DETAILED ACTION
This Office Action is in response to Applicant’s Amendment and Remarks filed on 06/04/2026.
Claims 1-9 received on 06/04/2026 are considered in this Office Action.
Claims 1-9 are pending for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Claims 1-8 were previously rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. 12233909. In response to the submission of a Terminal Disclaimer, the rejection is withdrawn.
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
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over GAO (US 20180157267 A1), in view of Hyundai (NPL- 2017 Hyundai Santa FE Rear Parking Sensors).
GAO is cited in the IDS received on 02/10/2026 and 12/31/2024.
Regarding claim 1, GAO teaches a vehicle parking assist (para. [0004]: “parking assistance”) apparatus comprising:
at least one camera mounted on a vehicle (FIG. 1; para. [0019]: “The cameras 14, 18, and 22 can be any type of imaging device”), and an electronic control unit (para. [0019]: “image data from the cameras 14, 18, and 22 is sent to a processor, or vehicle controller 11, which processes the image data. In the case of external cameras, image data may be wirelessly transmitted to the vehicle controller 11 for use as described in any of the various examples of the present disclosure”) configured to:
execute a process of registering a parking lot (FIG. 4 S402-S410; para. [0025]: “a method 400 represents an example parking assistance algorithm. An initial portion may include the vehicle learning a desired home parking location to be used in future parking maneuvers”), including:
when the vehicle completes parking in the parking lot (FIG. 4 420; para. [0025]: “At step 402 the algorithm includes determining whether the vehicle is in a parked state.”), acquire the features of a ground of the parking lot at a parking complete position from the camera images currently taken by the at least one camera at a time when the parking of the vehicle is completed (FIG. 2-3; para. [0028]: “At step 408 the algorithm includes capturing a reference image from one or more image capture devices which are positioned to generate images indicative of the vicinity of the vehicle. The one or more images is captured and used by the vehicle processor to associate the images with the vehicle home position. Each of the images are captured as a set of reference images. In some examples, distance information is also gathered with respect to the proximity of nearby objects or markers placed by a user”; para. [0045]: “other scenarios such as docking a vehicle at an outdoor parking space, painted ground markers, signs, and parking meters, for example, may all be visually detected as stationary items and similarly used for vehicle positioning”), and
register the acquired features taken at the time when the parking of the vehicle is completed as first features of the registered parking lot (FIG. 4 410; para. [0029]: “At step 410, both of the set of reference images and the home position may be stored in a memory of the vehicle processor.”);
after the process of registering the parking lot (FIG. 4; para. [0029]: “At step 410, both of the set of reference images and the home position may be stored in a memory of the vehicle processor”), execute a process of autonomous parking of the vehicle (FIG. 4 422; para. [0034]: “If at step 422 the autonomous docking is activated, one or more controllers associated with vehicle guidance is programmed to autonomously steer the vehicle at step 424 from vehicle current position toward the home vehicle position”) including:
while moving the vehicle by performing the autonomous parking, determine a positional relationship between the vehicle and the registered parking lot by comparing features in the currently acquired camera images obtained while performing the autonomous parking and the registered first features that were acquired at the time when the parking of the vehicle was completed previously (para. [0032]: “the current vehicle position is compared against the home vehicle position to determine a required movement to converge the current vehicle position with the home vehicle position. Such a comparison may include comparing the location data to acquire rough positioning guidance based on the macro level location information. The comparison also includes comparing the image data representing the vicinity of the vehicle for more granular positioning guidance to enable a precise return to a previously-learned vehicle position”; para. [0033]: “informing a driver about the current vehicle position status relative to the target home vehicle position”; para. [0054]: “Visible patterns from the images are used to assess the accuracy of the vehicle parked position relative features from the external patterns”; para. [0003]: “controller is further programmed to capture a current image corresponding to a current vehicle position in response to a subsequent approach toward the vicinity, and compare the current image to the reference image”), but fails to specifically teach the vehicle completes parking in the parking lot by a parking assist control.
However, Hyundai teaches the vehicle completes parking in the parking lot by a parking assist control (FIG. 1-FIG. 7, wherein parking assist control comprises of camera images and parking sensors along with audible warning based on proximity as shown in FIGs. 1-7).
