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 .
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.
Claim Status
This action is in response to application filed on November 4, 2024. Claims 1-20 are pending for examination.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-4, 6, 8-9, 12 and 14 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4, 8-11 and 16 of U.S. Patent No. 12,202,432 (reference application) respectively. Claims 1-4, 6, 8-9 and 12-14 are generally broader than the respective claims 1-4, 8-11 and 16 in U.S. Patent No. 12,202,432. Broader claims in a later application constitute obvious double patenting of narrow claims in an issued patent. See In re Van Ornum and Stang, 214, USPQ 761, 766, and 767 (CCPA) (the court sustained an obvious double patenting rejection of generic claims in a continuation application over narrower species claims in an issued patent); In re Vogel, 164 USPQ 619, 622, and 623 (CCPA 1970) (generic application claim specifying "meat" is obvious double patenting of narrow patent claim specifying "pork").
Reference application claim 1 corresponds to instant claim 1,
reference application claim 2 corresponds to instant claim 2,
reference application claim 3 corresponds to instant claim 3,
reference application claim 4 corresponds to instant claim 4,
reference application claim 10 corresponds to instant claim 6,
reference application claim 8 corresponds to instant claim 8,
reference application claim 9 corresponds to instant claim 9,
reference application claim 11 corresponds to instant claim 12,
reference application claim 16 corresponds to instant claim 14.
Claims 5, 7, 10-11, 13 and 15-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 14 of U.S. Patent No. 12,202,432 (reference application) in view of Szawarski et al. (Szawarski: US 20190225186 A1).
Regarding claims 5, 7, 10-11, 13 and 15-20, claim 14 of U.S. Patent No. 12,202,432 does not explicitly disclose the claimed limitation. However, Szawarski teaches those limitation as set forth in the prior art rejections below. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Szawarski in order to omit a sensor in a buckle of the seatbelt (Szawarski: Par 11).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 7-20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Szawarski et al. (Szawarski: US 20190225186 A1).
Regarding Claim 1, Szawarski teaches a method (Fig. 5), comprising:
performing a classification of an area of an image based at least in part on a group of pixels depicting at least one edge of a safety restraint device (Par 47, in a decision block 525, the computer 32 detects whether the seatbelt 50 is present or absent from a first region 76 of the image 74. The computer 32 may perform a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold. The memory of the computer 32 may store shapes representing the objects to the detected, e.g., the shoulder band 62, along with corresponding sizes, measured in pixels or in physical distance, e.g., inches. For example, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape and scored according to how closely the detected edges match the stored shape. If the score of a subset of detected edges is above a detection threshold, then the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62),
wherein the image depicts at least a portion of an occupant of a vehicle, and the safety restraint device corresponds to the occupant (Fig. 7, image 74 includes occupant and shoulder band 62and Par 45); and,
controlling, using one or more circuits, functionality of a subsystem of the vehicle based at least in part on the classification (Par [0048] If an absence of the seatbelt 50 is detected in the first region 76 (as shown in FIGS. 4B and 4C), next, in a block 530, the computer 32 actuates an output device to deliver an output including a limitation of vehicle speed).
Regarding Claim 2, Szawarski teaches the method of claim 1, but does not explicitly disclose herein performing the classification comprises using a plurality of neighboring pixels along a specified direction to determine a set of pixels that are part of the safety restraint device (Szawarski: Par 47, a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold … angle of 45’ and Par 45, The grayscale gradient may be a difference in brightness between two pixels divided by the distance between the pixels. The computer 32 may determine a location for an edge, e.g., wherever the grayscale gradient is above an edge threshold. Like the grayscale gradient, the edge threshold may be measured in brightness per pixel distance ).
Regarding Claim 3, Szawarski teaches the method of claim 2, further comprising determining the set of pixels that are part of the safety restraint device based, at least in part, on one or more intensity levels of the pixel and the plurality of neighboring pixels along the specified direction (Szawarski: Par 47, a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold and Par 45, The grayscale gradient may be a difference in brightness between two pixels divided by the distance between the pixels. The computer 32 may determine a location for an edge, e.g., wherever the grayscale gradient is above an edge threshold. Like the grayscale gradient, the edge threshold may be measured in brightness per pixel distance.).
Regarding Claim 4, Szawarski teaches the method of claim 1, further comprising generating a shape representing the safety restraint device based at least in part on the classification (Szawarski: Par 47, the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62).
