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 .
Response to Amendment
The amendment filed on 06/09/2026 has been entered. Claims 1-6 remain pending in this application. Claims 1, 5, and 6 have been amended. No claims have been cancelled or are new.
Response to Arguments
Applicant’s arguments filed 06/09/2026 regarding prior art rejections have been fully considered and are persuasive. All previous prior art rejections are overcome in consideration of amendments. However, additional prior art rejections are presented below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Trenkle (US 20230184930 A1), hereinafter Trenkle, in view of Teo (US 20230303113 A1), hereinafter Teo, in further view of Fletcher (US 20150210293 A1), hereinafter Fletcher.
Regarding claim 1, Trenkle, as shown below, discloses an object detection device comprising the following limitations:
an acquisition part that acquires a plurality of results of reception of a reflected wave generated when a probing wave transmitted from a sensor installed in a door of a vehicle is reflected by an object around the vehicle (See at least Fig. 3, [0055] “The vehicle door system designated by reference numeral 21 may include a door 22 and/or a cover 28, a radar sensor 23 and an evaluation device 24”, [0043]-[0044] “[0043] In a first step S1, the radar sensor or its transmitting antenna emits radar radiation, for example a pulsed HF signal, in the direction of an object to be detected. […] [0044] In a subsequent step S2, echo signals are picked up by the radar sensor or its receiving antenna, and are evaluated accordingly. The echo signals are generated by reflection of the radar signal on a surface of the object.”); and,
an estimation part that determines whether the object is an obstacle on a basis of the feature vector calculated by the second calculation part and the object detection model, and outputs a determination result. (See at least [0021] “the determined feature vector is compared with a first feature vector and a second feature vector in order to classify the object. The first feature vector represents an ideal pole, such as a point-like obstacle, wherein the second feature vector represents an ideal wall. Depending on the comparison, the object is classified as a wall or a pole” See also [0018]-[0023])
Trenkle does not explicitly disclose in a learning phase, a model generation part that (i) calculates a detection point cloud as a position of the object on a basis of the plurality of results of reception acquired by the acquisition part, (ii) generates an object detection model by performing machine learning of a relationship between a feature vector indicating a distribution shape of the detection point cloud and information indicating whether the object is an obstacle; in an estimation phase, a first calculation part that calculates a detection point cloud as a position of the object, on a basis of the plurality of results of reception acquired by the acquisition part; a second calculation part that calculates a feature vector indicating a distribution shape of the detection point cloud, on a basis of the detection point cloud calculated by the first calculation part. However, Teo, in the same or in a similar field of endeavor, discloses:
in a learning phase, a model generation part that (i) calculates a detection point cloud as a position of the object on a basis of the plurality of results of reception acquired by the acquisition part, (ii) generates an object detection model by performing machine learning of a relationship between a feature vector indicating a distribution shape of the detection point cloud and information indicating whether the object is an obstacle (See at least [0025] “radar to detect the markings by radar signature and extract lane or curb features from radar data associated with a radar image (e.g., extract shape, position, and/or orientation of lane and/or curb markings from radar points within a 3D radar map”, [0106] “the portion of the radar image (or the entire radar image) may be formatted as a feature vector to be input to the neural network” Teo discloses applying to a neural network (subset of machine learning) a 3D radar map/point cloud with feature vectors of at least shape and position of curbs (wall like obstacles). See also [0105]-[0106]);
in an estimation phase, a first calculation part that calculates a detection point cloud as a position of the object, on a basis of the plurality of results of reception acquired by the acquisition part (See at least [0098] The vehicle 502 (e.g., radar 502A or perception system 402) may determine positions of markings 504 based on certain ranges and azimuth angles of radar points of the returned radio waves […] the data associated with detected lane markings may be stored as a 3D point cloud”, [0125] “the localization system 406 can determine the shape, position, and orientation of the lane and/or curb using the radar points of the radar image.”);
a second calculation part that calculates a feature vector indicating a distribution shape of the detection point cloud, on a basis of the detection point cloud calculated by the first calculation part (See at least [0097] “The second condition may be a shape condition and/or a density condition. The shape condition may be satisfied if positions (indicated by ranges and azimuth angles) of radar points of returned radio waves match one or more defined shapes. The density condition may be satisfied if the positions of the radar points of the returned radio waves are close enough to each other to have a number of positions per unit volume greater than a minimum number of positions per unit volume.”, [0106] “the portion of the radar image (or the entire radar image) may be formatted as a feature vector to be input to the neural network, and the neural network may be trained to detect 3D shapes based on radar points of the 3D radar point cloud”, [0129] “the localization system 406 can determine a shape and/or size (length, width/depth, height, etc.) of a lane and/or a curb based on the distance between various points in a radar point cloud”); and
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the object detection system disclosed by Trenkle with the generation and calculation system disclosed by Teo. One would have been motivated to do so in order to advantageously utilize radar to overcome limitations and ensure safer operational actions (See at least [0027] “systems of the present disclosure may overcome certain limitations of optical sensors of cameras or lidar and provide operational data to ensure safer operational actions”).