GAO is considered to be analogous to the claimed invention because it is in the same field of autonomous parking. Hyundai is considered analogous to the claimed invention because it is reasonably pertinent to parking assist control. 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 GAO and incorporate the parking assist control of Hyundai. Doing so will enhance safety and user experience by providing assistance during manual parking performed by the driver.
Regarding claim 7, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1. GAO further teaches wherein: the acquired features on the parking lot includes information on feature points in the camera images (FIG. 4 S402-410; para. [0028]: “At step 408 the algorithm includes performing camera scene detection to identify visual characteristics of the current location. Discussed in more detail below, such visual characteristics may include the presence of predetermined markers or targets within a FOV, quick read (QR) tags containing information in a 2D bar code, or recognition of native environment landmark objects in the vicinity of the vehicle. The use of particular visual characteristics of the landscape around the vehicle which acquired by the vision system helps to increase positioning accuracy for subsequent parking maneuvers at a previously-learned location. At step 408 the algorithm includes capturing a reference image from one or more image capture devices which are positioned to generate images indicative of the vicinity of the vehicle. The one or more images is captured and used by the vehicle processor to associate the images with the vehicle home position. Each of the images are captured as a set of reference images. In some examples, distance information is also gathered with respect to the proximity of nearby objects or markers placed by a user”; para. [0041]: “Specific marker types may be used to provide information or transmit data to the vehicle. In alternate examples, a customer may position one or more predefined quick response (QR) codes in locations in a parking area that are within a FOV of the vision system 12”).
Regarding claim 8, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1. GAO further teaches wherein: the acquired features on the parking lot includes information on positions of feature points in the camera images relative to a predetermined position (FIG. 4 S402-410; para. [0028]: “At step 408 the algorithm includes performing camera scene detection to identify visual characteristics of the current location. Discussed in more detail below, such visual characteristics may include the presence of predetermined markers or targets within a FOV, quick read (QR) tags containing information in a 2D bar code, or recognition of native environment landmark objects in the vicinity of the vehicle. The use of particular visual characteristics of the landscape around the vehicle which acquired by the vision system helps to increase positioning accuracy for subsequent parking maneuvers at a previously-learned location. At step 408 the algorithm includes capturing a reference image from one or more image capture devices which are positioned to generate images indicative of the vicinity of the vehicle. The one or more images is captured and used by the vehicle processor to associate the images with the vehicle home position. Each of the images are captured as a set of reference images. In some examples, distance information is also gathered with respect to the proximity of nearby objects or markers placed by a user”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over GAO, in view of Hyundai, and further in view of Ilo (U.S. Pub No. 20190039605).
Ilo is cited in the IDS received on 12/31/2024.
Regarding claim 2, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1, but fails to specifically teach wherein: the electronic control unit is further configured to: acquire the features on the parking lot from the camera images at least once before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit starts to move the vehicle by the parking assist control; and register the acquired features as second features.
However, in the same field of endeavor, Ilo teaches wherein: the electronic control unit (FIG. 2 control unit) is further configured to: acquire the features on the parking lot from the camera images at least once before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit starts to move the vehicle by the parking assist control (FIG.7 S12-S16; para. [0058]: “the vehicle 1 to move from the position P1 (here, target position P0) with the driver's own operation. In this operation, the image capturing device 102 sequentially captures the registration time images Pi1. As shown in FIG. 7, upon detection of the registration mode (setting of registration mode) by the automated driving information registration unit 12 (step S10), the image memory 20 obtains the registration time image Pi1 from the image capturing device 102 and stores the image therein (step S12). In the automated driving information registration unit 12, the candidate feature point extraction unit 22 reads out the registration time image Pi1 from the image memory 20, and extracts the candidate feature points F1 (step S14). The candidate feature point extraction unit 22 extracts as the candidate feature points F1, all the positions that can be distinguished from the surroundings among all the objects that exist around the target position P0 (for example, that have contrast with the surroundings over a predetermined value). After the candidate feature points F1 are extracted, the automated driving information registration unit 12 detects whether the registration mode is stopped (step S16), and if the registration mode is not stopped (No at step S16), the process returns to step S12, the next registration time image Pi1 is obtained, and the candidate feature point F1 in that registration time image Pi1 is extracted”); and
register the acquired features as second features (FIG. 7 S18; para. [0059]: “the feature point selection unit 24 in the automated driving information registration unit 12 selects the feature point F2 from the candidate feature points F1 on the basis of the registration time images Pi1 (step S18). The feature point selection unit 24 selects the feature point F2 corresponding to the candidate feature point F1 that satisfies the relation in terms of the stereo photogrammetry among the candidate feature points F1, and calculates the temporary coordinate information thereof”).