Regarding Claim 5, Szawarski teaches the method of claim 4, further comprising selecting a set of pixels in the classification to generate the shape based at least in part on one or more safety restraint devices (Szawarski: Par 46, the computer 32 may select a location in the image 74 for the first region 76 at a chest of the occupant … the computer 32 may horizontally align a center of the first region 76 with a center of the detected head, and the computer 32 may set a top edge of the first region 76 at a vertical offset down from a bottom point of the detected head. The vertical offset may be a scalar value measured in, e.g., pixels or inches and stored in the memory of the computer 32. The vertical offset may be chosen to be, e.g., an average vertical distance between a chin and a sternum and Par 47, stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape ).
Regarding Claim 7, Szawarski teaches the method of claim 1, wherein the group of pixels comprises at least two edges of the safety restraint device (Szawarski: par 47, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape).
Regarding Claim 8, Szawarski teaches a system, comprising:
one or more processors; memory that stores computer-executable instructions that are executable by the one or more processors to cause the system (Fig. 3, computer 32 and Par 38) to:
perform a classification of an area of an image based at least in part on a group of pixels depicting at least one edge of a safety restraint device (Par 47, in a decision block 525, the computer 32 detects whether the seatbelt 50 is present or absent from a first region 76 of the image 74. The computer 32 may perform a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold. The memory of the computer 32 may store shapes representing the objects to the detected, e.g., the shoulder band 62, along with corresponding sizes, measured in pixels or in physical distance, e.g., inches. For example, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape and scored according to how closely the detected edges match the stored shape. If the score of a subset of detected edges is above a detection threshold, then the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62), wherein the image depicts at least a portion of an occupant of a vehicle, and the safety restraint device corresponds to the occupant (Fig. 7, image 74 includes occupant and shoulder band 62and Par 45); and,
control, using one or more circuits, functionality of a subsystem of the vehicle based at least in part on the classification (Par [0048] If an absence of the seatbelt 50 is detected in the first region 76 (as shown in FIGS. 4B and 4C), next, in a block 530, the computer 32 actuates an output device to deliver an output including a limitation of vehicle speed).
Regarding Claim 9, Szawarski teaches the system of claim 8, wherein the computer-executable instructions that are executable by the one or more processors cause the system to generate a shape representing the safety restraint device based at least in part on the classification (Szawarski: Par 47, the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62).
Regarding Claim 10, Szawarski teaches the system of claim 9, wherein the computer-executable instructions that are executable by the one or more processors cause the system to use a set of pixels in the classification to generate the shape (Szawarski: Par 46, the computer 32 may select a location in the image 74 for the first region 76 at a chest of the occupant … the computer 32 may horizontally align a center of the first region 76 with a center of the detected head, and the computer 32 may set a top edge of the first region 76 at a vertical offset down from a bottom point of the detected head. The vertical offset may be a scalar value measured in, e.g., pixels or inches and stored in the memory of the computer 32. The vertical offset may be chosen to be, e.g., an average vertical distance between a chin and a sternum and Par 47, stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape ).
Regarding Claim 11, Szawarski teaches the system of claim 9, wherein the computer-executable instructions that are executable by the one or more processors cause the system to use the shape to determine a position of the safety restraint device relative to the occupant (Szawarski: Fig. 5, and Par 52, The second actuation of the user interface 70 may communicate a message to the occupant that the seatbelt 50 is buckled improperly or that the occupant should re-buckle the seatbelt 50 with the shoulder band 62 in front of the torso of the occupant).
Regarding Claim 12, Szawarski teaches the system of claim 8, wherein the computer-executable instructions that are executable by the one or more processors cause the system to activate a signal to indicate that the safety restraint device is in an improper position relative to the occupant (Szawarski: Par 42, the computer 32 can provide a first output when the restraint system 48 is unbuckled and/or can provide a second output when the restraint system 48 is buckled improperly with the shoulder band 62 behind the occupant).
Regarding Claim 13, Szawarski teaches the system of claim 8, wherein the group of pixels comprises two edges of the safety restraint device in an autonomous vehicle (Szawarski: par 47, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape and Par 32, vehicle 30 may be an autonomous vehicle).
Regarding Claim 14, Szawarski teaches a vehicle (30), comprising:
a propulsion system (Par 32, propulsion system);
an image capturing device able to capture an image of a passenger of the vehicle (Fig. 1, camera 64 and Par 37 and Fig. 4A-4C); and
a computer system comprising instructions executable by the computer system (Fig. 3, computer 32 and Par 38) to at least:
perform a classification of an area of an image based at least in part on a group of pixels depicting at least one edge of a safety restraint device (Par 47, in a decision block 525, the computer 32 detects whether the seatbelt 50 is present or absent from a first region 76 of the image 74. The computer 32 may perform a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold. The memory of the computer 32 may store shapes representing the objects to the detected, e.g., the shoulder band 62, along with corresponding sizes, measured in pixels or in physical distance, e.g., inches. For example, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape and scored according to how closely the detected edges match the stored shape. If the score of a subset of detected edges is above a detection threshold, then the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62),
wherein the image depicts at least a portion of an occupant of a vehicle, and the safety restraint device corresponds to the occupant (Fig. 7, image 74 includes occupant and shoulder band 62and Par 45); and,
control, using one or more circuits, functionality of a subsystem of the vehicle based at least in part on the classification (Par [0048] If an absence of the seatbelt 50 is detected in the first region 76 (as shown in FIGS. 4B and 4C), next, in a block 530, the computer 32 actuates an output device to deliver an output including a limitation of vehicle speed.).
