GNSS/IMU 418) . With regard to claim 7, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to calculate the position of the more than one object relative to the blind spot and the trailer ([0004] a position of the rear of the trailer may be determined using the angle of articulation as well as information identifying one or more dimensions of the trailer; [0018] large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer …allow the determination of the angle of articulation as well as the computation of the pose, position or trajectory of the truck 102 and trailer 120) , and determine that one of the more than one object is within the blind spot using the object detection algorithm ([0026-[0027] A representative example of an approach to processing can be seen in FIG. 2D in which a trailer 120 is shown in two positions (a first position shown as 120 where the front of the trailer is parallel to the rear of the truck 102 and a second position shown as 120′ where the trailer is articulated)…) . With regard to claim 8, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to analyze the sensor data for radar data associated with the more than one object ([0003] the sensor data is analyzed to identify a current plane occupied by a front face of the trailer; [0031] one or more lidar 414 and radar 416 sensors may be positioned to sense or detect the presence and volume of objects proximate or in the path of the semi-truck 500) and calculate the position of all of the more than one object relative to the blind spot and the trailer ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120) . With regard to claim 9, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to generate an augmented image including the more than one object for presentation by the display ([0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) . With regard to claim 10, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to generate an augmented image including all of the more than one object for presentation by the display ([0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) . With regard to claim 11, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to generate an augmented image including proximity information for the more than one object for presentation by the display ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) . With regard to claim 12, the limitations are addressed above and Moak teaches wherein the electronic processor is further configured to generate an augmented image including the more than one object and more than one blind spot ([0001] equip trailers with external sensors or cameras to reduce the blind spots; [0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) . With regard to claim 13, the method claim corresponds to the system claim 1, respectively, and therefore is rejected with the same rationale. With regard to claim 14, the method claim corresponds to the system claim 4, respectively, and therefore is rejected with the same rationale. With regard to claim 15, the method claim corresponds to the system claim 4 and 6, respectively, and therefore is rejected with the same rationale. With regard to claim 16, the method claim corresponds to the system claim 7, respectively, and therefore is rejected with the same rationale. With regard to claim 17, the method claim corresponds to the system claim 8, respectively, and therefore is rejected with the same rationale. With regard to claim 18, Moak teaches a system of object detection for a trailer connected to a vehicle ([abstract] determining an angle of articulation between a vehicle and a trailer; [0002] systems and methods to detect trailer articulation angles) , the system comprising: a camera positioned at a rear end of the vehicle ([0043] the side mirrors 510 may provide mounting locations for rear-facing cameras 528) , the camera configured to capture images of the trailer ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , the images including an object other than the trailer ([0031] perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500; [0032] The images and location information may be processed to identify or detect objects around or in the path of the semi-truck 500) ; a sensor configured to capture sensor data about the object ([0003] a sensor (such as, for example, a lidar sensor or sensors) may be mounted on a back of a vehicle (e.g., such as on a rear surface of a cab of a semi-truck) and directed toward a front face of a trailer to capture sensor data) , the sensor data including a proximity of the object to the trailer ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , a display configured to display images from the perspective of the camera ([0004] cause information to be displayed on a display device; [0018] information may be displayed to the operator on a user interface (such as a display device mounted in the cab of the vehicle )) , a controller on the vehicle (Fig. 4, controller 482; [0030] control signals to control one or more actuators or other controllers associated with systems of the vehicle) , the controller including an input/output interface (Fig. 4, input/output interfaces 448; [0032] The computer system 440 may also cause information associated with the trailer orientation to be displayed to an operator (e.g., via I/O devices 448)) , a memory ([0023] The angle of articulation determined at 304 may be stored, e.g., in a memory device of the truck 102 (e.g., such as in a memory associated with the control system 400)) , and an electronic processor (Fig. 4, CPU(s) 442) configured to: receive the image data from the camera ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , receive the sensor data from the sensor ([0003] a sensor (such as, for example, a lidar sensor or sensors) may be mounted on a back of a vehicle (e.g., such as on a rear surface of a cab of a semi-truck) and directed