Ilo is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and incorporate the teaching of Ilo and register features throughout the parking process. Doing so will enhance the accuracy of the vehicle position by using features which are visible throughout the parking process (Ilo, para. [0010]: “According to this automated driving control device, one feature point can be used as the mark for a longer time; therefore, the vehicle position in the automated driving can be calculated more accurately in an appropriate level”).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over GAO, in view of Hyundai, and further in view of Ilo and further in view of WANG (US20180194344A1).
Regarding claim 3, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1, but fails to specifically teach acquire the features on the parking lot from the camera images at least once before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control; and register the acquired features as second features.
However, in the same field of endeavor, Ilo teaches electronic control unit (Fig. 2 control unit) is further configured to: acquire the features on the parking lot from the camera images before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control (FIG. 3; FIG. 7 S12-S16; para. [0058]: “the vehicle 1 to move from the position P1 (here, target position P0) with the driver's own operation. In this operation, the image capturing device 102 sequentially captures the registration time images Pi1. As shown in FIG. 7, upon detection of the registration mode (setting of registration mode) by the automated driving information registration unit 12 (step S10), the image memory 20 obtains the registration time image Pi1 from the image capturing device 102 and stores the image therein (step S12). In the automated driving information registration unit 12, the candidate feature point extraction unit 22 reads out the registration time image Pi1 from the image memory 20, and extracts the candidate feature points F1 (step S14). The candidate feature point extraction unit 22 extracts as the candidate feature points F1, all the positions that can be distinguished from the surroundings among all the objects that exist around the target position P0 (for example, that have contrast with the surroundings over a predetermined value). After the candidate feature points F1 are extracted, the automated driving information registration unit 12 detects whether the registration mode is stopped (step S16), and if the registration mode is not stopped (No at step S16), the process returns to step S12, the next registration time image Pi1 is obtained, and the candidate feature point F1 in that registration time image Pi1 is extracted”, wherein Ilo teaches capturing features throughout the entire parking process, thus comprising of before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot), and
register the acquired features as second features (FIG. 7 S18; para. [0059]: “the feature point selection unit 24 in the automated driving information registration unit 12 selects the feature point F2 from the candidate feature points F1 on the basis of the registration time images Pi1 (step S18). The feature point selection unit 24 selects the feature point F2 corresponding to the candidate feature point F1 that satisfies the relation in terms of the stereo photogrammetry among the candidate feature points F1, and calculates the temporary coordinate information thereof”).
Ilo is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and incorporate the teaching of Ilo and register features throughout the parking process. Doing so will enhance the accuracy of the vehicle position by using features which are visible throughout the parking process (Ilo, para. [0010]: “According to this automated driving control device, one feature point can be used as the mark for a longer time; therefore, the vehicle position in the automated driving can be calculated more accurately in an appropriate level”), but fails to specifically teach before the electronic control unit completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle.
However, in the same field of endeavor, WANG teaches completes parking the vehicle in the parking lot by the parking assist control and after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle (FIG. 1E; para. [0018]: “each of the measurements can be recorded as a trail of waypoints 120. In some examples, the frequency of recording can be based on the degree of change in vehicle dynamics (e.g., if the vehicle is turning, accelerating, decelerating, etc., the frequency of recording can be greater than if the vehicle is moving in a straight line at a fixed speed).”, wherein FIG. 1E shows waypoints and vehicle moving straight until completion of parking).
WANG is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and further in view of Ilo and incorporate the teaching of WANG and register features throughout the parking process, which comprise of waypoint between moving straight and completing parking the vehicle. Doing so will enhance the accuracy of the vehicle position by storing more features throughout the parking process
Regarding claim 4, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1, but fails to specifically teach further teaches acquire the features on the parking lot from the camera images at the time when the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control.