The combination of Trenkle and Teo does not explicitly disclose (iii) classifies the object as an obstacle or a non- obstacle based on the feature vector. However, Fletcher, in the same or in a similar field of endeavor, discloses:
(iii) classifies the object as an obstacle or a non- obstacle based on the feature vector (See at least [0005] “The system utilizes a comparison between the current feature vector and previously stored feature vectors to match the current environment with a previously observed environment, and then loads a previously stored object configuration that can improve collision detection performance by modifying a probability that an object is or is not an obstacle within the vehicle path”, [0018] “[0018] When the object configuration 25 is sent to the reverse collision avoidance system 12, the classifications of objects 18 from the object configuration 25 can be used to enhance the classification of objects 18 observed by the camera 14 and sensors 20.”);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the object detection system disclosed by Trenkle with the generation and calculation system disclosed by Teo with the classification system disclosed by Fletcher. One would have been motivated to do so in order to advantageously improve collision detection performance (See at least [0005] “improve collision detection performance by modifying a probability that an object is or is not an obstacle within the vehicle path. The improved identification of commonly encountered objects reduces intrusion to vehicle occupants by avoiding unnecessary braking interventions from the collision avoidance system”).
Regarding claim 2, the combination of Trenkle, Teo, and Fletcher as shown in the rejection above, discloses all of the limitations of claim 1. Trenkle further discloses
(See at least Fig. 3, [0055] “The vehicle door system designated by reference numeral 21 may include a door 22 and/or a cover 28, a radar sensor 23 and an evaluation device 24”),
Trenkle does not disclose the model generation part performs coordinate transform of the detection point cloud into three-dimensional coordinates cloud in the three- dimensional coordinates, and calculates a feature vector indicating a distribution shape of the detection point cloud in the set region of interest. However, Teo further discloses
the model generation part performs coordinate transform of the detection point cloud into three-dimensional coordinates (See at least [0125] “the localization system 406 can determine the proximity of the lane and/or curb to the vehicle based on the radar points in the radar image. For example, the radar points may have a 3D coordinate assigned to it that represents the location of the radar point relative to the vehicle” See also [0127] regarding ‘transformation’)(See at least [0106] “the portion of the radar image (or the entire radar image) may be formatted as a feature vector to be input to the neural network, and the neural network may be trained to detect 3D shapes based on radar points of the 3D radar point cloud” Teo discloses a set region of interest as a detection point cloud), and
the second calculation part performs coordinate transform of the detection point cloud calculated by the first calculation part into the three-dimensional coordinates (See at least [0125] “the localization system 406 can determine the proximity of the lane and/or curb to the vehicle based on the radar points in the radar image. For example, the radar points may have a 3D coordinate assigned to it that represents the location of the radar point relative to the vehicle” See also [0127] regarding ‘transformation’), sets at least one region of interest on a basis of the detection point cloud in the three- dimensional coordinates, and calculates a feature vector indicating a distribution shape of the detection point cloud in the set region of interest (See at least [0106] “the portion of the radar image (or the entire radar image) may be formatted as a feature vector to be input to the neural network, and the neural network may be trained to detect 3D shapes based on radar points of the 3D radar point cloud” Teo discloses a set region of interest as a detection point cloud).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the object detection system disclosed by Trenkle with the classification system disclosed by Fletcher with the generation and calculation system disclosed by Teo. One would have been motivated to do so in order to advantageously utilize radar to overcome limitations and ensure safer operational actions (See at least [0027] “systems of the present disclosure may overcome certain limitations of optical sensors of cameras or lidar and provide operational data to ensure safer operational actions”).