Regarding Claim 15, Szawarski teaches the vehicle of claim 14, wherein the group of pixels comprises two edges of the safety restraint device (Szawarski: par 47, the stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape).
Regarding Claim 16, Szawarski teaches the vehicle of claim 14, wherein the instructions, when executed, further cause the computer system to generate a shape representing the safety restraint device (Szawarski: Par 47, the subset of detected edges is classified as an instance of the stored shape, e.g., as an instance of the shoulder band 62).
Regarding Claim 17, Szawarski teaches the vehicle of claim 16, wherein the instructions, when executed, further cause the computer system to select a set of pixels in the classification to generate the shape (Szawarski: Par 46, the computer 32 may select a location in the image 74 for the first region 76 at a chest of the occupant … the computer 32 may horizontally align a center of the first region 76 with a center of the detected head, and the computer 32 may set a top edge of the first region 76 at a vertical offset down from a bottom point of the detected head. The vertical offset may be a scalar value measured in, e.g., pixels or inches and stored in the memory of the computer 32. The vertical offset may be chosen to be, e.g., an average vertical distance between a chin and a sternum and Par 47, stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape ).
Regarding Claim 18, Szawarski teaches the vehicle of claim 17, wherein the set of pixels are selected based on a range of one or more widths associated with the safety restraint device (Szawarski: Par 47, computer 32 may store shapes representing the objects to the detected, e.g., the shoulder band 62, along with corresponding sizes, measured in pixels … stored shape of the shoulder band 62 may be two parallel lines 3 inches apart at an angle of 45°. Subsets of the detected edges may be compared with the stored shape).
Regarding Claim 19, Szawarski teaches the vehicle of claim 14, wherein the instructions, when executed, further cause the computer system to represent the group of pixels comprising the at least one edge of the safety restraint device are as a curve comprising one or more intensity levels of the pixels in the group (Szawarski: Par 47, a conventional object-recognition algorithm on the first region 76 of the image 74 to recognize an object, e.g., the shoulder band 62. For example, the computer 32 may apply edge detection using grayscale gradients to the image 74. The computer 32 may determine a location for an edge within the first region 76, e.g., wherever the grayscale gradient is above the edge threshold … shape of the shoulder band 62 may be two parallel lines 3 inches apart i.e. a line is a special curve with zero curvature. and Par 45, The grayscale gradient may be a difference in brightness between two pixels divided by the distance between the pixels. The computer 32 may determine a location for an edge, e.g., wherever the grayscale gradient is above an edge threshold. Like the grayscale gradient, the edge threshold may be measured in brightness per pixel distance).
Regarding Claim 20, Szawarski teaches the vehicle of claim 19, wherein the curve comprises at least one peak of the one or more intensity levels that corresponds to the at least one edge of the safety restraint device (Szawarski: Par 45, The grayscale gradient may be a difference in brightness between two pixels divided by the distance between the pixels. i.e. the higher brightness is the peak of the two).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Szawarski in view of Baranowski et al. (US 20140270551 A1).
Regarding Claim 6, Szawarski teaches the method of claim 1, but does not explicitly disclose wherein performing the classification further comprises processing two or more groups of pixels in parallel on a graphics processing unit (GPU) to classify the safety restraint device in the image.
However, the preceding limitation is known in the art of detecting object in an image. Baranowski teaches a graphics processing unit (GPU) is configured to detect an object in an image (abstract) and further teaches processing two or more groups of pixels in parallel on a graphics processing unit (GPU) to classify the safety restraint device in the image (Par 11, parallel processing units may apply the random forest classifier to multiple pixels concurrently--performing equivalent mathematical operations on each pixel.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Baranowski in order to efficiently perform the same instruction on multiple pixels (Baranowski: Par 11).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Prior arts cited for the record but not used in Office Action, are listed in attached PTO-892.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nay Tun whose telephone number is (571)270-7939. The examiner can normally be reached on Mon-Thurs from 9:00-5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner's Supervisor, Steven Lim can be reached on (571) 270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/Nay Tun/Primary Examiner, Art Unit 2688
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