However, in the same field of endeavor, Ilo teaches electronic control unit (Fig. 2 control unit) is further configured to: acquire the features on the parking lot from the camera images at the time when the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control (FIG. 3; FIG. 7 S12-S16; para. [0058]: “the vehicle 1 to move from the position P1 (here, target position P0) with the driver's own operation. In this operation, the image capturing device 102 sequentially captures the registration time images Pi1. As shown in FIG. 7, upon detection of the registration mode (setting of registration mode) by the automated driving information registration unit 12 (step S10), the image memory 20 obtains the registration time image Pi1 from the image capturing device 102 and stores the image therein (step S12). In the automated driving information registration unit 12, the candidate feature point extraction unit 22 reads out the registration time image Pi1 from the image memory 20, and extracts the candidate feature points F1 (step S14). The candidate feature point extraction unit 22 extracts as the candidate feature points F1, all the positions that can be distinguished from the surroundings among all the objects that exist around the target position P0 (for example, that have contrast with the surroundings over a predetermined value). After the candidate feature points F1 are extracted, the automated driving information registration unit 12 detects whether the registration mode is stopped (step S16), and if the registration mode is not stopped (No at step S16), the process returns to step S12, the next registration time image Pi1 is obtained, and the candidate feature point F1 in that registration time image Pi1 is extracted”; para. [0070]: “Note that the automated travel route generation unit 48 updates and generates the automated travel route every time the vehicle 1 travels automatically by a predetermined distance or every time a predetermined period passes. For example, the image capturing device 102 captures more feature points F2 in the capture range as the vehicle 1 gets closer to the target position P0.”, wherein features are extracted throughout the entire parking process, thus comprising when the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control), and
register the acquired features as second features (FIG. 7 S18; para. [0059]: “the feature point selection unit 24 in the automated driving information registration unit 12 selects the feature point F2 from the candidate feature points F1 on the basis of the registration time images Pi1 (step S18). The feature point selection unit 24 selects the feature point F2 corresponding to the candidate feature point F1 that satisfies the relation in terms of the stereo photogrammetry among the candidate feature points F1, and calculates the temporary coordinate information thereof”).
Ilo is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and incorporate the teaching of Ilo and register features throughout the parking process. Doing so will enhance the accuracy of the vehicle position by using features which are visible throughout the parking process (Ilo, para. [0010]: “According to this automated driving control device, one feature point can be used as the mark for a longer time; therefore, the vehicle position in the automated driving can be calculated more accurately in an appropriate level”), but fails to specifically teach when the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control.
However, in the same field of endeavor, WANG teaches at the time when the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle (FIG. 1E; para. [0018]: “each of the measurements can be recorded as a trail of waypoints 120. In some examples, the frequency of recording can be based on the degree of change in vehicle dynamics (e.g., if the vehicle is turning, accelerating, decelerating, etc., the frequency of recording can be greater than if the vehicle is moving in a straight line at a fixed speed).”, wherein FIG. 1E shows waypoints and vehicle moving straight until completion of parking).
WANG is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and further in view of Ilo and incorporate the teaching of WANG and register features throughout the parking process, which comprise of waypoint of moving straight until completing parking the vehicle. Doing so will enhance the accuracy of the vehicle position by storing more features throughout the parking process
Regarding claim 5, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1, but fails to specifically teach acquire the features on the parking lot from the camera images when the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control; and register the acquired features as third features.
However, in the same field of endeavor, Ilo teaches electronic control unit (Fig. 2 control unit) is further configured to: acquire the features on the parking lot from the camera images when the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control after the electronic control unit starts to move the vehicle by the parking assist control (FIG. 3; FIG. 7 S12-S16; para. [0058]: “the vehicle 1 to move from the position P1 (here, target position P0) with the driver's own operation. In this operation, the image capturing device 102 sequentially captures the registration time images Pi1. As shown in FIG. 7, upon detection of the registration mode (setting of registration mode) by the automated driving information registration unit 12 (step S10), the image memory 20 obtains the registration time image Pi1 from the image capturing device 102 and stores the image therein (step S12). In the automated driving information registration unit 12, the candidate feature point extraction unit 22 reads out the registration time image Pi1 from the image memory 20, and extracts the candidate feature points F1 (step S14). The candidate feature point extraction unit 22 extracts as the candidate feature points F1, all the positions that can be distinguished from the surroundings among all the objects that exist around the target position P0 (for example, that have contrast with the surroundings over a predetermined value). After the candidate feature points F1 are extracted, the automated driving information registration unit 12 detects whether the registration mode is stopped (step S16), and if the registration mode is not stopped (No at step S16), the process returns to step S12, the next registration time image Pi1 is obtained, and the candidate feature point F1 in that registration time image Pi1 is extracted”; para. [0070]: “Note that the automated travel route generation unit 48 updates and generates the automated travel route every time the vehicle 1 travels automatically by a predetermined distance or every time a predetermined period passes. For example, the image capturing device 102 captures more feature points F2 in the capture range as the vehicle 1 gets closer to the target position P0.”, wherein Ilo teaches capturing features throughout the entire parking process, thus comprising of when the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle); and
register the acquired features as third features (FIG. 7 S18; para. [0059]: “the feature point selection unit 24 in the automated driving information registration unit 12 selects the feature point F2 from the candidate feature points F1 on the basis of the registration time images Pi1 (step S18). The feature point selection unit 24 selects the feature point F2 corresponding to the candidate feature point F1 that satisfies the relation in terms of the stereo photogrammetry among the candidate feature points F1, and calculates the temporary coordinate information thereof”).