Regarding claim 3, the combination of Trenkle, Teo, and Fletcher as shown in the rejection above, discloses all of the limitations of claim 1. Trenkle further discloses
the door is a swing door (See at least Fig. 3, [0030] “the sensor is a component of a vehicle door.”),
the object detection device further includes a control part that controls a driver unit that causes the door to perform an opening or closing operation (See at least [0030] “During an opening process of the vehicle door, a detection of the surroundings is carried out, wherein a warning signal is emitted if an imminent collision of the vehicle door with the object is detected on the basis of the surroundings information detected by the sensor. […] by means of a corresponding motorized drive for automated opening”), and,
when the estimation part outputs information indicating that the object is an obstacle, the control part sets an opening movable angle of the door on a basis of positional information about the obstacle, and controls the drive unit to cause the door to perform an opening operation to the set opening movable angle (See at least [0030] “In the event of a detected impending collision, further collision-avoiding measures may also be initiated, such as blocking the opening process, for example by means of a corresponding motorized drive for automated opening.” Trenkle discloses limiting the opening to a set angle which corresponds to the door remaining closed.).
Regarding claim 5, applicant recites limitations of the same or substantially the same scope as claim 1. Accordingly, claim 5 is rejected in the same or substantially the same manner as claim 1, shown above.
Regarding claim 6, applicant recites limitations of the same or substantially the same scope as claims 1 and 3. Accordingly, claim 6 is rejected in the same or substantially the same manner as claims 1 and 3, shown above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Trenkle, in view of Teo, in further view of Hioki (JP 2010236196 A), hereinafter Hioki.
Regarding claim 4, The combination of Trenkle, Teo, and Fletcher, as shown above, discloses all the limitations of claims 1 and 3. The combination of Trenkle, Teo, and Fletcher does not explicitly disclose the control part controls the drive unit to cause the door to perform an opening operation to the set opening movable angle, on a basis of a request for an automatic opening operation of the door from a user of the vehicle. However, Hioki, in the same or in a similar field of endeavor, discloses
the control part controls the drive unit to cause the door to perform an opening operation to the set opening movable angle, on a basis of a request for an automatic opening operation of the door from a user of the vehicle (See at least “As shown in FIG. 1, the vehicle door opening degree control device mainly opens the vehicle door including an ECU 1 that executes various control processes, various switches 6 to 8 and […] That is, in the first embodiment, the vehicle door is automatically opened and closed using the two types of motors 12 and 13 by the user's switch operation”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the object detection system disclosed by Trenkle with the generation and calculation system disclosed by Teo with the classification system disclosed by Fletcher with the automatic opening system disclosed by Hioki. One would have been motivated to do so in order to advantageously prevent damage, thereby improving user convenience. (See at least “As a result, the vehicle door 30 can be opened as much as possible within a range in which the vehicle door 30 does not come into contact with an obstacle, so that the convenience of the vehicle occupant can be improved”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH W GOOD whose telephone number is (571)272-4186. The examiner can normally be reached Mon - Thu 7:30 am - 5:00 pm.
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, Resha H Desai can be reached at (571) 270-7792. 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.
/KENNETH W GOOD/Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
Prosecution Insights