toward a front face of a trailer to capture sensor data) , determine, from the image data ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , a blind spot ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120) caused by obstruction by the trailer ([0019] Pursuant to some embodiments, alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate)) , analyze the sensor data for radar data associated with the object ([0003] the sensor data is analyzed to identify a current plane occupied by a front face of the trailer; [0031] one or more lidar 414 and radar 416 sensors may be positioned to sense or detect the presence and volume of objects proximate or in the path of the semi-truck 500) , calculate the position of the object relative to the blind spot and the trailer ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120) , determine that the object is within the blind spot using an object detection algorithm ([0026-[0027] A representative example of an approach to processing can be seen in FIG. 2D in which a trailer 120 is shown in two positions (a first position shown as 120 where the front of the trailer is parallel to the rear of the truck 102 and a second position shown as 120′ where the trailer is articulated)…) , and determine if the object has exceeded a proximity threshold ([0019] Pursuant to some embodiments, alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate)) , wherein response to the determination that the object is within the blind spot ([0031] the control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) , generate an augmented image including a representation of the object for presentation via the display ([0025] the pose or position data associated with the trailer 120 may be augmented with other data available to the control system 400 including, for example, speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like)) , and wherein response to the determination that the object has exceeded a proximity threshold ([0019] Pursuant to some embodiments, alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate) , controlling an aspect of the vehicle ([0019] Pursuant to some embodiments, alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate). In some embodiments, information associated with the angle of articulation may be analyzed in conjunction with other information about a state of the vehicle) . However, Moak does not specifically teach: - a visual representation of the blind spot and the object within the blind spot Huh teaches a method of providing driving information of a vehicle and tracking and recognizing driver gaze of the vehicle [abstract]. Huh teaches an image of a blind spot that is not viewed by a side mirror ([0009]; [0037] when a vehicle performs an operation such as driving or parking, an image of a blind spot depending on a heading direction on a navigation path of the vehicle or a driver gaze direction may be received from a surrounding server or the like and may be displayed by an augmented reality device) . Huh also teaches a visual representation of the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle; [0047] image information on a position hidden by an obstacle of a blind spot on a driving path; [0064] he left lane of the front side corresponds to a blind spot hidden by a vehicle 610 that drives on a front left side, and thus, the controller 150 may acquire a camera image 143 of a front side of the front vehicle 610 through the communication unit 130 and may display the camera image 143 through the augmented reality provision unit 140) and the object within the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle) . Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the blind spot shown through object detection as taught by Moak, with the visual representation of a blind spot as taught by Huh, to have achieved a system and method for providing driving information of a vehicle and an image of a blind spot that is not viewed by a side mirror or the like. With regard to claim 19, the limitations are addressed above and Moak teaches wherein response to the determination that the object has exceeded a proximity threshold, the controller displays a proximity alarm on the display ([0004] cause information to be displayed on a display device or to cause the generation of an alarm or alert; [0019] alarms or alerts may be generated when the angle of articulation exceeds a threshold (e.g., where the truck 102 and trailer 120 are in danger of jackknifing—a situation where the trailer pushes the truck forward causing it to rotate)…Alarms or alerts may also be triggered in situations where the rear of the trailer 120 may swing or extend out of a lane during a turn) . With regard to claim 20, the limitations are addressed above and Moak teaches wherein response to the determination that the object has exceeded a proximity threshold, the controller controls brakes of the vehicle ([0018] in autonomous or semi-autonomous systems or modes of operation, this information may be used to control or otherwise influence the operation of the truck 102 (e.g., by controlling the throttle, steering, brakes, or other components of the truck 102); [0032] to identify or detect objects around or in the path of the semi-truck 500 and control signals may be emitted to adjust the throttle 484, steering 486 or brakes 488 as needed to safely operate the semi-truck 500… The computer system 440 may include computer code which operates to perform a process such as the process 300 of FIG. 3 to update control systems (e.g., such as the throttle 484, steering 486, or brakes 488) based on the trailer orientation) . With regard to claim 21, the limitations are addressed above and Moak teaches wherein the augmented image includes a graphical rendering of the trailer (Figs. 2C-2D; [0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120; [0031] control system 400 