Ilo is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and incorporate the teaching of Ilo and register features throughout the parking process. Doing so will enhance the accuracy of the vehicle position by using features which are visible throughout the parking process (Ilo, para. [0010]: “According to this automated driving control device, one feature point can be used as the mark for a longer time; therefore, the vehicle position in the automated driving can be calculated more accurately in an appropriate level”), but fails to specifically teach when the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot.
However, in the same field of endeavor, WANG teaches when the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle (FIG. 1E; para. [0018]: “each of the measurements can be recorded as a trail of waypoints 120. In some examples, the frequency of recording can be based on the degree of change in vehicle dynamics (e.g., if the vehicle is turning, accelerating, decelerating, etc., the frequency of recording can be greater than if the vehicle is moving in a straight line at a fixed speed).”, wherein FIG. 1E shows waypoints and vehicle moving straight until completion of parking).
WANG is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and further in view of Ilo and incorporate the teaching of WANG and register features throughout the parking process, which comprise of waypoint at a predetermined distance after moving straight until completing parking the vehicle. Doing so will enhance the accuracy of the vehicle position by storing more features throughout the parking process
Regarding claim 6, GAO in view of Hyundai teaches the vehicle parking assist apparatus as set forth in claim 1, but fails to specifically teach after the electronic control unit starts to move the vehicle by the parking assist control, acquire the features on the parking lot from the camera images each time the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control; and register the acquired features as third features.
However, Ilo teaches wherein: the electronic control unit (Fig. 2 control unit) is further configured to: after the electronic control unit starts to move the vehicle by the parking assist control, acquire the features on the parking lot from the camera images each time the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot by the parking assist control (FIG. 3; FIG. 7 S12-S16; para. [0058]: “the vehicle 1 to move from the position P1 (here, target position P0) with the driver's own operation. In this operation, the image capturing device 102 sequentially captures the registration time images Pi1. As shown in FIG. 7, upon detection of the registration mode (setting of registration mode) by the automated driving information registration unit 12 (step S10), the image memory 20 obtains the registration time image Pi1 from the image capturing device 102 and stores the image therein (step S12). In the automated driving information registration unit 12, the candidate feature point extraction unit 22 reads out the registration time image Pi1 from the image memory 20, and extracts the candidate feature points F1 (step S14). The candidate feature point extraction unit 22 extracts as the candidate feature points F1, all the positions that can be distinguished from the surroundings among all the objects that exist around the target position P0 (for example, that have contrast with the surroundings over a predetermined value). After the candidate feature points F1 are extracted, the automated driving information registration unit 12 detects whether the registration mode is stopped (step S16), and if the registration mode is not stopped (No at step S16), the process returns to step S12, the next registration time image Pi1 is obtained, and the candidate feature point F1 in that registration time image Pi1 is extracted”; para. [0070]: “Note that the automated travel route generation unit 48 updates and generates the automated travel route every time the vehicle 1 travels automatically by a predetermined distance or every time a predetermined period passes. For example, the image capturing device 102 captures more feature points F2 in the capture range as the vehicle 1 gets closer to the target position P0.” wherein Ilo teaches capturing features throughout the entire parking process, thus comprising of after the electronic control unit starts to move the vehicle by the parking assist control, acquire the features on the parking lot from the camera images each time the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot); and
register the acquired features as third features (FIG. 7 S18; para. [0059]: “the feature point selection unit 24 in the automated driving information registration unit 12 selects the feature point F2 from the candidate feature points F1 on the basis of the registration time images Pi1 (step S18). The feature point selection unit 24 selects the feature point F2 corresponding to the candidate feature point F1 that satisfies the relation in terms of the stereo photogrammetry among the candidate feature points F1, and calculates the temporary coordinate information thereof”).
Ilo is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and incorporate the teaching of Ilo and register features throughout the parking process. Doing so will enhance the accuracy of the vehicle position by using features which are visible throughout the parking process (Ilo, para. [0010]: “According to this automated driving control device, one feature point can be used as the mark for a longer time; therefore, the vehicle position in the automated driving can be calculated more accurately in an appropriate level”), but fails to specifically teach each time the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot.
However, in the same field of endeavor, WANG teaches each time the vehicle moves a predetermined distance after the electronic control unit predicts that the vehicle moves straight until the electronic control unit completes parking the vehicle in the parking lot (FIG. 1E; para. [0018]: “each of the measurements can be recorded as a trail of waypoints 120. In some examples, the frequency of recording can be based on the degree of change in vehicle dynamics (e.g., if the vehicle is turning, accelerating, decelerating, etc., the frequency of recording can be greater than if the vehicle is moving in a straight line at a fixed speed).”, wherein FIG. 1E shows waypoints and vehicle moving straight until completion of parking).
WANG is considered to be analogous to the claimed invention because they are in the same field of autonomous parking. 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 of Gao in view of Hyundai and further in view of Ilo and incorporate the teaching of WANG and register features throughout the parking process, which comprise of waypoint at each time the vehicle moves a predetermined distance after moving straight until completing parking the vehicle. Doing so will enhance the accuracy of the vehicle position by storing more features throughout the parking process
Allowable Subject Matter
Claim 9 is allowed.
The following is an examiner’s statement of reasons for allowance.
Regarding claim 9, closest prior art, GAO (US 20180157267 A1), taken either individually or in combination with other prior art of record fails to teach the claimed invention as a whole.
GAO teaches a vehicle learning a desired home parking location to be used in future parking maneuvers, wherein features are acquired in a parked state (FIG. 4 420; para. [0025]: “At step 402 the algorithm includes determining whether the vehicle is in a parked state.”; FIG. 2-3; para. [0028]: “At step 408 the algorithm includes capturing a reference image from one or more image capture devices which are positioned to generate images indicative of the vicinity of the vehicle. The one or more images is captured and used by the vehicle processor to associate the images with the vehicle home position. Each of the images are captured as a set of reference images. In some examples, distance information is also gathered with respect to the proximity of nearby objects or markers placed by a user”; para. [0045]: “other scenarios such as docking a vehicle at an outdoor parking space, painted ground markers, signs, and parking meters, for example, may all be visually detected as stationary items and similarly used for vehicle positioning”), and allows autonomous parking to the learned parked state based on the features acquired (para. [0032]: “the current vehicle position is compared against the home vehicle position to determine a required movement to converge the current vehicle position with the home vehicle position. Such a comparison may include comparing the location data to acquire rough positioning guidance based on the macro level location information. The comparison also includes comparing the image data representing the vicinity of the vehicle for more granular positioning guidance to enable a precise return to a previously-learned vehicle position”; para. [0033]: “informing a driver about the current vehicle position status relative to the target home vehicle position”; para. [0054]: “Visible patterns from the images are used to assess the accuracy of the vehicle parked position relative features from the external patterns”; para. [0003]: “controller is further programmed to capture a current image corresponding to a current vehicle position in response to a subsequent approach toward the vicinity, and compare the current image to the reference image”), but fails to specifically teach the vehicle completes parking in the parking lot by a parking assist control prior to registration, wherein the parking assist control is a control to autonomously park the vehicle without any operations applied to an accelerator pedal, a brake pedal and a steering wheel by a driver.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bonnet (US20150088360A1) teaches performing the parking procedure autonomously from the start position using the stored data, wherein stored data is stored before beginning the autonomous parking procedure of the motor vehicle the target position and/or last driven trajectory of the motor vehicle.
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/ANDREW SANG KIM/Examiner, Art Unit 3668
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