may be operated to capture images from one or more cameras 412 mounted on various locations of the semi-truck 500 and perform processing (such as image processing) on those images to identify objects proximate or in a path of the semi-truck 500) . However, Moak does not specifically teach: - with a transparency effect applied to at least a portion of the trailer, such that the visual representation of the object on the display appears visually superimposed in the blind spot caused by obstruction by the trailer Huh teaches a visual representation of the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle; [0047] image information on a position hidden by an obstacle of a blind spot on a driving path; [0064] he left lane of the front side corresponds to a blind spot hidden by a vehicle 610 that drives on a front left side, and thus, the controller 150 may acquire a camera image 143 of a front side of the front vehicle 610 through the communication unit 130 and may display the camera image 143 through the augmented reality provision unit 140) and the object within the blind spot (Figs. 4-6; [0037] an image of a blind spot depending on a heading direction on a navigation path of the vehicle) . Huh also teaches with a transparency effect applied to at least a portion of the trailer, such that the visual representation of the object on the display appears visually superimposed in the blind spot caused by obstruction by the trailer (Figs. 4-6; [0060] the controller 150 may acquire a camera image 141 around corresponding coordinates through the communication unit 130 and may display the camera image 141 through the augmented reality provision unit 140; [0062] the controller 150 may acquire a camera image 142 around corresponding coordinates through the communication unit 130 and may display the camera image 142 through the augmented reality provision unit 140; [0064] the controller 150 may acquire a camera image 143 of a front side of the front vehicle 610 through the communication unit 130 and may display the camera image 143 through the augmented reality provision unit 140) . Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the blind spot shown through object detection as taught by Moak, with the visual representation of a blind spot as taught by Huh, to have achieved a system and method for providing driving information of a vehicle and an image of a blind spot that is not viewed by a side mirror or the like . Response to Arguments 07-37 AIA Applicant's arguments filed 3-16-2026 have been fully considered but they are not persuasive. Applicant argues that the references do not generate an augmented image including a visual representation of the blind spot and the object within the blind spot for presentation by a display . Examiner respectfully disagrees: The Moak reference teaches a system and method to determine an angle of articulation between a vehicle and a trailer [abstract]. Moak teaches that there are a number of sensors and cameras, long range cameras, mid-range front facing cameras, rear-facing cameras, etc., attached to the semi-truck shown in Figure 5B [0043]. The cameras capture images from various locations and perform image processing on such images to identify objects in the truck’s pathway [0031]. Moak also teaches the cameras can pick up a blind spots as sown in [0018] (“if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120”). Additionally, Moak teaches determining that an object is within the blind spot, and the Moak reference does acknowledge blind spots ([0018] if the operator is backing the truck 102 and trailer 120 in reverse (e.g., to align with a loading dock or the like), large blind spots can be created when the angle of articulation increases, and it can be difficult for an operator to accurately determine a trajectory of the trailer 120), it can generate an augmented image including a representation of the object for presentation of display, or in other words, a position associated with the trailer can be augmented to the control system [0025]. Examples of such items consisted of speed, acceleration, a current turning radius of the truck 102, orientation or environmental information (such as lane or object detection from cameras, other lidar sensors, or the like) [0025]. The Huh reference was brought forth as it teaches a blind spot hidden by a vehicle, and a camera image of a front side of the front vehicle through the communication unit which displays a camera image through the augmented reality provision unit ([0064]). Huh provides image information matching an object seen by a driver of a vehicle or an image of a blind spot as taught in [0009]. Huh teaches that an image of a blind spot can be received from the surrounding servers of a vehicle and displayed by an augmented reality device which is a visual representation of the object located within the blind spot [0037]. It is understood that the augmented device shows images appearing enlarged and can serve as a visual representation of such. Huh teaches that the image information of a blind spot can show a display of an image in an augmented reality provision unit, of the image at an exact point in the visual field [0050]. If the user is shown an augmented reality version of the blind spot, then it merely serves as a representation of the object shown in the blind spot. As such, Examiner asserts that the Moak and Huh references still teach the claim limitations. Conclusion 07-39 AIA 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 ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm. 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, Kieu Vu can be reached at 571-272-4057. 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. /ANDREA C LEGGETT/Primary Examiner, Art Unit 2171 Application/Control Number: 18/452,272 Page 2 Art Unit: 2171
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